Next year’s HICSS will take place in early January in Hawaii, where Professor Mirbabaie will contribute two research articles. Respectively, these explore how virtual influencers have become a more frequent alternative to human influencers and discuss how to improve fairness when deploying AI-based algorithms in hiring.
Virtual influencers (VIs) have become a more frequent alternative to human influencers (HIs). While it should be clear to users that VIs cannot practice values and virtues in the real world, they seem to express certain virtues. Therefore, in his paper, Professor Mirbabaie focuses on identifying virtues conveyed by VIs and the effect of expressing virtues on follower engagement through a qualitative content analysis of social media posts. This paper suggests that conveying certain virtues seems to affect follower engagement positively and that companies have used VIs for virtue signaling without their followers noticing.
Through deploying AI-based algorithms, firms seek to mitigate systematic discrimination and bias in automated decision-making in hiring. Even though various types of bias exist for AI-based algorithms (e.g., using biased historical data), AI is used to assess and rank applicants in the hiring process. In this paper, Professor Mirbabaie conducts a systematic literature review to identify suitable strategies to reduce AI’s bias in hiring. Based on identifying nine fundamental articles and extracting four types of approaches to address unfairness in AI (pre-process, in-process, post-process, feature selection), this results in (a) deriving a research agenda for future studies and (b) proposing strategies for practitioners who design and develop AIs for hiring purposes.
Rieskamp, J., Hofeditz, L., Mirbabaie, M., & Stieglitz, S. (2023). Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring–Using a Systematic Literature Review to Guide Future Research. In Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).