Reliance on metrics is a fundamental challenge for AI.

Through a series of case studies, we review how the unthinking pursuit of metric optimization can lead to real-world harms, including recommendation systems promoting radicalization, well-loved teachers fired by an algorithm, and essay grading software that rewards sophisticated garbage. The metrics used are often proxies for underlying, unmeasurable quantities (e.g., "watch time" of a video as a proxy for "user satisfaction"). We propose an evidence-based framework to mitigate such harms by (1) using a slate of metrics to get a fuller and more nuanced picture; (2) conducting external algorithmic audits; (3) combining metrics with qualitative accounts; and (4) involving a range of stakeholders, including those who will be most impacted.