Worse Than Human?

Derek E. Bambauer & Michael Risch

The rise of algorithm-driven decision making enabled by Big Data has generated widespread concern among legal scholars. However, few critics have considered data on people‚Äôs existing preferences about the role of algorithms in decision systems. This Article uses empirical analysis of a novel, large dataset of consumer  surveys to elucidate those preferences. The surveys explore whether people prefer to have an algorithm or a human determine an outcome affecting their welfare in a range of representative scenarios with varying stakes. The Article examines how preferences change when one type of decisionmaker produces results that are more accurate, faster, cheaper, or that incorporate private personal information. And it analyzes anchoring effects from the initial assignment of a decisionmaker, along with interactions among these variables, to test how malleable views about algorithms are.

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