The Problem with Metrics is a Fundamental Problem for AI

Autor:	Thomas, Rachel, Uminsky, David
Rok vydání:	2020
Předmět:	Computer Science - Computers and Society Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to EDSC (Ethics of Data Science Conference) 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.08512 Zobrazit plný text záznamu View this record from Arxiv