Imagine a scenario, you visit a bank for a loan of 1 million dollars. The Loan officer is aided by an AI-powered system which predicts or recommends if you are eligible for a loan or not and how much. The AI system recommended that you are not eligible for loan. Here are couple of questions to think upon,
- Will you as a customer be satisfied with the service?
- Won't you want a proper justification for the decision taken by AI?
- Won't the loan officer double check the decision taken by AI and for that he or she would want to know the underlying mechanism of the AI model?
- Should the banks completely trust and rely on AI-powered systems?
After pondering upon these questions, you will agree that 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.
There are multiple reasons why we need to understand the underlying mechanism of the Machine learning Models -
- Human Readability.
- Bias Mitigation.
- Justifiability.
- Interpretability.
**AI Explainability 360, a comprehensive open source toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.**
This Code Pattern highlights the use of the AI explainability 360 toolkits to demystify the decisions taken by the machine learning model to gain better insights and explainability which not only help the policy-makers, data scientists to develop trusted explainable AI applications but also the general public for transparency and allowing them to gain insight into the machine’s decision-making process. Understanding behind the scenes is essential to fostering trust and confidence in AI systems.
To demonstrate the use of the AI Explainability 360 Toolkit, we are using the existing Fraud Detection Code Pattern showcasing, explaining, and also guide the practitioner on choosing an appropriate explanation method or algorithm depending upon the type of customer(Data Scientist, General Public, SME, Policy Maker) that needs an explanation of the model.
This Code Pattern will also demonstrate the use of ART(Adversarial Robustness 360 Toolkit) to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference.
No single approach to explaining algorithms
Currently, AI explainability 360 toolkit provides eight state-of-the-art explainability algorithms that can add transparency throughout AI systems. Depending on the requirement and subjected to the problem statement you can choose them appropriately. The algorithms are explained in detail on this link.)
In this Code Pattern, we will demonstrate the working of the three explainability Algorithms:
- Contrastive Explanations Method (CEM) algorithm available in AI Explainability 360.
- AI Explainability 360—ProtoDash—works with an existing predictive model to show how the customer compares to others who have similar profiles and had similar repayment records to the model's prediction for the current customer, which helps to evaluate and predict the applicant’s risk. Based on the model’s prediction and the explanation for how it came to that recommendation, the Loan Officer can make a more informed decision.
- Generalized Linear Rule Model (GLRM) algorithm in the AI Explainability 360 Toolkit, which provides an enhanced level of explainability to a Data Scientist if the model can be deployed or not.
Architecture:
Flow:
- Log in to Watson Studio powered by spark, initiate Cloud Object Storage, and create a project.
- Upload the .csv data file to Object Storage.
- Load the Data File in Watson Studio Notebook.
- Install AI Explainability 360 Toolkit and Adversarial Robustness Toolbox in the Watson Studio Notebook.
- Visualization for explainability and interpretability of AI Model for the three different types of Users.
-
IBM Watson Studio: Analyze data using RStudio, Jupyter, and Python in a configured, collaborative environment that includes IBM value-adds, such as managed Spark.
-
IBM AI Explainability 360 : The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics.
-
IBM Adverserial Robustness : Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference.
-
IBM Cloud Object Storage: An IBM Cloud service that provides an unstructured cloud data store to build and deliver cost effective apps and services with high reliability and fast speed to market. This code pattern uses Cloud Object Storage.
- Artificial Intelligence: Any system which can mimic cognitive functions that humans associate with the human mind, such as learning and problem solving.
- Data Science: Systems and scientific methods to analyze structured and unstructured data in order to extract knowledge and insights.
- Analytics: Analytics delivers the value of data for the enterprise.
- Python: Python is a programming language that lets you work more quickly and integrate your systems more effectively.
Steps:
Sign up for IBM Cloud. By clicking on create a free account you will get 30 days trial account.
Sign up for IBM's Watson Studio.
Click on New Project and select per below.
Define the project by giving a Name and hit 'Create'.
Clone this repo
Navigate to data and save the file on the disk. Review the data glossary from the data folder for more details. Note: Citation is needed to use this dataset for any other projects.
Click on Assets and select Browse and add the csv file from your file system.
Follow the below steps to use Jupyter Notebook for building the model.
Create an account with IBM Cloud and then create a project in Watson Studio. Add the data as an asset. These three steps are given above in detail.
You will have to create three notebooks. Below, is the procedure to create one. Repeat these steps to create the other two.
- Open IBM Watson Studio.
- Go to the project and click on Add
- Click on
Create notebookto create a notebook. - Select the
From URLtab. - Enter a name for the notebook.
- Optionally, enter a description for the notebook.
- Enter this Notebook URL
- Select the runtime (8 vCPU and 32GB RAM)
- Click the
Createbutton.
After the notebook is imported, click on Not Trusted and select the option as Yes to trust the source of the notebook.
This notebook has been created to demonstrate the steps for building the model using Watson Studio platform. For other usecases, the notebook has to be created from scratch.
Click on 0010 icon at the top right side which will bring up the data assets tab.
Click on Insert to code dropdown and select the option Insert Pandas Dataframe.
When a notebook is executed, what is actually happening is that each code cell in the notebook is executed, in order, from top to bottom.
Each code cell is selectable and is preceded by a tag in the left margin. The tag
format is In [x]:. Depending on the state of the notebook, the x can be:
- A blank, this indicates that the cell has never been executed.
- A number, this number represents the relative order this code step was executed.
- A
*, this indicates that the cell is currently executing.
There are several ways to execute the code cells in your notebook:
- One cell at a time.
- Select the cell, and then press the
Playbutton in the toolbar.
- Select the cell, and then press the
- Batch mode, in sequential order.
- From the
Cellmenu bar, there are several options available. For example, you canRun Allcells in your notebook, or you canRun All Below, that will start executing from the first cell under the currently selected cell, and then continue executing all cells that follow.
- From the
All the three notebooks have given detailed explanation of the Algorithms and demonstrated their use on Fraud-Data.
- In the notebook
Fraud-AI_Protodash_&_CE.ipynb
This gives the profile of the instances similar to each other who have no fraud risk to the loan office.
- In the notebook
Fraud-AI_Protodash_&_CE.ipynb
The above results show that the customer should have 'less loan Amount', 'Loan_Term' for it to classified as No-Fraud-risk.
- In the notebook
booleanCG_fraud.ipynb
3) Unveiling Fraud Detection AI Model for Data Scientist using Boolean Rule Column Generation explainer
The results shows the rules identified by the model in the data to a data Scientist.
- In the notebook
Fraud_ART_Robustness.ipynb.ipynb
This notebook show how to generates the adversarial training data using Adversarial-Robustness-Toolbox. This will prepare the model against adversarial attacks so it doesn't misclassify and is able to distinguish noise from the real data.
The dataset which is referenced in this code pattern is created and owned by R.K.Sharath Kumar, Data Scientist, IBM India Software Labs.
This code pattern is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Developer Certificate of Origin, Version 1.1 (DCO) and the Apache Software License, Version 2.
Check the ASL FAQ link for more details