What About Applied Fairness?

By Jared Sylvester, Edward Raff. 2018

11 citations recorded by the source corpus.

Machine learning practitioners are often ambivalent about the ethical aspects of their products. We believe anything that gets us from that current state to one in which our systems are achieving some degree of fairness is an improvement that should be welcomed. This is true even when that progress does not get us 100% of the way to the goal of "complete" fairness or perfectly align with our personal belief on which measure of fairness is used. Some measure of fairness being built would still put us in a better position than the status quo. Impediments to getting fairness and ethical concerns applied in real applications, whether they are abstruse philosophical debates or technical overhead such as the introduction of ever more hyper-parameters, should be avoided. In this paper we further elaborate on our argument for this viewpoint and its importance.

Subfields: Artificial Intelligence, Safety Research

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Citations in the corpus

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation (2021, background): “The additional computational cost of training models, as well as the introduction of additional hyperparameters for fair learning pose signiﬁcant hindrances towards the adoption of fair models (Sylvester and Raff 2018).”
The false promise of risk assessments: epistemic reform and the limits of fairness (2020, background): “similarly pointed to the incompatibility of fairness metrics to dispel claims that algorithms are discriminatory [11, 28, 155].”