研究人们在可改善资格时，如何应对不公平AI决策。
Understanding Decision Subjects' Engagement with and Perceived Fairness of AI Models When Opportunities of Qualification Improvement Exist
- 设计三组实验，考察人对AI决策的互动与自我提升策略。
- 即使模型明显不公，人们仍愿持续互动并投资自我改进。
- 感知公平性受群体歧视影响，尤其当低资质者改善更难时。
我们研究了当个体面临可反复、战略性回应的AI决策时，决策公平性如何影响其参与度和对模型公平性的感知。考虑两种战略反应：是否继续与模型交互，以及是否投资自我以提高未来获得有利决策的机会。通过三个受试者实验发现，在重复、策略性互动中，模型的决策公平性并未改变人们对继续交互或自我提升的意愿，即使模型在显著的受保护属性上表现出不公平。然而，当模型系统性地偏向某一群体时，决策主体仍感知该模型不公平，尤其是当低资质者改善资格的难度更大时。
原文摘要 · Abstract (English)
We explore how an AI model's decision fairness affects people's engagement with and perceived fairness of the model if they are subject to its decisions, but could repeatedly and strategically respond to these decisions. Two types of strategic responses are considered -- people could determine whether to continue interacting with the model, and whether to invest in themselves to improve their chance of future favorable decisions from the model. Via three human-subject experiments, we found that in decision subjects' strategic, repeated interactions with an AI model, the model's decision fairness does not change their willingness to interact with the model or to improve themselves, even when the model exhibits unfairness on salient protected attributes. However, decision subjects still perceive the AI model to be less fair when it systematically biases against their group, especially if the difficulty of improving one's qualification for the favorable decision is larger for the lowly-qualified people.
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