@inproceedings{bcc183f1d7854a41aa7965f3bc9ca3d6,
title = "Explainabilitys gain is optimalitys loss?: How explanations bias decision-making",
abstract = "Decisions in organizations are about evaluating alternatives and choosing the one that would best serve organizational goals. To the extent that the evaluation of alternatives could be formulated as a predictive task with appropriate metrics, machine learning algorithms are increasingly being used to improve the efficiency of the process. Explanations help to facilitate communication between the algorithm and the human decision-maker, making it easier for the latter to interpret and make decisions on the basis of predictions by the former. Feature-based explanations' semantics of causal models, however, induce leakage from the decision-maker's prior beliefs. Our findings from a field experiment demonstrate empirically how this leads to confirmation bias and disparate impact on the decision-maker's confidence in the predictions. Such differences can lead to sub-optimal and biased decision outcomes. ",
keywords = "bias, confirmation bias, decision-making, explanations, human causal reasoning, human-Algorithm communication, semantics of explanations",
author = "Charles Wan and Rodrigo Belo and Leid Zejnilovic",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 5th AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, AIES 2022 ; Conference date: 01-08-2022 Through 03-08-2022",
year = "2022",
month = jul,
day = "26",
doi = "10.1145/3514094.3534156",
language = "English",
series = "AIES 2022 - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society",
publisher = "ACM - Association for Computing Machinery",
pages = "778--787",
booktitle = "AIES 2022 - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society",
address = "United States",
}