The purpose of this course is to equip you with the technical and conceptual tools to be able to engage deeply and critically with machine learning research and practice. The course strives to integrate historical and cultural context with contemporary scholarship on Fairness, Accountability, and Transparency in Machine Learning (FATML). The basic strategy is to ground the course in issues identified by FATML/”FAT*” computer science research and build practical knowledge through programming exercises replicating results from this literature.

The lectures will primarily provide context, while the formative assessments scaffold technical understanding and provide opportunities to critically reflect. The course will be taught from within a framework of anti-racism, intersectional feminism, and anti-oppression, focusing on US and UK contexts.


  • Historical context of modern machine learning
  • Race and machine learning
  • Gender and machine learning
  • Technical approaches to fairness
  • Radical reimaginings of machine learning
  • Tools for examining and communicating
  • Organizing work and social movements

Learning Outcomes

At the end of this course students will:

  • have a technical understanding of sources of bias and discrimination in machine learning.
  • have a conceptual understanding of how issues such as bias and discrimination in machine learning are linked to broader structures of power in society.
  • know how to evaluate interventions for improving fairness, accountability, and transparency.
  • be equipped with tools for imagining new, radical interventions in machine learning.
This page was last modified on 18 October 2019