人工智能决策的公平感知

doi:10.3724/SP.J.1042.2022.01078

摘要/Abstract

摘要：

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。

关键词: 人工智能, 算法, 公平, 决策

Abstract:

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 regard 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 based on the type of decision makers. The first category is fairness perception research in which AI is the decision-maker. Drawn upon moral foundations theory, fairness heuristic theory, and fairness theory, these studies explain how AI characteristics (i.e., transparency, controllability, rule, and appropriateness) and individual characteristics (demographics, personalities, and values) affect individuals’ fairness perceptions. Existing research revealed that there were three main underlying cognitive mechanisms underlying the relationship between AI or individual characteristics and their fairness perceptions of AI decision-making: (a) individual characteristics and AI appropriateness affect individuals’ fairness perceptions via their moral intuition; (b) AI transparency affects individuals’ fairness perceptions via their perceived understandability; and (c) AI controllability affects individuals’ fairness perceptions via individuals’ needs fulfillment. The second category is fairness perception research that compares AI and humans as decision-makers. Based on computers are social actors (CASA) hypothesis, the algorithm reductionism perspective, and the machine heuristic model, these studies explained how individuals’ different perceptions of attributes between AI and humans (i.e., mechanistic attributes vs. societal attributes, simplified attributes vs. complex attributes, objective attributes vs. subjective attributes) affect individuals’ fairness perceptions and have revealed some inconsistent research findings. Specifically, some studies found that individuals perceive AI decision makers as being mechanical (i.e., lack of emotion and human touch) and simplified (i.e., decontextualization) than human decision makers, which leads individuals perceive that the decisions made by humans rather than AI are fairer. However, other studies found that compared to human decision makers, individuals regard AI decision makers as being more objective (i.e., consistent, neutral, and free of responsibility) than human decision makers, which leads individuals perceive that the decisions made by AI rather than human are fairer. Also, a small number of studies found that there is no significant difference in individuals’ fairness perceptions between AI decision makers and human decision makers. Such mixed findings reveal that individuals’ fairness perceptions of decision-making may be dependent on the specifical attributes of AI that individuals perceived in different contexts. Based on this systematic review, we proposed five promising directions for future research to help expand fairness perception literature in the context of AI decision-making. That is, (a) exploring the affective mechanisms underlying the relationship between AI or individual characteristics and their fairness perceptions of AI decision-making; (b) exploring the antecedents of interactional fairness perceptions of AI decision-making; (c) exploring fairness perceptions when robotic AI is the decision maker; (d) clarifying the boundary conditions when AI decision-making is considered to be fairer than human decision-making, versus when human decision-making is considered to be fairer than AI decision-making; and (e) exploring fairness perceptions when AI and humans make decisions jointly. We hope this review contribute to the understanding of individuals' fairness perceptions of AI decision-making theoretically and practically.

Key words: artificial intelligence, algorithm, fairness, decision-making

蒋路远, 曹李梅, 秦昕, 谭玲, 陈晨, 彭小斐. (2022). 人工智能决策的公平感知. 心理科学进展 , 30(5), 1078-1092.

JIANG Luyuan, CAO Limei, QIN Xin, TAN Ling, CHEN Chen, PENG Xiaofei. (2022). Fairness perceptions of artificial intelligence decision-making. Advances in Psychological Science, 30(5), 1078-1092.

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类别	文献数量	亚类别	机制	作者和年份
AI特征	8	透明性	可理解性/需求满足	Binns et al., 2018; Dodge et al., 2019^†; Lee et al., 2019^†; Shin, 2020; Shin, 2021b^†; Shin, in press^†; Shin & Park, 2019^†; van Berkel et al., 2019^†
	3	可控性	需求满足	Lee et al., 2019^†; Uhde et al., 2020^†; Wang et al., 2020^†
	4	规则性	需求满足	Chang et al., 2020^†; Cheng et al., 2021^†; Saxena et al., 2020^†; Srivastava et al., 2019^†
	5	适当性	道德直觉	Harrison et al., 2020^†; Grgić-Hlača, Redmiles et al., 2018^†; Kasinidou et al., 2021^†; Nyarko et al., 2020^†; Plane et al., 2018
个体特征	6	人口统计特征	道德直觉/可理解性	Grgić-Hlača et al., 2020^†; Pierson, 2017^†; Pierson, 2018^†; Saha et al., 2020; Schoeffer et al., 2021; van Berkel et al., 2021^†
个体特征	6	人格和价值观	道德直觉	Araujo et al., 2020^†; Grgić-Hlača, Zafar et al., 2018^†; Htun et al., 2021^†; Langer et al., 2018^†; Shin, 2021a^†; Smith, 2020^†

类别	文献数量	亚类别	机制	作者和年份
AI特征	8	透明性	可理解性/需求满足	Binns et al., 2018; Dodge et al., 2019^†; Lee et al., 2019^†; Shin, 2020; Shin, 2021b^†; Shin, in press^†; Shin & Park, 2019^†; van Berkel et al., 2019^†
	3	可控性	需求满足	Lee et al., 2019^†; Uhde et al., 2020^†; Wang et al., 2020^†
	4	规则性	需求满足	Chang et al., 2020^†; Cheng et al., 2021^†; Saxena et al., 2020^†; Srivastava et al., 2019^†
	5	适当性	道德直觉	Harrison et al., 2020^†; Grgić-Hlača, Redmiles et al., 2018^†; Kasinidou et al., 2021^†; Nyarko et al., 2020^†; Plane et al., 2018
个体特征	6	人口统计特征	道德直觉/可理解性	Grgić-Hlača et al., 2020^†; Pierson, 2017^†; Pierson, 2018^†; Saha et al., 2020; Schoeffer et al., 2021; van Berkel et al., 2021^†
个体特征	6	人格和价值观	道德直觉	Araujo et al., 2020^†; Grgić-Hlača, Zafar et al., 2018^†; Htun et al., 2021^†; Langer et al., 2018^†; Shin, 2021a^†; Smith, 2020^†

类别	文献数量	机制类别	机制	作者, 年份
机械属性vs. 社会属性	11	情感	情感/人情味/善意	Helberger et al., 2020^†; Kaibel et al., 2019; Langer et al., in press; Lee & Rich, 2021^†
机械属性vs. 社会属性	11	互动	互动性/人际接触/尊重	Acikgoz et al., 2020^†; Lee & Baykal, 2017^†; Noble et al., 2021; Nørskov et al., 2020^†; O?tting & Maier, 2018^†; Schlicker et al., in press; Wang, 2018
简化属性vs. 复杂属性	5	去情景化	去情景化/定量化/ 隐性知识/简化性	Höddinghaus et al., 2021^†; Lee, 2018^†; Nagtegaal, 2021^†; Newman et al., 2020
客观属性vs. 主观属性	6	一致性	一致性	Howard et al., 2020; Langer, König, & Papathanasiou, 2019; Langer, König, Sanchez, & Samadi, 2019
		中立性	中立性	Marcinkowski et al., 2020^†; Miller & Keiser, 2021^†
		责任归因	蓄意性归因	宋晓兵, 何夏楠, 2020

类别	文献数量	机制类别	机制	作者, 年份
机械属性vs. 社会属性	11	情感	情感/人情味/善意	Helberger et al., 2020^†; Kaibel et al., 2019; Langer et al., in press; Lee & Rich, 2021^†
机械属性vs. 社会属性	11	互动	互动性/人际接触/尊重	Acikgoz et al., 2020^†; Lee & Baykal, 2017^†; Noble et al., 2021; Nørskov et al., 2020^†; O?tting & Maier, 2018^†; Schlicker et al., in press; Wang, 2018
简化属性vs. 复杂属性	5	去情景化	去情景化/定量化/ 隐性知识/简化性	Höddinghaus et al., 2021^†; Lee, 2018^†; Nagtegaal, 2021^†; Newman et al., 2020
客观属性vs. 主观属性	6	一致性	一致性	Howard et al., 2020; Langer, König, & Papathanasiou, 2019; Langer, König, Sanchez, & Samadi, 2019
		中立性	中立性	Marcinkowski et al., 2020^†; Miller & Keiser, 2021^†
		责任归因	蓄意性归因	宋晓兵, 何夏楠, 2020