Fairness is a growing concern for high-risk decision-making using Artificial Intelligence (AI) but ensuring it through purely technical means is challenging: there is no universally accepted fairness measure, fairness is context-dependent, and there might be conflicting perspectives on what is considered fair. Thus, involving stakeholders, often without a background in AI or fairness, is a promising avenue. Research to directly involve stakeholders is in its infancy, and many questions remain on how to support stakeholders to feedback on fairness, and how this feedback can be integrated into AI models. Our work follows an approach where stakeholders can give feedback on specific decision instances and their outcomes with respect to their fairness, and then to retrain an AI model. In order to investigate this approach, we conducted two studies of a complex AI model for credit rating used in loan applications. In study 1, we collected feedback from 58 lay users on loan application decisions, and conducted offline experiments to investigate the effects on accuracy and fairness metrics. In study 2, we deepened this investigation by showing 66 participants the results of their feedback with respect to fairness, and then conducted further offline analyses. Our work contributes two datasets and associated code frameworks to bootstrap further research, highlights the opportunities and challenges of employing lay user feedback for improving AI fairness, and discusses practical implications for developing AI applications that more closely reflect stakeholder views about fairness.

翻译：公平性已成为人工智能（AI）用于高风险决策时日益受到关注的问题，但仅通过技术手段确保公平性面临挑战：缺乏普遍接受的公平性度量标准，公平性具有情境依赖性，且对于何为公平可能存在相互冲突的观点。因此，让通常不具备AI或公平性专业背景的利益相关者参与其中，是一条具有前景的路径。直接纳入利益相关者的研究尚处于起步阶段，关于如何支持利益相关者提供公平性反馈，以及如何将这些反馈整合到AI模型中，仍存在诸多问题。本研究采用一种方法，允许利益相关者针对具体决策实例及其结果提供公平性反馈，进而重新训练AI模型。为探究该方法，我们对贷款申请中使用的复杂信用评级AI模型进行了两项研究。在研究1中，我们收集了58名普通用户对贷款申请决策的反馈，并通过离线实验分析了其对准确性和公平性指标的影响。在研究2中，我们向66名参与者展示了其反馈对公平性的影响结果，并进行了进一步的离线分析。本研究的贡献包括：提供了两个数据集及配套代码框架以推动后续研究，揭示了利用普通用户反馈改进AI公平性的机遇与挑战，并讨论了开发更贴合利益相关者公平性观点的AI应用的实际意义。