ABSTRACT
Metrics can play a central role in decision making across data driven organizations and their advantages and disadvantages have been widely studied. Metrics play an even more central role in AI algorithms and as such their risks and disadvantages are heightened. Moreover, this challenge also yields, in parallel, an equally grand contradiction in AI development: optimizing metrics results in far from optimal outcomes. A modern AI case study can be drawn from recommendation systems, which are widely used across many platforms to rank and promote content for users. Platforms are rife with attempts to game their algorithms, to show up higher in search results or recommended content, through fake clicks, fake reviews, fake followers. Automatic essay grading software currently used in at least 22 USA states focuses primarily on metrics like sentence length, vocabulary, spelling, and subject-verb agreement, but is unable to evaluate aspects of writing that are hard to quantify, such as creativity.
