Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers. While the emerging field of Explainable Artificial Intelligence (XAI) has mainly focused on developing algorithms that generate such explanations, there has not yet been sufficient consideration of customers' preferences for various types and formats of explanations. We discuss theoretically and study empirically people's preferences for explanations of algorithmic decisions. We focus on three main attributes that describe automatically-generated explanations from existing XAI algorithms (format, complexity, and specificity), and capture differences across contexts (online targeted advertising vs. loan applications) as well as heterogeneity in users' cognitive styles. Despite their popularity among academics, we find that counterfactual explanations are not popular among users, unless they follow a negative outcome (e.g., loan application was denied). We also find that users are willing to tolerate some complexity in explanations. Finally, our results suggest that preferences for specific (vs. more abstract) explanations are related to the level at which the decision is construed by the user, and to the deliberateness of the user's cognitive style.
Do Consumers Accept AIs as Moral Compliance Agents?
Faculty of Business and Economics, University of Melbourne（商学院与经济学院，墨尔本大学）；University of Melbourne（墨尔本大学）；University of Melbourne, Parkville（墨尔本大学，帕克维尔）
Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence
Guangdong College of Commerce（广东商学院）；University of New South Wales（新南威尔士大学）；Swinburne University of Technology（斯威本科技大学）；Australian National University（澳大利亚国立大学）；Northeastern University（东北大学）；Cornell University（康奈尔大学）
When Bias Backfires: The Modulatory Role of Counterfactual Explanations on the Adoption of Algorithmic Bias in XAI-Supported Human Decision-Making
Machine Learning Group, Bielefeld University（比勒菲尔德大学机器学习组）；Research Center Trustworthy Data Science and Security of the University Alliance Ruhr, Faculty of Informatics, Technical University Dortmund（鲁尔大学联盟可信数据科学与安全研究所以及多特蒙德技术大学信息学院）
