To Trust or to Think
By Zana Buçinca, Maja Malaya, Krzysztof Z Gajos. Proc. ACM Hum. Comput. Interact., 2021
682 citations recorded by the source corpus.
People supported by AI-powered decision support tools frequently overrely on the AI: they accept an AI's suggestion even when that suggestion is wrong. Adding explanations to the AI decisions does not appear to reduce the overreliance and some studies suggest that it might even increase it. Informed by the dual-process theory of cognition, we posit that people rarely engage analytically with each individual AI recommendation and explanation, and instead develop general heuristics about whether and when to follow the AI suggestions. Building on prior research on medical decision-making, we designed three cognitive forcing interventions to compel people to engage more thoughtfully with the AI-generated explanations. We conducted an experiment (N=199), in which we compared our three cognitive forcing designs to two simple explainable AI approaches and to a no-AI baseline. The results demonstrate that cognitive forcing significantly reduced overreliance compared to the simple explainable AI approaches. However, there was a trade-off: people assigned the least favorable subjective ratings to the designs that reduced the overreliance the most. To audit our work for intervention-generated inequalities, we investigated whether our interventions benefited equally people with different levels of Need for Cognition (i.e., motivation to engage in effortful mental activities). Our results show that, on average, cognitive forcing interventions benefited participants higher in Need for Cognition more. Our research suggests that human cognitive motivation moderates the effectiveness of explainable AI solutions.
Subfields: Artificial Intelligence, Family Practice, General Decision Sciences
View Paper: https://www.semanticscholar.org/paper/65772de56676a6a437c8abaeccef34224f62ee13
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Citations in the corpus
- The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review (2024, background): “Students can inadvertently become overly dependent on AI-generated assistance, potentially detracting from their ability to make independent, well-informed decisions (Buçinca et al., 2021).”
- Trust in AI: progress, challenges, and future directions (2024, neutral): “People often over-rely on arti fi cial intelligence systems, trusting them too much (Buçinca et al., 2021).”