Four out of eight chapters of our book on fairness and machine learning are now available—halfway there! The newly released chapter is on testing systems for discrimination.. fairmlbook.org/testing.html
A few thoughts in this thread on why you might find this interesting and useful.
A few thoughts in this thread on why you might find this interesting and useful.
Algorithmic auditing has exploded in the last 5 years (there are about 50 studies in our references). This young community has a lot to learn from the classic audit studies that date back to the 70s. The entire first half of the chapter is on traditional tests for discrimination.
The algorithmic bias conversation needs to move beyond simple measures of disparity. We help the reader connect these measurements to moral notions of fairness, understand the harms that can result, and identify the sources and mechanisms that produce disparities.
Algorithmic fairness is a small part of a broader ongoing debate on justice, rights, power, and accountability. The book is intentionally narrow in scope; we try to make clear the limits of the "fairness" frame and provide pointers to further reading.
The chapter is opinionated, like the rest of the book. We hope you find the survey useful and our perspective thought-provoking. It is a work in progress and we’re grateful for the interest and feedback we’ve received so far. Write to us any time at contact@fairmlbook.org.
