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Interesting resources related to XAI (Explainable Artificial Intelligence)

Papers

2019

  • iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models; Alicja Gosiewska, Przemyslaw Biecek; Explainable Artificial Intelligence (XAI) brings a lot of attention recently. Explainability is being presented as a remedy for lack of trust in model predictions. Model agnostic tools such as LIME, SHAP, or Break Down promise instance level interpretability for any complex machine learning model. But how certain are these explanations? Can we rely on additive explanations for non-additive models? In this paper, we examine the behavior of model explainers under the presence of interactions. We define two sources of uncertainty, model level uncertainty, and explanation level uncertainty. We show that adding interactions reduces explanation level uncertainty. We introduce a new method iBreakDown that generates non-additive explanations with local interaction.

images/ibreakdown.png

  • VINE: Visualizing Statistical Interactions in Black Box Models; Matthew Britton; As machine learning becomes more pervasive, there is an urgent need for interpretable explanations of predictive models. Prior work has developed effective methods for visualizing global model behavior, as well as generating local (instance-specific) explanations. However, relatively little work has addressed regional explanations - how groups of similar instances behave in a complex model, and the related issue of visualizing statistical feature interactions. The lack of utilities available for these analytical needs hinders the development of models that are mission-critical, transparent, and align with social goals. We present VINE (Visual INteraction Effects), a novel algorithm to extract and visualize statistical interaction effects in black box models. We also present a novel evaluation metric for visualizations in the interpretable ML space.

images/vine.png

  • Clinical applications of machine learning algorithms: beyond the black box; David Watson et al; Machine learning algorithms may radically improve our ability to diagnose and treat disease; For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models; Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers
  • ICIE 1.0: A Novel Tool for InteractiveContextual Interaction Explanations; Simon B. van der Zon et al; With the rise of new laws around privacy and awareness,explanation of automated decision making becomes increasingly impor-tant. Nowadays, machine learning models are used to aid experts indomains such as banking and insurance to find suspicious transactions,approve loans and credit card applications. Companies using such sys-tems have to be able to provide the rationale behind their decisions;blindly relying on the trained model is not sufficient. There are currentlya number of methods that provide insights in models and their decisions,but often they are either good at showing global or local behavior. Globalbehavior is often too complex to visualize or comprehend, so approxima-tions are shown, and visualizing local behavior is often misleading as itis difficult to define what local exactly means (i.e. our methods don’t“know” how easily a feature-value can be changed; which ones are flexi-ble, and which ones are static). We introduce theICIEframework (Inter-active Contextual Interaction Explanations) which enables users to viewexplanations of individual instances under differentcontexts.Wewillseethat various contexts for the same case lead to different explanations,revealing different feature interaction

images/ICIE_2019.png

  • Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI; Shane T. Mueller, Robert R. Hoffman, William Clancey, Abigail Emrey, Gary Klein; This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted.
  • Explaining Explanations: An Overview of Interpretability of Machine Learning; Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, Lalana Kagal; There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we provide our definition of explainability and show how it can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient.
  • SAFE ML: Surrogate Assisted Feature Extraction for Model Learning; Alicja Gosiewska, Aleksandra Gacek, Piotr Lubon, Przemyslaw Biecek; Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, interpretable models require more work related to feature engineering, which is very time consuming. Can we train interpretable and accurate models, without timeless feature engineering? In this article, we show a method that uses elastic black-boxes as surrogate models to create a simpler, less opaque, yet still accurate and interpretable glass-box models. New models are created on newly engineered features extracted/learned with the help of a surrogate model. We show applications of this method for model level explanations and possible extensions for instance level explanations. We also present an example implementation in Python and benchmark this method on a number of tabular data sets.
  • Attention is not Explanation; Sarthak Jain, Byron C. Wallace; Attention mechanisms have seen wide adoption in neural NLP models. In addition to improving predictive performance, these are often touted as affording transparency: models equipped with attention provide a distribution over attended-to input units, and this is often presented (at least implicitly) as communicating the relative importance of inputs. However, it is unclear what relationship exists between attention weights and model outputs. In this work, we perform extensive experiments across a variety of NLP tasks that aim to assess the degree to which attention weights provide meaningful explanations for predictions. We find that they largely do not. For example, learned attention weights are frequently uncorrelated with gradient-based measures of feature importance, and one can identify very different attention distributions that nonetheless yield equivalent predictions. Our findings show that standard attention modules do not provide meaningful explanations and should not be treated as though they do.
  • Efficient Search for Diverse Coherent Explanations; Chris Russell; This paper proposes new search algorithms for counterfactual explanations based upon mixed integer programming. We are concerned with complex data in which variables may take any value from a contiguous range or an additional set of discrete states. We propose a novel set of constraints that we refer to as a "mixed polytope" and show how this can be used with an integer programming solver to efficiently find coherent counterfactual explanations i.e. solutions that are guaranteed to map back onto the underlying data structure, while avoiding the need for brute-force enumeration. We also look at the problem of diverse explanations and show how these can be generated within our framework.
  • Seven Myths in Machine Learning Research; Oscar Chang, Hod Lipson; As deep learning becomes more and more ubiquitous in high stakes applications like medical imaging, it is important to be careful of how we interpret decisions made by neural networks. For example, while it would be nice to have a CNN identify a spot on an MRI image as a malignant cancer-causing tumor, these results should not be trusted if they are based on fragile interpretation methods
  • Towards Aggregating Weighted Feature Attributions; Umang Bhatt, Pradeep Ravikumar, Jose M. F. Moura; Current approaches for explaining machine learning models fall into two distinct classes: antecedent event influence and value attribution. The former leverages training instances to describe how much influence a training point exerts on a test point, while the latter attempts to attribute value to the features most pertinent to a given prediction. In this work, we discuss an algorithm, AVA: Aggregate Valuation of Antecedents, that fuses these two explanation classes to form a new approach to feature attribution that not only retrieves local explanations but also captures global patterns learned by a model.
  • An Evaluation of the Human-Interpretability of Explanation; Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, Finale Doshi-Velez; What kinds of explanation are truly human-interpretable remains poorly understood. This work advances our understanding of what makes explanations interpretable under three specific tasks that users may perform with machine learning systems: simulation of the response, verification of a suggested response, and determining whether the correctness of a suggested response changes under a change to the inputs. Through carefully controlled human-subject experiments, we identify regularizers that can be used to optimize for the interpretability of machine learning systems. Our results show that the type of complexity matters: cognitive chunks (newly defined concepts) affect performance more than variable repetitions, and these trends are consistent across tasks and domains. This suggests that there may exist some common design principles for explanation systems.
  • Interpretable machine learning: definitions, methods, and applications; W. James Murdocha, Chandan Singh, Karl Kumbiera, Reza Abbasi-As, and Bin Yu; Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related, and what common concepts can be used to evaluate them.
  • Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making; Sina Aghaei, Mohammad Javad Azizi, Phebe Vayanos; In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e.g., to make product recommendations, or to guide the production of entertainment). More recently, these algorithms are increasingly being used to assist socially sensitive decisionmaking (e.g., to decide who to admit into a degree program or to prioritize individuals for public housing). Yet, these automated tools may result in discriminative decision-making in the sense that they may treat individuals unfairly or unequally based on membership to a category or a minority, resulting in disparate treatment or disparate impact and violating both moral and ethical standards. This may happen when the training dataset is itself biased (e.g., if individuals belonging to a particular group have historically been discriminated upon). However, it may also happen when the training dataset is unbiased, if the errors made by the system affect individuals belonging to a category or minority differently (e.g., if misclassification rates for Blacks are higher than for Whites). In this paper, we unify the definitions of unfairness across classification and regression. We propose a versatile mixed-integer optimization framework for learning optimal and fair decision trees and variants thereof to prevent disparate treatment and/or disparate impact as appropriate. This translates to a flexible schema for designing fair and interpretable policies suitable for socially sensitive decision-making. We conduct extensive computational studies that show that our framework improves the state-of-the-art in the field (which typically relies on heuristics) to yield non-discriminative decisions at lower cost to overall accuracy.

2018

  • Stealing Hyperparameters in Machine Learning; Binghui Wang, Neil Zhenqiang Gong; Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentiality of the proprietary algorithms that the learner uses to learn them. In this work, we propose attacks on stealing the hyperparameters that are learned by a learner. We call our attacks hyperparameter stealing attacks. Our attacks are applicable to a variety of popular machine learning algorithms such as ridge regression, logistic regression, support vector machine, and neural network. We evaluate the effectiveness of our attacks both theoretically and empirically. For instance, we evaluate our attacks on Amazon Machine Learning. Our results demonstrate that our attacks can accurately steal hyperparameters. We also study countermeasures. Our results highlight the need for new defenses against our hyperparameter stealing attacks for certain machine learning algorithms.
  • Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation; Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose Distill-and-Compare, a model distillation and comparison approach to audit such models. To gain insight into black-box models, we treat them as teachers, training transparent student models to mimic the risk scores assigned by black-box models. We compare the student model trained with distillation to a second un-distilled transparent model trained on ground-truth outcomes, and use differences between the two models to gain insight into the black-box model. Our approach can be applied in a realistic setting, without probing the black-box model API. We demonstrate the approach on four public data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending Club. We also propose a statistical test to determine if a data set is missing key features used to train the black-box model. Our test finds that the ProPublica data is likely missing key feature(s) used in COMPAS.

images/distill_and_compare.png

  • DIVE: A Mixed-Initiative System Supporting Integrated DataExploration Workflows; Kevin Hu et al; Generating knowledge from data is an increasingly important ac-tivity. This process of data exploration consists of multiple tasks:data ingestion, visualization, statistical analysis, and storytelling.Though these tasks are complementary, analysts often execute themin separate tools. Moreover, these tools have steep learning curvesdue to their reliance on manual query specification. Here, we de-scribe the design and implementation of DIVE, a web-based systemthat integrates state-of-the-art data exploration features into a sin-gle tool. DIVE contributes a mixed-initiative interaction schemethat combines recommendation with point-and-click manual spec-ification, and a consistent visual language that unifies differentstages of the data exploration workflow. In a controlled user studywith 67 professional data scientists, we find that DIVE users weresignificantly more successful and faster than Excel users at com-pleting predefined data visualization and analysis tasks

images/DIVE_2018.png

  • Learning Explanatory Rules from Noisy Data; Richard Evans, Edward Grefenstette; Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised. As their size and expressivity increases, so too does the variance of the model, yielding a nearly ubiquitous overfitting problem. Although mitigated by a variety of model regularisation methods, the common cure is to seek large amounts of training data---which is not necessarily easily obtained---that sufficiently approximates the data distribution of the domain we wish to test on. In contrast, logic programming methods such as Inductive Logic Programming offer an extremely data-efficient process by which models can be trained to reason on symbolic domains. However, these methods are unable to deal with the variety of domains neural networks can be applied to: they are not robust to noise in or mislabelling of inputs, and perhaps more importantly, cannot be applied to non-symbolic domains where the data is ambiguous, such as operating on raw pixels. In this paper, we propose a Differentiable Inductive Logic framework, which can not only solve tasks which traditional ILP systems are suited for, but shows a robustness to noise and error in the training data which ILP cannot cope with.

  • Towards Interpretable R-CNN by Unfolding Latent Structures; Tianfu Wu, Xilai Li, Xi Song, Wei Sun, Liang Dong and Bo Li; This paper presents a method of learning qualitatively interpretable models in object detection using popular two-stage region-based ConvNet detection systems (i.e., R-CNN). R-CNN consists of a region proposal network and a RoI (Region-of-Interest) prediction network. By interpretable models, we focus on weaklysupervised extractive rationale generation, that is learning to unfold latent discriminative part configurations of object instances automatically and simultaneously in detection without using any supervision for part configurations. We utilize a top-down hierarchical and compositional grammar model embedded in a directed acyclic AND-OR Graph (AOG) to explore and unfold the space of latent part configurations of RoIs. We propose an AOGParsing operator to substitute the RoIPooling operator widely used in RCNN, so the proposed method is applicable to many stateof-the-art ConvNet based detection systems.

  • Fair lending needs explainable models for responsible recommendation; Jiahao Chen; The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.

  • ICIE 1.0: A Novel Tool for Interactive Contextual Interaction Explanations; Simon B. van der Zon; Wouter Duivesteijn; Werner van Ipenburg; Jan Veldsink; Mykola Pechenizkiy; With the rise of new laws around privacy and awareness, explanation of automated decision making becomes increasingly important. Nowadays, machine learning models are used to aid experts in domains such as banking and insurance to find suspicious transactions, approve loans and credit card applications. Companies using such systems have to be able to provide the rationale behind their decisions; blindly relying on the trained model is not sufficient. There are currently a number of methods that provide insights in models and their decisions, but often they are either good at showing global or local behavior. Global behavior is often too complex to visualize or comprehend, so approximations are shown, and visualizing local behavior is often misleading as it is difficult to define what local exactly means (i.e. our methods don’t “know” how easily a feature-value can be changed; which ones are flexible, and which ones are static). We introduce the ICIE framework (Interactive Contextual Interaction Explanations) which enables users to view explanations of individual instances under different contexts. We will see that various contexts for the same case lead to different explanations, revealing different feature interactions.

  • Delayed Impact of Fair Machine Learning; Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt; Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs.

  • The Challenge of Crafting Intelligible Intelligence; Daniel S. Weld, Gagan Bansal; Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. To trust their behavior, we must make AI intelligible, either by using inherently interpretable models or by developing new methods for explaining and controlling otherwise overwhelmingly complex decisions using local approximation, vocabulary alignment, and interactive explanation. This paper argues that intelligibility is essential, surveys recent work on building such systems, and highlights key directions for research.

  • An Interpretable Model with Globally Consistent Explanations for Credit Risk; Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang; We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment. Rather than present a black box model and explain it afterwards, we provide a globally interpretable model that is as accurate as other neural networks. Our "two-layer additive risk model" is decomposable into subscales, where each node in the second layer represents a meaningful subscale, and all of the nonlinearities are transparent. We provide three types of explanations that are simpler than, but consistent with, the global model. One of these explanation methods involves solving a minimum set cover problem to find high-support globally-consistent explanations. We present a new online visualization tool to allow users to explore the global model and its explanations.

  • HELOC Applicant Risk Performance Evaluation by Topological Hierarchical Decomposition; Kyle Brown, Derek Doran, Ryan Kramer, Brad Reynolds; Strong regulations in the financial industry mean that any decisions based on machine learning need to be explained. This precludes the use of powerful supervised techniques such as neural networks. In this study we propose a new unsupervised and semi-supervised technique known as the topological hierarchical decomposition (THD). This process breaks a dataset down into ever smaller groups, where groups are associated with a simplicial complex that approximate the underlying topology of a dataset. We apply THD to the FICO machine learning challenge dataset, consisting of anonymized home equity loan applications using the MAPPER algorithm to build simplicial complexes. We identify different groups of individuals unable to pay back loans, and illustrate how the distribution of feature values in a simplicial complex can be used to explain the decision to grant or deny a loan by extracting illustrative explanations from two THDs on the dataset.

  • From Black-Box to White-Box: Interpretable Learning with Kernel Machines; Hao Zhang, Shinji Nakadai, Kenji Fukumizu; We present a novel approach to interpretable learning with kernel machines. In many real-world learning tasks, kernel machines have been successfully applied. However, a common perception is that they are difficult to interpret by humans due to the inherent black-box nature. This restricts the application of kernel machines in domains where model interpretability is highly required. In this paper, we propose to construct interpretable kernel machines. Specifically, we design a new kernel function based on random Fourier features (RFF) for scalability, and develop a two-phase learning procedure: in the first phase, we explicitly map pairwise features to a high-dimensional space produced by the designed kernel, and learn a dense linear model; in the second phase, we extract an interpretable data representation from the first phase, and learn a sparse linear model. Finally, we evaluate our approach on benchmark datasets, and demonstrate its usefulness in terms of interpretability by visualization.

  • From Soft Classifiers to Hard Decisions: How fair can we be?; Ran Canetti, Aloni Cohen, Nishanth Dikkala, Govind Ramnarayan, Sarah Scheffler, Adam Smith; We study the feasibility of achieving various fairness properties by post-processing calibrated scores, and then show that deferring post-processors allow for more fairness conditions to hold on the final decision. Specifically, we show: 1. There does not exist a general way to post-process a calibrated classifier to equalize protected groups' positive or negative predictive value (PPV or NPV). For certain "nice" calibrated classifiers, either PPV or NPV can be equalized when the post-processor uses different thresholds across protected groups... 2. When the post-processing is allowed to defer on some decisions (that is, to avoid making a decision by handing off some examples to a separate process), then for the non-deferred decisions, the resulting classifier can be made to equalize PPV, NPV, false positive rate (FPR) and false negative rate (FNR) across the protected groups. This suggests a way to partially evade the impossibility results of Chouldechova and Kleinberg et al., which preclude equalizing all of these measures simultaneously. We also present different deferring strategies and show how they affect the fairness properties of the overall system. We evaluate our post-processing techniques using the COMPAS data set from 2016.

  • A Survey of Methods for Explaining Black Box Models; Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, Dino Pedreschi; In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.

  • Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning; Nicolas Papernot, Patrick McDaniel; In this work, we exploit the structure of deep learning to enable new learning-based inference and decision strategies that achieve desirable properties such as robustness and interpretability. We take a first step in this direction and introduce the Deep k-Nearest Neighbors (DkNN). This hybrid classifier combines the k-nearest neighbors algorithm with representations of the data learned by each layer of the DNN: a test input is compared to its neighboring training points according to the distance that separates them in the representations. We show the labels of these neighboring points afford confidence estimates for inputs outside the model's training manifold, including on malicious inputs like adversarial examples--and therein provides protections against inputs that are outside the models understanding. This is because the nearest neighbors can be used to estimate the nonconformity of, i.e., the lack of support for, a prediction in the training data. The neighbors also constitute human-interpretable explanations of predictions.

  • RISE: Randomized Input Sampling for Explanation of Black-box Models; Vitali Petsiuk, Abir Das, Kate Saenko; Deep neural networks are being used increasingly to automate data analysis and decision making, yet their decision-making process is largely unclear and is difficult to explain to the end users. In this paper, we address the problem of Explainable AI for deep neural networks that take images as input and output a class probability. We propose an approach called RISE that generates an importance map indicating how salient each pixel is for the model's prediction. In contrast to white-box approaches that estimate pixel importance using gradients or other internal network state, RISE works on black-box models. It estimates importance empirically by probing the model with randomly masked versions of the input image and obtaining the corresponding outputs. We compare our approach to state-of-the-art importance extraction methods using both an automatic deletion/insertion metric and a pointing metric based on human-annotated object segments. Extensive experiments on several benchmark datasets show that our approach matches or exceeds the performance of other methods, including white-box approaches.

  • Visualizing the Feature Importance for Black Box Models; Giuseppe Casalicchio, Christoph Molnar, and Bernd Bisch; Based on a recent method for model-agnostic global feature importance, we introduce a local feature importance measure for individual observations and propose two visual tools: partial importance (PI) and individual conditional importance (ICI) plots which visualize how changes in a feature affect the model performance on average, as well as for individual observations. Our proposed methods are related to partial dependence (PD) and individual conditional expectation (ICE) plots, but visualize the expected (conditional) feature importance instead of the expected (conditional) prediction. Furthermore, we show that averaging ICI curves across observations yields a PI curve, and integrating the PI curve with respect to the distribution of the considered feature results in the global feature importance

  • Interpreting Blackbox Models via Model Extraction; Osbert Bastani, Carolyn Kim, Hamsa Bastani; Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions. We propose to construct global explanations of complex, blackbox models in the form of a decision tree approximating the original model---as long as the decision tree is a good approximation, then it mirrors the computation performed by the blackbox model. We devise a novel algorithm for extracting decision tree explanations that actively samples new training points to avoid overfitting. We evaluate our algorithm on a random forest to predict diabetes risk and a learned controller for cart-pole. Compared to several baselines, our decision trees are both substantially more accurate and equally or more interpretable based on a user study. Finally, we describe several insights provided by our interpretations, including a causal issue validated by a physician.

  • A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees; Min Wu, Matthew Wicke1, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska; Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the feature robustness problem, which aims to quantify the robustness of individual features to adversarial perturbations. We demonstrate that, under the assumption of Lipschitz continuity, both problems can be approximated using finite optimisation by discretising the input space, and the approximation has provable guarantees, i.e., the error is bounded. We then show that the resulting optimisation problems can be reduced to the solution of two-player turn-based games, where the first player selects features and the second perturbs the image within the feature. While the second player aims to minimise the distance to an adversarial example, depending on the optimisation objective the first player can be cooperative or competitive. We employ an anytime approach to solve the games, in the sense of approximating the value of a game by monotonically improving its upper and lower bounds. The Monte Carlo tree search algorithm is applied to compute upper bounds for both games, and the Admissible A* and the Alpha-Beta Pruning algorithms are, respectively, used to compute lower bounds for the maximum safety radius and feature robustness games. When working on the upper bound of the maximum safe radius problem, our tool demonstrates competitive performance against existing adversarial example crafting algorithms. Furthermore, we show how our framework can be deployed to evaluate pointwise robustness of neural networks in safety-critical applications such as traffic sign recognition in self-driving cars.

  • All Models are Wrong but Many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance; Aaron Fisher, Cynthia Rudin, Francesca Dominici; Variable importance (VI) tools describe how much covariates contribute to a prediction model’s accuracy. However, important variables for one well-performing model (for example, a linear model f(x) = x T β with a fixed coefficient vector β) may be unimportant for another model. In this paper, we propose model class reliance (MCR) as the range of VI values across all well-performing model in a prespecified class. Thus, MCR gives a more comprehensive description of importance by accounting for the fact that many prediction models, possibly of different parametric forms, may fit the data well.

  • Please Stop Explaining Black Box Models for High Stakes Decisions; Cynthia Rudin; There are black box models now being used for high stakes decision-making throughout society. The practice of trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward – it is to design models that are inherently interpretable.

  • State of the Art in Fair ML: From Moral Philosophy and Legislation to Fair Classifiers; Elias Baumann, Josef Rumberger; Machine learning is becoming an ever present part in our lives as many decisions, e.g. to lend a credit, are no longer made by humans but by machine learning algorithms. However those decisions are often unfair and discriminating individuals belonging to protected groups based on race or gender. With the recent General Data Protection Regulation (GDPR) coming into effect, new awareness has been raised for such issues and with computer scientists having such a large impact on peoples lives it is necessary that actions are taken to discover and prevent discrimination. This work aims to give an introduction into discrimination, legislative foundations to counter it and strategies to detect and prevent machine learning algorithms from showing such behavior.

  • Explaining Explanations in AI; Brent Mittelstadt, Chris Russell, Sandra Wachter; Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it's important to remember Box's maxim that "All models are wrong but some are useful." We focus on the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly.

  • On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection; Vivian Lai, Chenhao Tan; Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full automation. To realize the potential of machine learning for improving human decisions, it is important to understand how assistance from machine learning models affect human performance and human agency. In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency. We propose a spectrum between full human agency and full automation, and develop varying levels of machine assistance along the spectrum that gradually increase the influence of machine predictions. We find that without showing predicted labels, explanations alone do not statistically significantly improve human performance in the end task. In comparison, human performance is greatly improved by showing predicted labels (>20% relative improvement) and can be further improved by explicitly suggesting strong machine performance. Interestingly, when predicted labels are shown, explanations of machine predictions induce a similar level of accuracy as an explicit statement of strong machine performance. Our results demonstrate a tradeoff between human performance and human agency and show that explanations of machine predictions can moderate this tradeoff.

  • On the Art and Science of Machine Learning Explanations; Patrick Hall; explanatory methods that go beyond the error measurements and plots traditionally used to assess machine learning models. Some of the methods are tools of the trade while others are rigorously derived and backed by long-standing theory. The methods, decision tree surrogate models, individual conditional expectation (ICE) plots, local interpretable model agnostic explanations (LIME), partial dependence plots, and Shapley explanations, vary in terms of scope, fidelity, and suitable application domain. Along with descriptions of these methods, this text presents real-world usage recommendations supported by a use case and in-depth software examples.

  • Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems; Richard Tomsett, Dave Braines, Dan Harborne, Alun Preece, Supriyo Chakraborty; we should not ask if the system is interpretable, but to whom is it interpretable. We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system. We illustrate the use of our model in a variety of scenarios, exploring how an agent's role influences its goals, and the implications for defining interpretability. Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.

  • Interpreting Models by Allowing to Ask; Sungmin Kang, David Keetae Park, Jaehyuk Chang, Jaegul Choo; Questions convey information about the questioner, namely what one does not know. In this paper, we propose a novel approach to allow a learning agent to ask what it considers as tricky to predict, in the course of producing a final output. By analyzing when and what it asks, we can make our model more transparent and interpretable. We first develop this idea to propose a general framework of deep neural networks that can ask questions, which we call asking networks. A specific architecture and training process for an asking network is proposed for the task of colorization, which is an exemplar one-to-many task and thus a task where asking questions is helpful in performing the task accurately. Our results show that the model learns to generate meaningful questions, asks difficult questions first, and utilizes the provided hint more efficiently than baseline models. We conclude that the proposed asking framework makes the learning agent reveal its weaknesses, which poses a promising new direction in developing interpretable and interactive models.

  • Contrastive Explanation: A Structural-Model Approach; Tim Miller; ...Research in philosophy and social sciences shows that explanations are contrastive: that is, when people ask for an explanation of an event the fact they (sometimes implicitly) are asking for an explanation relative to some contrast case; that is, "Why P rather than Q?". In this paper, we extend the structural causal model approach to define two complementary notions of contrastive explanation, and demonstrate them on two classical AI problems: classification and planning.

  • Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation; Jichen Zhu, Antonios Liapis, Sebastian Risi, Rafael Bidarra, Michael Youngblood; In this vision paper, we propose a new research area of eXplainable AI for Designers (XAID), specifically for game designers. By focusing on a specific user group, their needs and tasks, we propose a human-centered approach for facilitating game designers to co-create with AI/ML techniques through XAID. We illustrate our initial XAID framework through three use cases, which require an understanding both of the innate properties of the AI techniques and users’ needs, and we identify key open challenges.

  • AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling; Cristina Conati, Kaska Porayska-Pomsta, Manolis Mavrikis; Interpretability of the underlying AI representations is a key raison d'être for Open Learner Modelling (OLM) -- a branch of Intelligent Tutoring Systems (ITS) research. OLMs provide tools for 'opening' up the AI models of learners' cognition and emotions for the purpose of supporting human learning and teaching. - use case

  • Instance-Level Explanations for Fraud Detection: A Case Study; Dennis Collaris, Leo M. Vink, Jarke J. van Wijk; Fraud detection is a difficult problem that can benefit from predictive modeling. However, the verification of a prediction is challenging; for a single insurance policy, the model only provides a prediction score. We present a case study where we reflect on different instance-level model explanation techniques to aid a fraud detection team in their work. To this end, we designed two novel dashboards combining various state-of-the-art explanation techniques.

  • On the Robustness of Interpretability Methods; David Alvarez-Melis, Tommi S. Jaakkola; We argue that robustness of explanations---i.e., that similar inputs should give rise to similar explanations---is a key desideratum for interpretability. We introduce metrics to quantify robustness and demonstrate that current methods do not perform well according to these metrics. Finally, we propose ways that robustness can be enforced on existing interpretability approaches.

  • Contrastive Explanations with Local Foil Trees; Jasper van der Waa, Marcel Robeer, Jurriaan van Diggelen, Matthieu Brinkhuis, Mark Neerincx; Recent advances in interpretable Machine Learning (iML) and eXplainable AI (XAI) construct explanations based on the importance of features in classification tasks. However, in a high-dimensional feature space this approach may become unfeasible without restraining the set of important features. We propose to utilize the human tendency to ask questions like "Why this output (the fact) instead of that output (the foil)?" to reduce the number of features to those that play a main role in the asked contrast. Our proposed method utilizes locally trained one-versus-all decision trees to identify the disjoint set of rules that causes the tree to classify data points as the foil and not as the fact.

  • Evaluating Feature Importance Estimates; Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim; Estimating the influence of a given feature to a model prediction is challenging. We introduce ROAR, RemOve And Retrain, a benchmark to evaluate the accuracy of interpretability methods that estimate input feature importance in deep neural networks. We remove a fraction of input features deemed to be most important according to each estimator and measure the change to the model accuracy upon retraining.

  • Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach; Arthur Colombini Gusmão, Alvaro Henrique Chaim Correia, Glauber De Bona, Fabio Gagliardi Cozman; Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.

  • Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models; Jiawei Zhang, Yang Wang, Piero Molino, Lezhi Li and David S. Ebert; Intoduces Manifold - tool for visual exploration of a model during inspection (hypothesis), explanation (reasoning), and refinement (verification). Supports comparison of multiple models. Visual exploratory approach for machine learning model development.

  • Interpretable Explanations of Black Boxes by Meaningful Perturbation; Ruth C. Fong, Andrea Vedaldi; (from abstract) general framework for learning different kinds of explanations for any black box algorithm. framework to find the part of an image most responsible for a classifier decision... method is model-agnostic and testable because it is grounded in explicit and interpretable image perturbations.

  • Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models; Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana; (...) We then develop a post-processing technique (API) that provably transforms pretrained additive models to satisfy the interpretability axioms without sacrificing accuracy. The technique works not just on models trained with our algorithm, but on any multiclass additive model. We demonstrate API on a 12-class infant-mortality dataset. (...) Initially for Generalized additive models (GAMs).

  • Statistical Paradises and Paradoxes in Big Data; Xiao-Li Meng; (...) Paradise gained or lost? Data quality-quantity tradeoff. (“Which one should I trust more: a 1% survey with 60% response rate or a non-probabilistic dataset covering 80% of the population?”); Data Quality × Data Quantity × Problem Difficulty;

  • Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges; Gabrielle Ras, Marcel van Gerven, Pim Haselager; Issues regarding explainable AI involve four components: users, laws & regulations, explanations and algorithms. Overall, it is clear that (visual) explanations can be given about various aspects of the influence of the input on the output ... It is likely that in the future we will see the rise of a new category of explanation methods that combine aspects of rule-extraction, attribution and intrinsic methods, to answer specific questions in a simple human interpretable language. Furthermore, it is obvious that current explanation methods are tailored to expert users, since the interpretation of the results require knowledge of the DNN process. As far as we are aware, explanation methods, e.g. intuitive explanation interfaces, for lay users do not exist.

  • TED: Teaching AI to Explain its Decisions; Noel C. F. Codella et al; Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a growing demand for such systems to provide explanations for their decisions. Conventional approaches to this problem attempt to expose or discover the inner workings of a machine learning model with the hope that the resulting explanations will be meaningful to the consumer. In contrast, this paper suggests a new approach to this problem. It introduces a simple, practical framework, called Teaching Explanations for Decisions (TED), that provides meaningful explanations that match the mental model of the consumer.

  • Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard?; John Zerilli, Alistair Knott, James Maclaurin, Colin Gavaghan; We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and explainability are certainly important desiderata in algorithmic governance, we worry that automated decision-making is being held to an unrealistically high standard, possibly owing to an unrealistically high estimate of the degree of transparency attainable from human decision-makers. In this paper, we review evidence demonstrating that much human decision-making is fraught with transparency problems, show in what respects AI fares little worse or better and argue that at least some regulatory proposals for explainable AI could end up setting the bar higher than is necessary or indeed helpful. The demands of practical reason require the justification of action to be pitched at the level of practical reason. Decision tools that support or supplant practical reasoning should not be expected to aim higher than this. We cast this desideratum in terms of Daniel Dennett’s theory of the “intentional stance” and argue that since the justification of action for human purposes takes the form of intentional stance explanation, the justification of algorithmic decisions should take the same form. In practice, this means that the sorts of explanations for algorithmic decisions that are analogous to intentional stance explanations should be preferred over ones that aim at the architectural innards of a decision tool.

  • A comparative study of fairness-enhancing interventions in machine learning; Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P. Hamilton, Derek Roth; Computers are increasingly used to make decisions that have significant impact in people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers and predictors have appeared in the literature. This paper seeks to study the following questions: how do these different techniques fundamentally compare to one another, and what accounts for the differences? Specifically, we seek to bring attention to many under-appreciated aspects of such fairness-enhancing interventions. Concretely, we present the results of an open benchmark we have developed that lets us compare a number of different algorithms under a variety of fairness measures, and a large number of existing datasets. We find that although different algorithms tend to prefer specific formulations of fairness preservations, many of these measures strongly correlate with one another. In addition, we find that fairness-preserving algorithms tend to be sensitive to fluctuations in dataset composition (simulated in our benchmark by varying training-test splits), indicating that fairness interventions might be more brittle than previously thought.

  • Check yourself before you wreck yourself: Assessing discrete choice models through predictive simulations; Timothy Brathwaite; Graphical model checks : Typically, discrete choice modelers develop ever-more advanced models and estimation methods. Compared to the impressive progress in model development and estimation, model-checking techniques have lagged behind. Often, choice modelers use only crude methods to assess how well an estimated model represents reality. Such methods usually stop at checking parameter signs, model elasticities, and ratios of model coefficients. In this paper, I greatly expand the discrete choice modelers' assessment toolkit by introducing model checking procedures based on graphical displays of predictive simulations.

  • Example and Feature importance-based Explanations for Black-box Machine Learning Models; Ajaya Adhikari, D.M.J Tax, Riccardo Satta, Matthias Fath; As machine learning models become more accurate, they typically become more complex and uninterpretable by humans. The black-box character of these models holds back its acceptance in practice, especially in high-risk domains where the consequences of failure could be catastrophic such as health-care or defense. Providing understandable and useful explanations behind ML models or predictions can increase the trust of the user. Example-based reasoning, which entails leveraging previous experience with analogous tasks to make a decision, is a well known strategy for problem solving and justification. This work presents a new explanation extraction method called LEAFAGE, for a prediction made by any black-box ML model. The explanation consists of the visualization of similar examples from the training set and the importance of each feature. Moreover, these explanations are contrastive which aims to take the expectations of the user into account. LEAFAGE is evaluated in terms of fidelity to the underlying black-box model and usefulness to the user. The results showed that LEAFAGE performs overall better than the current state-of-the-art method LIME in terms of fidelity, on ML models with non-linear decision boundary. A user-study was conducted which focused on revealing the differences between example-based and feature importance-based explanations. It showed that example-based explanations performed significantly better than feature importance-based explanation, in terms of perceived transparency, information sufficiency, competence and confidence. Counter-intuitively, when the gained knowledge of the participants was tested, it showed that they learned less about the black-box model after seeing a feature importance-based explanation than seeing no explanation at all. The participants found feature importance-based explanation vague and hard to generalize it to other instances.

2017

  • Black Hat Visualization; Michael Correll, Jeffrey Heer; People lie, mislead, and bullshit in a myriad of ways. Visualizations,as a form of communication, are no exception to these tendencies.Yet, the language we use to describe how people can use visualiza-tions to mislead can be relatively sparse. For instance, one can be“lying with vis” or using “deceptive visualizations.” In this paper, weuse the language of computer security to expand the space of waysthat unscrupulous people (black hats) can manipulate visualizationsfor nefarious ends. In addition to forms of deception well-coveredin the visualization literature, we also focus on visualizations whichhave fidelity to the underlying data (and so may not be considereddeceptive in the ordinary use of the term in visualization), but stillhave negative impact on how data are perceived. We encouragedesigners to think defensively and comprehensively about how theirvisual designs can result in data being misinterprete.

images/correll_2017.png

  • A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations; Josua Krause, Aritra Dasgupta, Jordan Swartz, Yindalon Aphinyanaphongs, Enrico Bertini; Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary classifier. The approach leverages "instance-level explanations", measures of local feature relevance that explain single instances, and uses them to build a set of visual representations that guide the users in their investigation. The workflow is based on three main visual representations and steps: one based on aggregate statistics to see how data distributes across correct / incorrect decisions; one based on explanations to understand which features are used to make these decisions; and one based on raw data, to derive insights on potential root causes for the observed patterns.
  • Fair Forests: Regularized Tree Induction to Minimize Model Bias; Edward Raff, Jared Sylvester, Steven Mills; The potential lack of fairness in the outputs of machine learning algorithms has recently gained attention both within the research community as well as in society more broadly. Surprisingly, there is no prior work developing tree-induction algorithms for building fair decision trees or fair random forests. These methods have widespread popularity as they are one of the few to be simultaneously interpretable, non-linear, and easy-to-use. In this paper we develop, to our knowledge, the first technique for the induction of fair decision trees. We show that our "Fair Forest" retains the benefits of the tree-based approach, while providing both greater accuracy and fairness than other alternatives, for both "group fairness" and "individual fairness.'" We also introduce new measures for fairness which are able to handle multinomial and continues attributes as well as regression problems, as opposed to binary attributes and labels only. Finally, we demonstrate a new, more robust evaluation procedure for algorithms that considers the dataset in its entirety rather than only a specific protected attribute.
  • Towards A Rigorous Science of Interpretable Machine Learning; Finale Doshi-Velez and Been Kim; In such cases, a popular fallback is the criterion of interpretability: if the system can explain its reasoning, we then can verify whether that reasoning is sound with respect to these auxiliary criteria. Unfortunately, there is little consensus on what interpretability in machine learning is and how to evaluate it for benchmarking. To large extent, both evaluation approaches rely on some notion of “you’ll know it when you see it.” Should we be concerned about a lack of rigor?; Multi-objective trade-offs: Mismatched objectives: Ethics: Safety: Scientific Understanding:
  • Attentive Explanations: Justifying Decisions and Pointing to the Evidence; Dong Huk Park et al; Deep models are the defacto standard in visual decision problems due to their impressive performance on a wide array of visual tasks. We propose two large-scale datasets with annotations that visually and textually justify a classification decision for various activities, i.e. ACT-X, and for question answering, i.e. VQA-X.
  • SPINE: SParse Interpretable Neural Embeddings; Anant Subramanian, Danish Pruthi, Harsh Jhamtani, Taylor Berg-Kirkpatrick, Eduard Hovy; Prediction without justification has limited utility. Much of the success of neural models can be attributed to their ability to learn rich, dense and expressive representations. While these representations capture the underlying complexity and latent trends in the data, they are far from being interpretable. We propose a novel variant of denoising k-sparse autoencoders that generates highly efficient and interpretable distributed word representations (word embeddings), beginning with existing word representations from state-of-the-art methods like GloVe and word2vec. Through large scale human evaluation, we report that our resulting word embedddings are much more interpretable than the original GloVe and word2vec embeddings. Moreover, our embeddings outperform existing popular word embeddings on a diverse suite of benchmark downstream tasks.
  • Detecting concept drift in data streams using model explanation; Jaka Demšar, Zoran Bosnic; Interesting use case for explainers - PDP like explainers are used to identify concept drift.
  • Explanation of Prediction Models with ExplainPrediction intoroduces two methods EXPLAIN and IME (R packages) for local and global explanations.
  • What do we need to build explainable AI systems for the medical domain?; Andreas Holzinger, Chris Biemann, Constantinos Pattichis, Douglas Kell. In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain. This is due to the fact that medical professionals are working mostly with distributed heterogeneous and complex sources of data. In this paper we concentrate on three sources: images, omics data and text. We argue that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitate transparency and trust. However, the full effectiveness of all AI/ML success is limited by the algorithm’s inabilities to explain its results to human experts - but exactly this is a big issue in the medical domain.

2016

  • Equality of Opportunity in Supervised Learning; Moritz Hardt, Eric Price, Nathan Srebro; We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to the decision maker, who can respond by improving the classification accuracy. In line with other studies, our notion is oblivious: it depends only on the joint statistics of the predictor, the target and the protected attribute, but not on interpretation of individualfeatures. We study the inherent limits of defining and identifying biases based on such oblivious measures, outlining what can and cannot be inferred from different oblivious tests. We illustrate our notion using a case study of FICO credit scores. images/equal_opp.png

  • Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models; Josua Krause, Adam Perer, Kenney Ng; Describes Prospector - tool for visual exploration of predictive models. Few interesting and novel ideas, like Partial Dependence Bars. Prospector can compare models and shows both local and global explanations.

  • The Mythos of Model Interpretability; Zachary C. Lipton; Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. (...) First, we examine the motivations underlying interest in interpretability, finding them to be diverse and occasionally discordant. Then, we address model properties and techniques thought to confer interpretability, identifying transparency to humans and post-hoc explanations as competing notions. Throughout, we discuss the feasibility and desirability of different notions, and question the oft-made assertions that linear models are interpretable and that deep neural networks are not.

  • What makes classification trees comprehensible?; Rok Piltaver, Mitja Luštrek, Matjaž Gams, Sanda Martinčić-Ipšić; Classification trees are attractive for practical applications because of their comprehensibility. However, the literature on the parameters that influence their comprehensibility and usability is scarce. This paper systematically investigates how tree structure parameters (the number of leaves, branching factor, tree depth) and visualisation properties influence the tree comprehensibility. In addition, we analyse the influence of the question depth (the depth of the deepest leaf that is required when answering a question about a classification tree), which turns out to be the most important parameter, even though it is usually overlooked. The analysis is based on empirical data that is obtained using a carefully designed survey with 98 questions answered by 69 respondents. The paper evaluates several tree-comprehensibility metrics and proposes two new metrics (the weighted sum of the depths of leaves and the weighted sum of the branching factors on the paths from the root to the leaves) that are supported by the survey results. The main advantage of the new comprehensibility metrics is that they consider the semantics of the tree in addition to the tree structure itself.

2015

  • The Residual-based Predictiveness Curve - A Visual Tool to Assess the Performance of Prediction Models; Giuseppe Casalicchio, Bernd Bischl, Anne-Laure Boulesteix, Matthias Schmid; The RBP (residual-based predictiveness) curve reflects both the calibration and the discriminatory power of a prediction model. In addition, the curve can be conveniently used to conduct valid performance checks and marker comparisons. The RBP curve is implemented in the R package RBPcurve.
  • Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model; Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan; We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then... statements (e.g., if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of simple, readily interpretable decision statements. We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists. It employs a novel prior structure to encourage sparsity.

2009

  • How to Explain Individual Classification Decisions, David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Muller; (from abstract) The only method that is currently able to provide such explanations are decision trees. ... Model agnostic method, introduces explanation vectors that summarise steepness of changes of model decisions as function of model inputs.

2005

  • The Tyranny of Tacit Knowledge: What Artificial Intelligence Tells us About Knowledge Representation ; Kurt D. Fenstermacher; Polanyi's tacit knowledge captures the idea "we can know more than we can tell." Many researchers in the knowledge management community have used the idea of tacit knowledge to draw a distinction between that which cannot be formally represented (tacit knowledge) and knowledge which can be so represented (explicit knowledge). I argue that the deference that knowledge management researchers give to tacit knowledge hinders potentially fruitful work for two important reasons. First, the inability to explicate knowledge does not imply that the knowledge cannot be formally represented. Second, assuming the inability to formalize tacit knowledge as it exists in the minds of people does not exclude the possibility that computer systems might perform the same tasks using alternative representations. By reviewing work from artificial intelligence, I will argue that a richer model of cognition and knowledge representation is needed to study and build knowledge management systems.

2004

Books

2018

Tools

2019

  • Assessing Causality from Observational Data using Pearl's Structural Causal Models;
  • sklearn_explain; Model explanation provides the ability to interpret the effect of the predictors on the composition of an individual score.
  • heatmapping.org; This webpage aims to regroup publications and software produced as part of a joint project at Fraunhofer HHI, TU Berlin and SUTD Singapore on developing new method to understand nonlinear predictions of state-of-the-art machine learning models. Machine learning models, in particular deep neural networks (DNNs), are characterized by very high predictive power, but in many case, are not easily interpretable by a human. Interpreting a nonlinear classifier is important to gain trust into the prediction, and to identify potential data selection biases or artefacts. The project studies in particular techniques to decompose the prediction in terms of contributions of individual input variables such that the produced decomposition (i.e. explanation) can be visualized in the same way as the input data.
  • ggeffects; Daniel Lüdecke; Compute marginal effects from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Marginal effects can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a generic plot()-method to plot the results using 'ggplot2'.

2018

  • KDD 2018: Explainable Models for Healthcare AI; The Explainable Models for Healthcare AI tutorial was presented by a trio from KenSci Inc. that included a data scientist and a clinician. The premise of the session was that explainability is particularly important in healthcare applications of machine learning, due to the far-reaching consequences of decisions, high cost of mistakes, fairness and compliance requirements. The tutorial walked through a number of aspects of interpretability and discussed techniques that can be applied to explain model predictions.
  • MAGMIL: Model Agnostic Methods for Interpretable Machine Learning; European Union’s new General Data Protection Regulation which is going to be enforced beginning from 25th of May, 2018 will have potential impact on the routine use of machine learning algorithms by restricting automated individual decision-making (that is, algorithms that make decisions based on user-level predictors) which ”significantly affect” users. The law will also effectively create a ”right to explanation,” whereby a user can ask for an explanation of an algorithmic decision that was made about them. Considering such challenging norms on the use of machine learning systems, we are making an attempt to make the models more interpretable. While we are concerned about developing a deeper understanding of decisions made by a machine learning model, the idea of extracting the explaintations from the machine learning system, also known as model-agnostic interpretability methods has some benefits over techniques such as model specific interpretability methods in terms of flexibility.
  • A toolbox to iNNvestigate neural networks' predictions!; Maximilian Alber; In the recent years neural networks furthered the state of the art in many domains like, e.g., object detection and speech recognition. Despite the success neural networks are typically still treated as black boxes. Their internal workings are not fully understood and the basis for their predictions is unclear. In the attempt to understand neural networks better several methods were proposed, e.g., Saliency, Deconvnet, GuidedBackprop, SmoothGrad, IntergratedGradients, LRP, PatternNet&-Attribution. Due to the lack of a reference implementations comparing them is a major effort. This library addresses this by providing a common interface and out-of-the-box implementation for many analysis methods. Our goal is to make analyzing neural networks' predictions easy!
  • Black Box Auditing and Certifying and Removing Disparate Impact; This repository contains a sample implementation of Gradient Feature Auditing (GFA) meant to be generalizable to most datasets. For more information on the repair process, see our paper on Certifying and Removing Disparate Impact. For information on the full auditing process, see our paper on Auditing Black-box Models for Indirect Influence.
  • Skater: Python Library for Model Interpretation/Explanations; Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction).
  • Weight Watcher; Charles Martin; Weight Watcher analyzes the Fat Tails in the weight matrices of Deep Neural Networks (DNNs). This tool can predict the trends in the generalization accuracy of a series of DNNs, such as VGG11, VGG13, ..., or even the entire series of ResNet models--without needing a test set ! This relies upon recent research into the Heavy (Fat) Tailed Self Regularization in DNNs
  • Adversarial Robustness Toolbox - ART; This is a library dedicated to adversarial machine learning. Its purpose is to allow rapid crafting and analysis of attacks and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers.
  • Model Describer; Python script that generates html report that summarizes predictive models. Interactive and rich in descriptions.
  • AI Fairness 360; Python library developed by IBM to help detect and remove bias in machine learning models. Some introduction
  • The What-If Tool: Code-Free Probing of Machine Learning Models; An interactive tool for What-If scenarios developed in Google, part of TensorBoard.

2017

  • Impact encoding for categorical features; Imagine working with a dataset containing all the zip codes in the United States. That is a datset containing nearly 40,000 unique categories. How would you deal with that kind of data if you planned to do predictive modelling? One hot encoding doesn't get you anywhere useful, since that would add 40,000 sparse variables to your dataset. Throwing the data out could be leaving valuable information on the table, so that doesn't seem right either. In this post, I'm going to examine how to deal with categorical variables with high cardinality using a stratey called impact encoding. To illustrate this example, I use a data set containing used car sales. The probelm is especially well suited because there are several categorical features with many levels. Let's get started.
  • FairTest; FairTest enables developers or auditing entities to discover and test for unwarranted associations between an algorithm's outputs and certain user subpopulations identified by protected features.
  • Explanation Explorer; Visual tool implemented in python for visual diagnostics of binary classifiers using lnstance-level explanations (local explainers).
  • ggeffects; Create Tidy Data Frames of Marginal Effects for ‚ggplot‘ from Model Outputs, The aim of the ggeffects-package is similar to the broom-package: transforming “untidy” input into a tidy data frame, especially for further use with ggplot. However, ggeffects does not return model-summaries; rather, this package computes marginal effects at the mean or average marginal effects from statistical models and returns the result as tidy data frame (as tibbles, to be more precisely).

Articles

2019

  • The AI Black Box Explanation Problem; At a very high level, we articulated the problem in two different flavours: eXplanation by Design (XbD): given a dataset of training decision records, how to develop a machine learning decision model together with its explanation; Black Box eXplanation (BBX): given the decision records produced by a black box decision model, how to reconstruct an explanation for it.
  • VOZIQ Launches ‘Agent Connect,’ an Explainable AI Product to Enable Large-Scale Customer Retention Programs; RESTON, VIRGINIA , USA, April 3, 2019 /EINPresswire.com/ -- VOZIQ, an enterprise cloud-based application solution provider that enables recurring revenue businesses to drive large-scale predictive customer retention programs, announced the launch of its new eXplainable AI (XAI) product ‘Agent Connect’ to help businesses enhance proactive retention capabilities of their most critical resource – customer retention agents. ‘Agent Connect’ is VOZIQ’s newest product powered by next-generation eXplainable AI (XAI) that brings together multiple retention risk signals with expressed and inferred needs, sentiment, churn drivers and behaviors that lead to attrition of customers discovered directly from millions of customer interactions by analyzing unstructured and structured customer data, and converts insights those into easy-to-act, prescriptive intelligence about predicted health for any customer.
  • Derisking machine learning and artificial intelligence ; Machine learning and artificial intelligence are set to transform the banking industry, using vast amounts of data to build models that improve decision making, tailor services, and improve risk management. According to the McKinsey Global Institute, this could generate value of more than $250 billion in the banking industry. But there is a downside, since machine-learning models amplify some elements of model risk. And although many banks, particularly those operating in jurisdictions with stringent regulatory requirements, have validation frameworks and practices in place to assess and mitigate the risks associated with traditional models, these are often insufficient to deal with the risks associated with machine-learning models. Conscious of the problem, many banks are proceeding cautiously, restricting the use of machine-learning models to low-risk applications, such as digital marketing. Their caution is understandable given the potential financial, reputational, and regulatory risks. Banks could, for example, find themselves in violation of antidiscrimination laws, and incur significant fines—a concern that pushed one bank to ban its HR department from using a machine-learning résumé screener. A better approach, however, and ultimately the only sustainable one if banks are to reap the full benefits of machine-learning models, is to enhance model-risk management.
  • Explainable AI should help us avoid a third 'AI winter'; The General Data Protection Regulation (GDPR) that came into force last year across Europe has rightly made consumers and businesses more aware of personal data. However, there is a real risk that through over-correcting around data collection critical AI development will be negatively impacted. This is not only an issue for data scientists, but also those companies that use AI-based solutions to increase competitiveness. The potential negative impact would not only be on businesses implementing AI but also on consumers who may miss out on the benefits AI could bring to the products and services they rely on.
  • Explainable AI: From Prediction To Understanding; It’s not enough to make predictions. Sometimes, you need to generate a deep understanding. Just because you model something doesn’t mean you really know how it works. In classical machine learning, the algorithm spits out predictions, but in some cases, this isn’t good enough. Dr. George Cevora explains why the black box of AI may not always be appropriate and how to go from prediction to understanding.
  • Why Explainable AI (XAI) is the future of marketing and e-commerce; “New machine-learning systems will have the ability to explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future.” – David Gunning, Head of DARPA. As machine learning begins to play a greater role in the delivery of personalized customer experiences in commerce and content, one of the most powerful opportunities is the development of systems that offer marketers the ability to maximize every dollar spent on marketing programs via actionable insights. But the rise of AI in business for actionable insights also creates a challenge: How can marketers know and trust the reasoning behind why an AI system is making recommendations for action? Because AI makes decisions using incredibly complex processes, its decisions are often opaque to the end-user.
  • Interpretable AI or How I Learned to Stop Worrying and Trust AI Techniques to build Robust, Unbiased AI Applications; Ajay Thampi; In the last five years alone, AI researchers have made significant breakthroughs in areas such as image recognition, natural language understanding and board games! As companies are considering handing over critical decisions to AI in industries like healthcare and finance, the lack of understanding of complex machine learned models is hugely problematic. This lack of understanding could result in models propagating bias and we’ve seen quite a few examples of this in criminal justice, politics, retail, facial recognition and language understanding.
  • In Search of Explainable Artificial Intelligence; Today, if a new entrepreneur wants to understand why the banks rejected a loan application for his start-up, or if a young graduate wants to know why the large corporation for which he was hoping to work did not invite her for an interview, they will not be able to discover the reasons that led to these decisions. Both the bank and the corporation used artificial intelligence (AI) algorithms to determine the outcome of the loan or the job application. In practice, this means that if your loan application is rejected, or your CV rejected, no explanation can be provided. This produces an embarrassing scenario, which tends to relegate AI technologies to suggesting solutions, which must be validated by human beings.
  • Explainable AI and the Rebirth of Rules; Artificial intelligence (AI) has been described as a set of “prediction machines.” In general, the technology is great at generating automated predictions. But if you want to use artificial intelligence in a regulated industry, you better be able to explain how the machine predicted a fraud or criminal suspect, a bad credit risk, or a good candidate for drug trials. International law firm Taylor Wessing (the firm) wanted to use AI as a triage tool to help advise clients of the firm about their predicted exposure to regulations such as the Modern Slavery Act or the Foreign Corrupt Practices Act. Clients often have suppliers or acquisitions around the world, and they need systematic due diligence to determine where they should investigate more deeply into possible risk. Supply chains can be especially complicated with hundreds of small suppliers. Rumors of Rule Engines’ Death Have Been Greatly Exaggerated
  • Attacking discrimination with smarter machine learning; Here we discuss "threshold classifiers," a part of some machine learning systems that is critical to issues of discrimination. A threshold classifier essentially makes a yes/no decision, putting things in one category or another. We look at how these classifiers work, ways they can potentially be unfair, and how you might turn an unfair classifier into a fairer one. As an illustrative example, we focus on loan granting scenarios where a bank may grant or deny a loan based on a single, automatically computed number such as a credit score.
  • Better Preference Predictions: Tunable and Explainable Recommender Systems; Amber Roberts; Ad recommendations should be understandable to the individual consumer, but is it possible to increase interpretability without sacrificing accuracy?
  • Machine Learning is Creating a Crisis in Science; Kevin McCaney; The adoption of machine-learning techniques is contributing to a worrying number of research findings that cannot be repeated by other researchers.
  • Artificial Intelligence and Ethics; Jonathan Shaw; On march 2018, at around 10 P.M., Elaine Herzberg was wheeling her bicycle across a street in Tempe, Arizona, when she was struck and killed by a self-driving car. Although there was a human operator behind the wheel, an autonomous system—artificial intelligence—was in full control. This incident, like others involving interactions between people and AI technologies, raises a host of ethical and proto-legal questions. What moral obligations did the system’s programmers have to prevent their creation from taking a human life? And who was responsible for Herzberg’s death? The person in the driver’s seat? The company testing the car’s capabilities? The designers of the AI system, or even the manufacturers of its onboard sensory equipment?
  • Building Trusted Human-Machine Partnerships; A key ingredient in effective teams – whether athletic, business, or military – is trust, which is based in part on mutual understanding of team members’ competence to fulfill assigned roles. When it comes to forming effective teams of humans and autonomous systems, humans need timely and accurate insights about their machine partners’ skills, experience, and reliability to trust them in dynamic environments. At present, autonomous systems cannot provide real-time feedback when changing conditions such as weather or lighting cause their competency to fluctuate. The machines’ lack of awareness of their own competence and their inability to communicate it to their human partners reduces trust and undermines team effectiveness.
  • HOW AUGMENTED ANALYTICS AND EXPLAINABLE AI WILL CAUSE A DISRUPTION IN 2019 & BEYOND; Kamalika Some; Artificial intelligence (AI) is a transformational $15 trillion opportunity which has caught the attention of all tech users, leaders and influencers. Yet, as AI becomes more sophisticated, the algorithmic ‘black box’ dominates more to make all the decisions. To have a confident outcome and stakeholder trust with an ultimate aim to capitalise on the opportunities, it is essential to know the rationale of how the algorithm arrived at its recommendation or decision, the basic premise behind Explainable AI (XAI).
  • Why ‘Explainable AI’ is the Next Frontier in Financial Crime Fighting ; Chad Hetherington; Financial institutions (FIs) must manage compliance budgets without losing sight of primary functions and quality control. To answer this, many have made the move to automating time-intensive, rote tasks like data gathering and sorting through alerts by adopting innovative technologies like AI and machine learning to free up time-strapped analysts for more informed and precise decision-making processes.
  • Machine Learning Interpretability: Do You Know What Your Model Is Doing?; Marcel Spitzer; With the adoption of GDPR, there are now EU-wide regulations concerning automated individual decision-making and profiling (Art. 22, also termed „right to explanation“), engaging companies to give individuals information about processing, to introduce ways for them to request intervention and to even carry out regular checks to make sure that the systems are working as intended.
  • Building explainable machine learning models; Thomas Wood; Sometimes as data scientists we will encounter cases where we need to build a machine learning model that should not be a black box, but which should make transparent decisions that humans can understand. This can go against our instincts as scientists and engineers, as we would like to build the most accurate model possible.
  • AI is not IT; Silvie Spreeuwenberg; XAI suggests something in between. It is still narrow AI but used in such a way that there is a feedback loop to the environment. The feedback loop may involve human intervention. We understand the scope of the narrow AI solution. We can adjust the solution when the task at hand requires more knowledge, or are warned in a meaningful way when the task at hand does not fit in the scope of the AI solution.
  • A computer program used for bail and sentencing decisions was labeled biased against blacks. It’s actually not that clear.; This past summer, a heated debate broke out about a tool used in courts across the country to help make bail and sentencing decisions. It’s a controversy that touches on some of the big criminal justice questions facing our society. And it all turns on an algorithm.
  • AAAS: Machine learning 'causing science crisis'; Machine-learning techniques used by thousands of scientists to analyse data are producing results that are misleading and often completely wrong. Dr Genevera Allen from Rice University in Houston said that the increased use of such systems was contributing to a “crisis in science”. She warned scientists that if they didn’t improve their techniques they would be wasting both time and money.
  • Automatic Machine Learning is broken; Debt that comes with maintenance and understand of complex models
  • Charles River Analytics creates tool to help AI communicate effectively with humans; Developer of intelligent systems solutions, Charles River Analytics Inc. created the Causal Models to Explain Learning (CAMEL) approach under the Defense Advanced Research Projects Agency's (DARPA) Explainable Artificial Intelligence (XAI) effort. The goal of the CAMEL tool approach will be help artificial intelligence effectively communicate with human teammates.
  • Inside DARPA’s effort to create explainable artificial intelligence; Among DARPA’s many exciting projects is Explainable Artificial Intelligence (XAI), an initiative launched in 2016 aimed at solving one of the principal challenges of deep learning and neural networks, the subset of AI that is becoming increasing prominent in many different sectors.
  • Boston University researchers develop framework to improve AI fairness; Experience in the past few years shows AI algorithms can manifest gender and racial bias, raising concern over their use in critical domains, such as deciding whose loan gets approved, who’s qualified for a job, who gets to walk free and who stays in prison. New research by scientists at Boston University shows just how hard it is to evaluate fairness in AI algorithms and tries to establish a framework for detecting and mitigating problematic behavior in automated decisions. Titled “From Soft Classifiers to Hard Decisions: How fair can we be?,” the research paper is being presented this week at the Association for Computing Machinery conference on Fairness, Accountability, and Transparency (ACM FAT*).

2018

  • Understanding Explainable AI; (Extracted from The Basis Technology Handbook for Integrating AI in Highly Regulated Industries) For the longest time, the public perception of AI has been linked to visions of the apocalypse: AI is Skynet, and we should be afraid of it. You can see that fear in the reactions to the Uber self-driving car tragedy. Despite the fact that people cause tens of thousands of automobile deaths per year, it strikes a nerve when even a single accident involves AI. This fear belies something very important about the technical infrastructure of the modern world: AI is already thoroughly baked in. That’s not to say that there aren’t reasons to get skittish about our increasing reliance on AI technology. The “black box” problem is one such justified reason for hesitation.

  • The Importance of Human Interpretable Machine Learning; This article is the first in my series of articles aimed at ‘Explainable Artificial Intelligence (XAI)’. The field of Artificial Intelligence powered by Machine Learning and Deep Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. Rather than just running lab experiments to publish a research paper, the key objective of data science and machine learning in the 21st century has changed to tackling and solving real-world problems, automating complex tasks and making our life easier and better. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same. New models do come into existence like Capsule Networks, but industry adoption of the same usually takes several years. Hence, in the industry, the main focus of data science or machine learning is more ‘applied’ rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.

  • Uber Has Open-Sourced Autonomous Vehicle Visualization; With an open source version of its Autonomous Visualization System, Uber is hoping to create a standard visualization system for engineers to use in autonomous vehicle development.

  • Holy Grail of AI for Enterprise - Explainable AI (XAI); Saurabh Kaushik; Apart from a solution of the above scenarios, XAI offers deeper Business benefits, such as: Improves AI Model performance as explanation help pinpoint issues in data and feature behaviors. Better Decision Making as explanation provides added info and confidence for Man-in-Middle to act wisely and decisively. Gives a sense of Control as an AI system owner clearly knows levers for its AI system’s behavior and boundary. Gives a sense of Safety as each decision can be subjected to pass through safety guidelines and alerts on its violation. Build Trust with stakeholders who can see through all the reasoning of each and every decision made. Monitor for Ethical issues and violation due to bias in training data. Better mechanism to comply with Accountability requirements within the organization for auditing and other purposes. Better adherence to Regulatory requirements (like GDPR) where ‘Right to Explain’ is must-have for a system.

  • Artificial Intelligence Is Not A Technology; Kathleen Walch; Making intelligent machines is both the goal of AI as well as the underlying science behind understanding what it takes to make a machine intelligent. AI represents our desired outcome and many of the developments along the way of that understanding such as self-driving vehicles, image recognition technology, or natural language processing and generation are steps along the journey to AGI.

  • The Building Blocks of Interpretability; Chris Olah ...; Interpretability techniques are normally studied in isolation. We explore the powerful interfaces that arise when you combine them — and the rich structure of this combinatorial space

  • Why Machine Learning Interpretability Matters; Even though machine learning (ML) has been around for decades, it seems that in the last year, much of the news (notably in mainstream media) surrounding it has turned to interpretability - including ideas like trust, the ML black box, and fairness or ethics. Surely, if the topic is growing in popularity, that must mean it’s important. But why, exactly - and to whom?

  • IBM, Harvard develop tool to tackle black box problem in AI translation; seq2seq vis; Researchers at IBM and Harvard University have developed a new debugging tool to address this issue. Presented at the IEEE Conference on Visual Analytics Science and Technology in Berlin last week, the tool lets creators of deep learning applications visualize the decision-making an AI makes when translating a sequence of words from one language to another.

  • The Five Tribes of Machine Learning Explainers; Michał Łopuszyński; Lightning talk from PyData Berlin 2018

  • Beware Default Random Forest Importances; Terence Parr, Kerem Turgutlu, Christopher Csiszar, and Jeremy Howard; TL;DR: The scikit-learn Random Forest feature importance and R's default Random Forest feature importance strategies are biased. To get reliable results in Python, use permutation importance, provided here and in our rfpimp package (via pip). For R, use importance=T in the Random Forest constructor then type=1 in R's importance() function. In addition, your feature importance measures will only be reliable if your model is trained with suitable hyper-parameters.

  • A Case For Explainable AI & Machine Learning; Very nice list of possible use-cases for XAI, examples: Energy theft detection - Different types of theft require different action by the investigators; Credit scoring - he Fair Credit Reporting Act (FCRA) is a federal law that regulates credit reporting agencies and compels them to insure the information they gather and distribute is a fair and accurate summary of a consumer's credit history; Video threat detection - Flagging an individual as a threat has a potential for significant legal implications;

  • Ethics of AI: A data scientist’s perspective; QuantumBlack

  • Explainable AI vs Explaining AI; Ahmad Haj Mosa; Some ideas that links tools for XAI with ideas from ,,Thinking fast, thinking slow''.

  • Regulating Black-Box Medicine; Data drive modern medicine. And our tools to analyze those data are growing ever more powerful. As health data are collected in greater and greater amounts, sophisticated algorithms based on those data can drive medical innovation, improve the process of care, and increase efficiency. Those algorithms, however, vary widely in quality. Some are accurate and powerful, while others may be riddled with errors or based on faulty science. When an opaque algorithm recommends an insulin dose to a diabetic patient, how do we know that dose is correct? Patients, providers, and insurers face substantial difficulties in identifying high-quality algorithms; they lack both expertise and proprietary information. How should we ensure that medical algorithms are safe and effective?

  • 3 Signs of a Good AI Model; Troy Hiltbrand; Until recently, the success of an AI project was judged only by its outcomes for the company, but an emerging industry trend suggests another goal -- explainable artificial intelligence (XAI). The gravitation toward XAI stems from demand from consumers (and ultimately society) to better understand how AI decisions are made. Regulations, such as the General Data Protection Regulation (GDPR) in Europe, have increased the demand for more accountability when AI is used to make automated decisions, especially in cases where bias has a detrimental effect on individuals.

  • Rapid new advances are now underway in AI; Yet, as AI gets more widely deployed, the importance of having explainable models will increase. Simply, if systems are responsible for making a decision, there comes a step in the process whereby that decision has to be shown — communicating what the decision is, how it was made and – now – why did the AI do what it did.

  • Why We Need to Audit Algorithms; James Guszcza Iyad Rahwan Will Bible Manuel Cebrian Vic Katyal; Algorithmic decision-making and artificial intelligence (AI) hold enormous potential and are likely to be economic blockbusters, but we worry that the hype has led many people to overlook the serious problems of introducing algorithms into business and society. Indeed, we see many succumbing to what Microsoft’s Kate Crawford calls “data fundamentalism” — the notion that massive datasets are repositories that yield reliable and objective truths, if only we can extract them using machine learning tools. A more nuanced view is needed. It is by now abundantly clear that, left unchecked, AI algorithms embedded in digital and social technologies can encode societal biases, accelerate the spread of rumors and disinformation, amplify echo chambers of public opinion, hijack our attention, and even impair our mental wellbeing.

  • Taking machine thinking out of the black box; Anne McGovern; Adaptable Interpretable Machine Learning project is redesigning machine learning models so humans can understand what computers are thinking.

  • Explainable AI won’t deliver. Here’s why; Cassie Kozyrkov; Interpretability: you do understand it but it doesn’t work well. Performance: you don’t understand it but it does work well. Why not have both?

  • We Need an FDA For Algorithms; Hannah Fry; Do we need to develop a brand-new intuition about how to interact with algorithms? What do you mean when you say that the best algorithms are the ones that take the human into account at every stage? What is the most dangerous algorithm?

  • Explainable AI, interactivity and HCI; Erik Stolterman Bergqvist; develop AI systems that technically can explain their inner workings in some way that makes sense to people. approach the XAI from a legal point of view. explanable AI is needed for practical reasons, pproach the topic from a more philosophical perspective and ask some broader questions about how reasonable it is for humans to ask systems to be able to explain their actions

  • Why your firm must embrace explainable AI to get ahead of the hype and understand the business logic of AI; Maria Terekhova; If AI is to have true business-ready capabilities, it will only succeed if we can design the business logic behind it. That means business leaders who are steeped in business logic need to be front-and-center in the AI design and management processes.

  • Explainable AI : The margins of accountability; Yaroslav Kuflinski; How much can anyone trust a recommendation from an AI? Increasing the adoption of ethics in artificial intelligence

2017

  • Sent to Prison by a Software Program’s Secret Algorithms; Adam Liptak The new York Times; The report in Mr. Loomis’s case was produced by a product called Compas, sold by Northpointe Inc. It included a series of bar charts that assessed the risk that Mr. Loomis would commit more crimes. The Compas report, a prosecutor told the trial judge, showed “a high risk of violence, high risk of recidivism, high pretrial risk.” The judge agreed, telling Mr. Loomis that “you’re identified, through the Compas assessment, as an individual who is a high risk to the community.”
  • AI Could Resurrect a Racist Housing Policy And why we need transparency to stop it.- "The fact that we can't investigate the COMPAS algorithm is a problem"

2016

  • How We Analyzed the COMPAS Recidivism Algorithm; ProPublica investigation. Black defendants were often predicted to be at a higher risk of recidivism than they actually were. Our analysis found that black defendants who did not recidivate over a two-year period were nearly twice as likely to be misclassified as higher risk compared to their white counterparts (45 percent vs. 23 percent). The analysis also showed that even when controlling for prior crimes, future recidivism, age, and gender, black defendants were 45 percent more likely to be assigned higher risk scores than white defendants.

Theses

2018

2016

Audio

2018

  • Explaining Explainable AI; In this webinar, we will conduct a panel discussion with Patrick Hall and Tom Aliff around the business requirements of explainable AI and the subsequent value that can benefit any organization

  • Approaches to Fairness in Machine Learning with Richard Zemel; Today we continue our exploration of Trust in AI with this interview with Richard Zemel, Professor in the department of Computer Science at the University of Toronto and Research Director at Vector Institute.

  • Making Algorithms Trustworthy with David Spiegelhalter; In this, the second episode of our NeurIPS series, we’re joined by David Spiegelhalter, Chair of Winton Center for Risk and Evidence Communication at Cambridge University and President of the Royal Statistical Society.

Workshops

2018

  • Explainable AI; Ricardo Baeza-Yates; Big Data Congress 2018
  • Trust and explainability: The relationship between humans & AI; Thomas Bolander; The measure of success for AI applications is the value they create for human lives. In that light, they should be designed to enable people to understand AI systems successfully, participate in their use, and build their trust. AI technologies already pervade our lives. As they become a central force in society, the field is shifting from simply building systems that are intelligent to building intelligent systems that are human-aware and trustworthy.
  • 21 fairness definitions and their politics; This tutorial has two goals. The first is to explain the technical definitions. In doing so, I will aim to make explicit the values embedded in each of them. This will help policymakers and others better understand what is truly at stake in debates about fairness criteria (such as individual fairness versus group fairness, or statistical parity versus error-rate equality). It will also help com­puter scientists recognize that the proliferation of definitions is to be celebrated, not shunned, and that the search for one true definition is not a fruitful direction, as technical considerations cannot adjudicate moral debates.
  • Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018)

2017

  • NIPS 2017 Tutorial on Fairness in Machine Learning; Solon Barocas, Moritz Hardt
  • Interpretability for AI safety; Victoria Krakovna; Long-term AI safety, Reliably specifying human preferences and values to advanced AI systems, Setting incentives for AI systems that are aligned with these preferences
  • Debugging machine-learning; Michał Łopuszyński; Model introspection You can answer thy why question, only for very simple models (e.g., linear model, basic decision trees) Sometimes, it is instructive to run such a simple model on your dataset, even though it does not provide top-level performance You can boost your simple model by feeding it with more advanced (non-linearly transformed) features

Other

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