Reliance on metrics is a fundamental challenge for AI
| Autor: | Rachel L. Thomas, David Uminsky |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Patterns, Vol 3, Iss 5, Pp 100476- (2022) |
| Druh dokumentu: | article |
| ISSN: | 2666-3899 27136434 |
| DOI: | 10.1016/j.patter.2022.100476 |
| Popis: | Summary: 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. The bigger picture: The success of current artificial intelligence (AI) approaches such as deep learning centers on their unreasonable effectiveness at metric optimization, yet overemphasizing metrics leads to a variety of real-world harms, including manipulation, gaming, and a myopic focus on short-term qualities and inadequate proxies. This principle is classically captured in Goodhart’s law: when a measure becomes the target, it ceases to be an effective measure. Current AI approaches have we aponized Goodhart’s law by centering on optimizing a particular measure as a target. This poses a grand contradiction within AI design and ethics: optimizing metrics results in far from optimal outcomes. It is crucial to understand this dynamic in order to mitigate the risks and harms we are facing as a result of misuse of AI. |
| Databáze: | Directory of Open Access Journals |
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