Fairness in AI

Add to your list(s) Download to your calendar using vCal

• Elre Oldewage (University of Cambridge)
• Wednesday 17 February 2021, 11:00-12:30
• https://eng-cam.zoom.us/j/86068703738?pwd=YnFleXFQOE1qR1h6Vmtwbno0LzFHdz09.

If you have a question about this talk, please contact Elre Oldewage.

Applications of machine learning almost always involve data about people. Unfortunately, training data for such applications usually encode the demographic disparities that are present (or have historically been present) in our society. This, in turn, may lead to machine learning solutions that re-enforce discrimination—not necessarily on purpose, but due to bias in the training data. As machine learning researchers, the solution may seem simple: get better, unbiased data. However, as we will discuss, bias is nigh impossible to escape completely. Instead, we must develop techniques to identify and counter biased decision-making by ML models.

In this talk, we will provide motivational examples and discuss a number of sources from which bias and discrimination may arise. We will consider different definitions for fairness, eventually boiling them down to three main criteria: independence, separation and sufficiency. We’ll also discuss a number of bias mitigation strategies, by which these criteria may be achieved. Finally, we’ll discuss the limitations of the three observational criteria and briefly discuss fairness from a causal perspective.

Recommended reading:

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. A Survey on Bias and Fairness in Machine Learning. arXiv e-prints, art. arXiv:1908.09635, 2019. Available at: https://arxiv.org/abs/1908.09635

Additional reading, for those interested in the topic, is the Fair ML Book, available for free online:

Barocas, S., Hardt, M., and Narayanan, A. Fairness and Machine Learning. fairmlbook.org, 2019. Available at: http://www.fairmlbook.org

This talk is part of the Machine Learning Reading Group @ CUED series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge University Engineering Department Talks
• Cambridge talks
• Centre for Smart Infrastructure & Construction
• Chris Davis' list
• Computational Continuum Mechanics Group Seminars
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Journal Clubs
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• ML
• Machine Learning Reading Group
• Machine Learning Reading Group @ CUED
• Machine Learning Summary
• Quantum Matter Journal Club
• Required lists for MLG
• School of Technology
• Simon Baker's List
• TQS Journal Clubs
• Trust & Technology Initiative - interesting events
• bld31
• custom
• https://eng-cam.zoom.us/j/86068703738?pwd=YnFleXFQOE1qR1h6Vmtwbno0LzFHdz09
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.