人工智能决策的公平感知

Fairness perceptions of artificial intelligence decision-making

不平等问题是全球社会和经济发展需要应对的首要挑战,也是实现全球可持续发展目标的核心障碍。人工智能(artificial intelligence, AI)为缓解不平等、促进社会公平提供了新的途径。然而,新近研究发现,即使客观上AI决策具有公平性和准确性,个体仍可能对AI决策的公平感知较低。因此,近年来越来越多的研究开始关注AI决策公平感知的影响因素。然而,目前研究较为分散,呈现出研究范式不统一、理论不清晰和机制未厘清等特征。这既不利于跨学科的研究对话,也不利于研究者和实践者对AI决策公平感知形成系统性理解。基于此,通过系统的梳理,现有研究可以划分为两类:(1) AI单一决策的公平感知研究,主要聚焦于AI特征和个体特征如何影响个体对AI决策的公平感知;(2) AI-人类二元决策的公平感知研究,主要聚焦于对比个体对AI决策与人类决策公平感知的差异。在上述梳理基础上,未来研究可以进一步探索AI决策公平感知的情绪影响机制等方向。

Inequality is the biggest challenge for global social and economic development,which has the potential to impede the goal of global sustainable development.One way to reduce such inequality is to use artificial intelligence(AI)for decision-making.However,recent research has found that while AI is more accurate and is not influenced by personal bias,people are generally averse to AI decision-making and perceive it as being less fair.Given the theoretical and practical importance of fairness perceptions of AI decision-making,a growing number of researchers have recently begun investigating how individuals form fairness perceptions in regards to AI decision-making.However,existing research is generally quite scattered and disorganized,which has limited researchers’and practitioners’understanding of fairness perceptions of AI decision-making from a conceptual and systematic perspective.Thus,this review first divided the relevant research into two categories.That is,(a)fairness perception studies in which AI is the decision-maker,which focus on how AI characteristics and individual characteristics affect individuals’fairness perceptions;and(b)fairness perception studies that compare AI and humans as decision-makers,which focus on the comparative effects of AI or humans as decision makers on individuals’fairness perceptions.Based on this systematic review,we proposed five promising directions for future research,such as exploring the affective mechanisms underlying the relationship between AI or individual characteristics and fairness perceptions.