Summary of Ai Oversight and Human Mistakes: Evidence From Centre Court, by David Almog et al.
AI Oversight and Human Mistakes: Evidence from Centre Court
by David Almog, Romain Gauriot, Lionel Page, Daniel Martin
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); General Economics (econ.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates how artificial intelligence (AI) oversight affects human decision-making in high-stakes settings. Specifically, it examines the Hawk-Eye review system used to monitor umpires’ calls in top tennis tournaments. The results show that while AI oversight leads to a reduction in overall mistake rates, umpires become more cautious and increase the rate of calling balls “in” (Type I errors). A structural estimation model suggests that umpires are motivated by the psychological costs of being overruled by AI, with a 37% increased concern about Type II errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI is changing how we make decisions. This paper looks at how AI helps or hurts human decision-making in important situations. In tennis, AI helps referees get it right more often. But what happens when AI tells the referee they’re wrong? The results show that while AI makes things better overall, referees start to be more careful and make mistakes the other way (Type I errors). It’s like referees are worried about getting corrected too much! |
