The Issue of Bias. The Framing Powers of Machine Learning
Marcello Pelillo, Teresa Scantamburlo (eds.), Machine We Trust. Perspectives on Dependable AI, MIT Press 2021, https://mitpress.mit.edu/books/machines-we-trust
17 Pages Posted: 19 Dec 2019 Last revised: 13 Sep 2021
Date Written: December 3, 2019
From a computer science perspective, bias may refer to the productive bias that enables ML, both at the level of picking the training set and at the level of training the algorithms. It reminds one of David Wolpert’s ‘no free lunch theorem’, if not of Humean scepticism or Gadamer’s acknowledgment of constitutive presumptions. This e.g. relates to the trade-off between the size of a training set, its relevance, the types of algorithms used, and the accuracy and/or speed of the results. From a societal perspective, bias may refer to unfair treatment or even unlawful discrimination. It is crucial to distinguish inherent computational bias from the unwarranted impact of unfair or wrongful bias, while teasing out where they meet and how they interact. This includes an inquiry into the ethical assessments of ML bias, based on the fact that ML applications are reconfiguring the ‘choice architectures’ of our online and offline environments. Finally, I will briefly argue that ethics will not do when confronting the framing powers of machine bias, highlighting the need to bring the design choice that determine these framing powers under the Rule of Law.
Keywords: Inductive bias, framing powers, no-free-lunch theorem, choice architecture, ethics, bounded rationality, Rule of Law
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