Mitigating Bias with Postprocessing you own this product

prerequisites
beginner Python, Jupyter Notebook, and pandas • basic scikit-learn • basics of machine learning
skills learned
setting up a Google Colab environment • AIF360 open source library • post-processing for fairness with the “equalized odds'' method
Adriano Soares Koshiyama, Umar Mohammed, Emre Kazim, and Catherine Inness
1 week · 5-9 hours per week · INTERMEDIATE
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Wonderful project to work on. Author may explain about mortgage problems, but it applies to most machine learning problems.

Dinesh Ghanta, Senior Data Scientist, Oracle
Look inside
In this liveProject, you’ll use the open source AI Fairness 360 toolkit from IBM and the “equalized odds post-processing” method to post-process your model for the purpose of bias mitigation. “Equalized odds post-processing” seeks to mitigate bias by making modifications to the prediction label after a model has been trained. After charting bias metrics on a basic classifier, you’ll tune the classification threshold to explore the impact on revealed biases.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

I definitely learned some useful tools, such as AIF360. I will definitely be using it in the near future.

Khaing Win, Data Science Consultant, Amazon Web Services

project authors

Adriano Koshiyama
Adriano Koshiyama is a Research Fellow in Computer Science at University College London, and the co-founder of Holistic AI, a start-up focused on providing advisory, auditing and assurance of AI systems. He helps manage TheAlgo Conferences (thealgo.co) and is the main investigator at UCL Algorithm Standards and Technology Lab. Academically, he has published more than 30 papers in international conferences and journals.
Catherine Inness
Catherine Inness is a senior manager in Accenture's Data Science practice in the UK. She has more than ten years of experience in technology, focused on the public sector. She holds an MSc in data science and machine learning from University College London, where her research focused on algorithm fairness.
Umar Mohammed
Umar Mohammed is the lead developer at Holistic AI. He has over 10 years of experience writing software and is currently focused on creating debiasing tools for AI systems. He holds an MSc in vision imaging and virtual environments from University College London where his research focused on face recognition. He has published papers on computer vision in international conferences.
Emre Kazim
Emre Kazim is a research fellow in the computer science department of University College London, working in the field of AI ethics. His current focus is on governance, policy, and auditing of AI systems, including algorithm interpretability and certification. Emre has a PhD in philosophy.

prerequisites

The liveProject is for beginner data scientists and software engineers looking to tackle the basic principles of measuring and mitigating ML bias. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basic Python
  • Basic Jupyter Notebook
  • Basic pandas
  • Basic scikit-learn
  • Basic seaborn
TECHNIQUES
  • Basic machine learning

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