Building a secure and transparent ML pipeline using open source technologies

The application of AI algorithms in domains such as criminal justice, credit scoring, and hiring holds unlimited promise. At the same time, it raises legitimate concerns about algorithmic fairness. There’s a growing demand for fairness, accountability, and transparency from ML systems. And we need to remember that training data isn’t the only source of possible bias and adversarial contamination. It can also be introduced through inappropriate data handling, inappropriate model selection, or incorrect algorithm design. We need a pipeline that’s open, transparent, secure, and fair, and that fully integrates into the AI lifecycle. Such a pipeline requires a robust set of bias and adversarial checkers, debiasing and defense algorithms, and explanations.

Animesh Singh, Svetlana Levitan, Tommy Li, Romeo Kienzler demonstrate how to build an ML pipeline that’s open, secure, and fair and that fully integrates into the AI lifecycle, using open source tools like AIF360, ART, Kubeflow, Model Asset Exchange (MAX) etc.

Key Open Source Projects Used:

• AI Fairness 360: https://github.com/IBM/AIF360
• Adversarial Robustness Toolbox: https://github.com/IBM/adversarial-robustness-toolbox
• Model Asset Exchange: https://developer.ibm.com/exchanges/models/
• KubeFlow Pipelines: https://github.com/kubeflow/pipelines
• Kubeflow Serving: https://github.com/kubeflow/kfserving

Get Started

If you have a notebook server, you can run notebooks locally for AIF360 and ART. For running the Pipelines notebook, you will need to sign up on IBM Cloud, as it requires a Cloud environment and Object Storage access.

• Sign up for free IBM Cloud account: http://ibm.biz/oscon2019
• Watson Studio: https://dataplatform.cloud.ibm.com/

Instructions

1. Create a Jupyter notebook on Watson Studio or on the local machine.

To start with this tutorial, we need to first get an instance of Jupyter notebook running either on the Cloud using Watson Studio or on the local machine.

• To set it up on Watson Studio, visit the Watson Studio tutorial and follow all the steps to learn about how to create a Notebook on Watson Studio and use it to train with the MNIST PyTorch Model.

• To run it on local Machine, clone this repository and start a local Jupyter Server.

git clone https://github.com/IBM/Trusted-ML-Pipelines.git
cd Trusted-ML-Pipelines
pip install -r requirements.txt
jupyter notebook

2. Run the AIF360, ART, ML Pipeline Notebooks

We will be going over the following three Jupyter notebooks. The first two notebooks will go over the concepts behind Adversarial Robustness Toolbox (ART) and AI Fairness 360 (AIF360). Then the last notebook will create a Machine Learning Pipeline using the open source project, KubeFlow Pipeline, to leverage the concepts we learned from the previous two notebooks as an end to end pipeline scenario.

If you are using Watson Studio, we recommend to use runtime Default Python 3.6 S (4 vCPU and 16 GB RAM).

2.1. AIF360: Gender Classification

This notebook will train a Gender Classification model using PyTorch. Then it will showcase how AIF360 can detect the bias from the dataset and mitigate it in the dataset using a preprocessing algorithm called reweighting.

If you are using Watson Studio, you can load this notebook with the below URL link.

https://raw.githubusercontent.com/IBM/Trusted-ML-Pipelines/master/notebooks/tutorial_gender_classification.ipynb

2.2. ART: MNIST Adversarial training

This notebook will use an attack method called fast gradient attack to generate adversarial samples. Then go over some concepts of Adversarial training and showcase how this kind of training can better persist attacks from adversarial samples.

If you are using Watson Studio, you can load this notebook with the below URL link.

https://raw.githubusercontent.com/IBM/Trusted-ML-Pipelines/master/notebooks/adversarial-training-mnist-WS.ipynb

To run locally use the notebook adversarial-training-mnist.ipynb

2.3. End to End ML Pipeline with Gender Classification (using WML, AIF360, ART, Kubeflow Pipeline and Kubeflow Serving)

In this notebook we will create an End to End Machine Learning Pipeline that preprocesses the data and trains the PyTorch Gender Classification model. Then, it applies the fast gradient attack and AIF360 bias detection that generate metrics for this model to evaluate its robustness and fairness. Finally, it deploys the model using KFServing which leverages Knative in the background to serve models in a serverless environment.

You can load this notebook into Watson Studio with the below URL link.

https://raw.githubusercontent.com/IBM/Trusted-ML-Pipelines/master/notebooks/ml-pipeline.ipynb

3. (Optional) Advanced deep learning models for app developers

If you are interested in how to create and use open source deep learning models for app developers, you can use the below workshop to learn more about how to wrap models into container, using TensorFlowJS and NodeRed. https://github.com/CODAIT/max-workshop-oscon-2019

Due to the limitation of network connectivity on the workshop, you can use the below link to try out the object-dector models API. http://max-object-detector.max.us-south.containers.appdomain.cloud/