Machine Learning Bias

Measuring Bias in a Dataset you own this product

This project is part of the liveProject series Mitigate Machine Learning Bias in Mortgage Lending Data
prerequisites
beginner Python, Jupyter Notebook, and pandas • basic scikit-learn • basics of machine learning
skills learned
setting up a Google Colab environment • preprocessing a dataset using one-hot encoding • charting and visualizing metrics
Adriano Soares Koshiyama, Umar Mohammed, Emre Kazim, and Catherine Inness
1 week · 5-9 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series Mitigate Machine Learning Bias in Mortgage Lending Data liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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Look inside
In this liveProject, you’ll compute and chart metrics that show the key characteristics in Home Mortgage Disclosure Act (HMDA) dataset, and investigate relationships between key demographics and other features that may lead to a biased machine learning model. You’ll compute and chart “equality of outcome” bias metrics, and produce a report into your insights.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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

you will learn

In this liveProject, you’ll learn to assess your training dataset for bias and identify any patterns or issues that may be unfairly prejudiced against protected characteristics.

  • Setting up a Google Colab environment to run Python code in a Jupyter Notebook.
  • Loading a pickled dataset into Google Colab.
  • Preprocessing a dataset using one-hot encoding.
  • Using the pandas library to compute metrics.
  • Using the seaborn library to produce charts visualizing the metrics.

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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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