Fairness in AI is a growing concern for high-stakes decision making. Engaging stakeholders, especially lay users, in fair AI development is promising yet overlooked. Recent efforts explore enabling lay users to provide AI fairness-related feedback, but there is still a lack of understanding of how to integrate users' feedback into an AI model and the impacts of doing so. To bridge this gap, we collected feedback from 58 lay users on the fairness of a XGBoost model trained on the Home Credit dataset, and conducted offline experiments to investigate the effects of retraining models on accuracy, and individual and group fairness. Our work contributes baseline results of integrating user fairness feedback in XGBoost, and a dataset and code framework to bootstrap research in engaging stakeholders in AI fairness. Our discussion highlights the challenges of employing user feedback in AI fairness and points the way to a future application area of interactive machine learning.

翻译：人工智能的公平性在高风险决策场景中日益受到关注。让利益相关者（尤其是外行用户）参与公平人工智能的开发虽前景广阔却常被忽视。近期研究尝试让外行用户提供与人工智能公平性相关的反馈，但关于如何将用户反馈整合至人工智能模型及其影响机制仍存在认知空白。为填补这一空白，我们收集了58名外行用户针对基于Home Credit数据集训练的XGBoost模型公平性提出的反馈，并通过离线实验探究重新训练模型对准确率、个体公平性及群体公平性的影响。本研究贡献了将用户公平性反馈整合至XGBoost的基线结果，以及促进利益相关者参与人工智能公平性研究的基准数据集和代码框架。我们的讨论揭示了用户反馈在人工智能公平性应用中的挑战，并指明了交互式机器学习的未来应用方向。