The Counter-Intuitiveness of Fairness in Machine Learning

The Counter-Intuitiveness of Fairness in Machine Learning

In this article, I will review an idea on how to bring fairness to ML. The idea appears to be counter-intuitive at first blush, but as a couple of articles have illustrated, it has a solid statistical and legal grounding.

The idea that what happened in the past can serve as a good predictor of the future is the central tenet behind much of the incredible success of Machine Learning (ML). However, this “past-as-prelude” approach to predicting behavior is increasingly under scrutiny due to its perceived failures relating to bias, discrimination, and fairness. For example, the revelation that the Apple credit card algorithm was giving women less credit than men; the accusation that a widely used software for assessing recidivism risk was discriminating against black defendants. These reports not only grab news headlines, they rile our hardwired sense of fairness¹.

In criminal justice, there is a sustained debate on whether existing anti-discrimination laws are adequate for the oversight of predictive algorithms. In addition, there is a burgeoning research community examining how we can safeguard fairness in predictive algorithms under these laws (e.g. Barocas & SelbstHuq). As recent unrest and protests over systemic discrimination have shown, the consequence of getting it wrong is severe. As more and more of our lives become automated, there is an urgency in accelerating our effort in making this technology acceptable to the society as a whole.

In this article, I will review an idea on how to bring fairness to ML. The idea appears to be counter-intuitive at first blush, but as a couple of articles have illustrated, it has a solid statistical and legal grounding.

This article is intended for anyone with a basic understanding of ML and is interested in how we can work towards implementing fairness in ML.

artificial-intelligence linear-regression fairness bias machine-learning

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