“人们可以改变，模式也可以被打破”：自动化决策系统中权衡的情境化
"People can change, and patterns can be broken": Contextualizing Tradeoffs in Automated Decision-Making Systems
- University of Alberta（阿尔伯塔大学）
- University of Chicago（芝加哥大学）
- University of Waterloo（滑铁卢大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过准实验（N=777）发现，人们在高风险决策中偏好人类决策，且对公平性的理解超越正式定义，强调ADM系统设计需情境化并以人为本。
AI中文摘要：
自动化决策（ADM）系统越来越多地部署在抵押贷款、监狱量刑、健康保险覆盖和招聘等领域。在此类高风险领域设计负责任的ADM系统，需要确保隐私保护、不同人口群体间的公平性以及对对抗性操纵的鲁棒性。然而，优先考虑这些目标中的某一个会以牺牲另一个为代价，迫使部署时不得不选择接受哪种权衡。这些权衡直接或间接地影响着社会中人们的生活、安全和基本权利，因此，在提出适当解决方案之前，需要了解该人群的看法和优先事项。为此，我们开展了一项准实验研究（N=777），参与者评估了四个具有受控权衡的决策场景。参与者在四个场景中的三个中显著偏好人类决策（HDM）而非ADM，强调了人类判断、情境理解以及纳入不可量化因素的能力的价值。此外，在权衡方面，我们的发现不仅表明参与者的偏好高度依赖于情境，而且他们对特定目标（即公平性）的看法超越了正式定义。参与者通过多种视角解读公平性，包括隐私风险和易受操纵性，并将不公平或被操纵的结果视为准确性的失败。总体而言，我们的发现强调了在高风险情境中设计和治理ADM系统时，情境感知和以人为中心的方法的重要性。与其仅追求技术目标，不如根据ADM系统的权衡如何与特定领域内的具体期望以及社会价值观和对伤害与公平的看法相一致来评估它们。
英文摘要：
Automated decision-making (ADM) systems are increasingly deployed in domains such as mortgage lending, prison sentencing, health insurance coverage, and hiring. Designing a responsible ADM system in such high-stakes domains requires ensuring privacy protection, fairness across demographic groups, and robustness against adversarial manipulation. However, prioritizing one of these objectives comes at the cost of another, forcing a choice as to which tradeoff to accept in a deployment. These tradeoffs explicitly or implicitly impact the life, safety, and fundamental rights of the people in a society, and thus, the perceptions and priorities of this population are needed before we can produce appropriate solutions. To this end, we conducted a quasi-experimental study (N = 777) in which participants evaluated four decision-making scenarios with controlled tradeoffs. Participants significantly preferred human decision-making (HDM) over ADM in three of four scenarios, emphasizing the value of human judgment, contextual understanding, and the ability to incorporate non-quantifiable factors. Furthermore, in terms of tradeoffs, our findings not only show that participants' preferences are highly context-dependent, but also that their perception of a specific objective, fairness, extends beyond formal definitions. Participants interpret fairness through multiple lenses, including privacy risks and susceptibility to manipulation, and view unfair or manipulated outcomes as failures of accuracy. Overall, our findings highlight the importance of context-aware and human-centered approaches when designing and governing ADM systems in high-stakes situations. Rather than purely technical objectives, it is essential to evaluate ADM systems based on how their tradeoffs align with specific expectations within a given domain, as well as with societal values and perceptions of harm and fairness.
