  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.
