Reading List for Fairness in AI Topics
Papers, books, and resources to learn about fairness in vision, NLP, and more
Published in
4 min readJun 25, 2020
Recent discussion in the machine learning community has brought to light the importance and necessity of understanding not just machine learning, but all the considerations of bias and fairness behind every algorithm’s usage.
“This isn’t a call for ‘diversity’ in datasets or ‘improved accuracy’ in performance — it’s a call for a fundamental reconsideration of the institutions and individuals that design, develop, deploy this tech in the first place.” — Vidushi Marda
For newcomers to this field of fairness in AI, here is a compilation of helpful papers, books, and resources for learning more about the field and specific applications. This list is divided into the following categories:
- Fairness + Computer Vision
- Fairness + Natural Language Processing (NLP)
- Algorithmic Fairness (Theoretical Underpinnings)
- Books (Intended for All Audiences)
- Survey Papers
