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.
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
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.
features
- Self-paced
- 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.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.