Human Decision Making with Machine Assistance

By Nina Grgic-Hlaca, Christoph Engel, Krishna P. Gummadi. 2019

113 citations recorded by the source corpus.

Much of political debate focuses on the concern that machines might take over. Yet in many domains it is much more plausible that the ultimate choice and responsibility remain with a human decision-maker, but that she is provided with machine advice. A quintessential illustration is the decision of a judge to bail or jail a defendant. In multiple jurisdictions in the US, judges have access to a machine prediction about a defendant's recidivism risk. In our study, we explore how receiving machine advice influences people's bail decisions. We run a vignette experiment with laypersons whom we test on a subsample of cases from the database of this prediction tool. In study 1, we ask them to predict whether defendants will recidivate before tried, and manipulate whether they have access to machine advice. We find that receiving machine advice has a small effect, which is biased in the direction of predicting no recidivism. In the field, human decision makers sometimes have a chance, after the fact, to learn whether the machine has given good advice. In study 2, after each trial we inform participants of ground truth. This does not make it more likely that they follow the advice, despite the fact that the machine is (on average) slightly more accurate than real judges. This also holds if initially the advice is mostly correct, or if it initially is mostly to predict (no) recidivism. Real judges know that their decisions affect defendants' lives. They may also be concerned about reelection or promotion. Hence a lot is at stake. In study 3 we emulate high stakes by giving participants a financial incentive. An incentive to find the ground truth, or to avoid false positive or false negatives, does not make participants more sensitive to machine advice. But an incentive to follow the advice is effective.

Subfields: Economics and Econometrics, Political Science and International Relations, Safety Research

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Citations in the corpus

Manipulating and Measuring Model Interpretability (2018, background): “[40] Nina Grgić-Hlača, Christoph Engel, and Krishna P Gummadi.”
Human-AI Interactions in Public Sector Decision-Making:"Automation Bias"and"Selective Adherence"to Algorithmic Advice (2021, background): “These findings are further supported by a series of experimental studies among laypersons (Green and Chen 2019a, 2019b; Grgić-Hlača, Engel, and Gummadi 2019).”
Human-Centered Approaches to Fair and Responsible AI (2020, background): “An emerging line of work seeks to systematically study human perceptions of algorithmic fairness [17, 24, 14], explain algorithmic decisions to promote trust and a sense of fairness [7, 10, 19], understand human use of algorithmic decisions [13, 12, 23], and develop methods to incorporate them into ”
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies (2021, background): “, how much people’s decisions rely on or are influenced by the AI’s), such as acceptance of model suggestions [13, 16, 22, 26, 35, 80, 81, 90, 94, 96, 101, 143, 152, 153, 155], likelihood to switch [58, 96, 101, 109, 152, 155], weight of model advice [95, 111], choice to use the”
Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making (2021, methodology): “This dataset was widely used by researchers to understand how people interact with machine assistance in their decision making [23, 31].”