Fairness and Explanation in AI-Informed Decision Making

• Published in: Machine learning and knowledge extraction Vol. 4; no. 2; pp. 556 - 579
• Main Authors: Angerschmid, Alessa, Zhou, Jianlong, Theuermann, Kevin, Chen, Fang, Holzinger, Andreas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: AI ethics; AI explanation; AI fairness; Algorithms; Artificial intelligence; Bias; Decision making; Efficiency; Investigations; Machine learning; Medical diagnosis; perception of fairness; Perceptions; Trust
• ISSN: 2504-4990; 2504-4990
• DOI: 10.3390/make4020026

AI-assisted decision-making that impacts individuals raises critical questions about transparency and fairness in artificial intelligence (AI). Much research has highlighted the reciprocal relationships between the transparency/explanation and fairness in AI-assisted decision-making. Thus, considering their impact on user trust or perceived fairness simultaneously benefits responsible use of socio-technical AI systems, but currently receives little attention. In this paper, we investigate the effects of AI explanations and fairness on human-AI trust and perceived fairness, respectively, in specific AI-based decision-making scenarios. A user study simulating AI-assisted decision-making in two health insurance and medical treatment decision-making scenarios provided important insights. Due to the global pandemic and restrictions thereof, the user studies were conducted as online surveys. From the participant’s trust perspective, fairness was found to affect user trust only under the condition of a low fairness level, with the low fairness level reducing user trust. However, adding explanations helped users increase their trust in AI-assisted decision-making. From the perspective of perceived fairness, our work found that low levels of introduced fairness decreased users’ perceptions of fairness, while high levels of introduced fairness increased users’ perceptions of fairness. The addition of explanations definitely increased the perception of fairness. Furthermore, we found that application scenarios influenced trust and perceptions of fairness. The results show that the use of AI explanations and fairness statements in AI applications is complex: we need to consider not only the type of explanations and the degree of fairness introduced, but also the scenarios in which AI-assisted decision-making is used.