Four-Project Series

Mitigate Machine Learning Bias in Mortgage Lending Data you own this product

beginner Python, Jupyter Notebook, and pandas • basic scikit-learn • basics of machine learning
skills learned
charting and visualizing metrics • computing bias performance metrics on a logistic regression classifier • preprocessing with the “reweighing subjects” method • post-processing with the “equalized odds” method
Adriano Soares Koshiyama, Umar Mohammed, Emre Kazim, and Catherine Inness
4 weeks · 5-9 hours per week average · INTERMEDIATE

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In this series of liveProjects, you’ll apply techniques for measuring and mitigating bias in a machine learning algorithm. You’ll step into the role of a data scientist for a bank, and investigate the potential biases that arise when automated decision-making is applied to your company’s mortgage offers—in particular, whether your algorithm is biased by gender. Each project in this series covers a different aspect of fairness measurement and intervention, including exploring a dataset with a focus on fairness, and mitigating bias in a logistic regression model.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

here's what's included

Project 1 Measuring Bias in a Dataset
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.
Project 2 Measuring Bias in a Model
In this liveProject, you’ll investigate and report on whether your company’s mortgage application classifier is making fair decisions between male and female applicants. You’ll train a logistic regression classifier on the HMDA dataset, compute performance metrics with a logistic regression classifier, and then chart “equality of opportunity” bias metrics.
Project 3 Mitigating Bias with Preprocessing
In this liveProject, you’ll use the open-source AI Fairness 360 toolkit from IBM to measure and mitigate bias with model preprocessing. You will chart bias metrics on a basic classifier, before preprocessing your training data with the “reweighing” method. Reweighing seeks to mitigate bias by making modifications on the training data by computing and applying a set of weights. The weights are calculated such that the training data, with weights applied, is free of discrimination with respect to a protected attribute. Once your model is preprocessed, you’ll construct a classifier that makes use of the data.
Project 4 Mitigating Bias with Postprocessing
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.

book resources

When you start each of the projects in this series, you'll get full access to the following book for 90 days.

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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 ( 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.


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 the following:

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


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
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.