BiasMirror: Towards Mitigating Implicit Bias
Kathryn Brohman, Ali Khan, Fu, Tiancong, Raghava Rao Mukkamala, Abayomi Baiyere · Journal of the Association for Information Systems · 2024
This research-in-progress study attends to the issue of implicit bias, which underlies discriminatory attitudes and behaviors that people may not be aware that they hold. Specifically in the context of performance evaluations, attending to such bias is important as these evaluations form the foundations of many other important decisions such as pay, bonuses and awards, promotion, mobility, and layoffs. Leveraging a design science approach, our research aims to develop and implement effective interventions to mitigate bias in performance evaluations, utilizing digital technologies, specifically generative AI, and access to a unique dataset. In this short paper, we outline our DSR process, which includes a field experiment to test the efficacy of our intervention. Beyond its practical implications, this study is poised to contribute to the theoretical understanding of implicit bias as a societal concern and advance knowledge on the design of digital technologies in mitigating these.