I am pleased to share the final published version of my short essay with Yuki Matsumi. It was written for a symposium in Fordham Law Review.
AI, Algorithms, and Awful Humans
92 Fordham L. Rev. 1923 (2024)
Mini Abstract:
This Essay critiques arguments that algorithmic decision-making is better than human decision-making. Two arguments are often advanced to justify the increasing use of algorithms in decisions. The “Awful Human Argument” asserts that human decision-making is often awful and that machines can decide better than humans. Another argument, the “Better Together Argument,” posits that machines can augment and improve human decision-making. We argue that such contentions are far too optimistic and fail to appreciate the shortcomings of machine decisions and the difficulties in combining human and machine decision-making. Automated decisions often rely too much on quantifiable data to the exclusion of qualitative data, resulting in a change to the nature of the decision itself. Whereas certain matters might be readily reducible to quantifiable data, such as the weather, human lives are far more complex. Human and machine decision-making often do not mix well. Humans often perform badly when reviewing algorithmic output.
Download the piece for free here:
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This post was authored by Professor Daniel J. Solove, who through TeachPrivacy develops computer-based privacy and data security training.
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