The Problem with Metrics is a Fundamental Problem for AI
This addresses a fundamental contradiction in AI development that affects all stakeholders, but it is incremental as it builds on existing critiques without introducing a new paradigm.
The paper tackles the problem of overemphasizing metrics in AI, which leads to manipulation and negative consequences, and proposes a framework involving multiple metrics, qualitative accounts, and stakeholder involvement to mitigate these harms.
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.