Decision-makers Processing of AI Algorithmic Advice: Automation Bias versus Selective Adherence

• URL: http://arxiv.org/abs/2103.02381v1
• Date: Wed, 3 Mar 2021 13:10:50 GMT
• Title: Decision-makers Processing of AI Algorithmic Advice: Automation Bias versus Selective Adherence
• Authors: Saar Alon-Barkat and Madalina Busuioc
• Abstract summary: Key concern is that human overreliance on algorithms introduces new biases in the human-algorithm interaction. A second concern regards decision-makers inclination to selectively adopt algorithmic advice when it matches their pre-existing beliefs and stereotypes. We assess these via two studies simulating the use of algorithmic advice in decisions pertaining to the employment of school teachers in the Netherlands. Our findings of selective, biased adherence belie the promise of neutrality that has propelled algorithm use in the public sector.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Artificial intelligence algorithms are increasingly adopted as decisional aides by public organisations, with the promise of overcoming biases of human decision-makers. At the same time, the use of algorithms may introduce new biases in the human-algorithm interaction. A key concern emerging from psychology studies regards human overreliance on algorithmic advice even in the face of warning signals and contradictory information from other sources (automation bias). A second concern regards decision-makers inclination to selectively adopt algorithmic advice when it matches their pre-existing beliefs and stereotypes (selective adherence). To date, we lack rigorous empirical evidence about the prevalence of these biases in a public sector context. We assess these via two pre-registered experimental studies (N=1,509), simulating the use of algorithmic advice in decisions pertaining to the employment of school teachers in the Netherlands. In study 1, we test automation bias by exploring participants adherence to a prediction of teachers performance, which contradicts additional evidence, while comparing between two types of predictions: algorithmic v. human-expert. We do not find evidence for automation bias. In study 2, we replicate these findings, and we also test selective adherence by manipulating the teachers ethnic background. We find a propensity for adherence when the advice predicts low performance for a teacher of a negatively stereotyped ethnic minority, with no significant differences between algorithmic and human advice. Overall, our findings of selective, biased adherence belie the promise of neutrality that has propelled algorithm use in the public sector.

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