2020/02/20 by Rachel Thomas, Rachel L. Thomas, Thomas, Rachel +2 · 41 citations
Computer Science · #Artificial Intelligence (cs.AI) #Artificial intelligence #Computability, Logic, AI Algorithms #Computer science #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning and Algorithms #Machine Learning and Data Classification #cs.AI #cs.CY
paper · pdf · doi:10.48550/arxiv.2002.08512
published in arXiv (Cornell University) (Cornell University) · Accepted to EDSC (Ethics of Data Science Conference) 2020
arxiv created 2020/02/20 · openalex publication_date 2020/02/20 · arxiv updated 2020/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Optimizing a given metric is a central aspect of most current AI approaches, yet overemphasizing metrics leads to manipulation, gaming, a myopic focus on short-term goals, and other unexpected negative consequences. This poses a fundamental contradiction for AI development. Through a series of real-world case studies, we look at various aspects of where metrics go wrong in practice and aspects of how our online environment and current business practices are exacerbating these failures. Finally, we propose a framework towards mitigating the harms caused by overemphasis of metrics within AI by: (1) using a slate of metrics to get a fuller and more nuanced picture, (2) combining metrics with qualitative accounts, and (3) involving a range of stakeholders, including those who will be most impacted.