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Human tennis calls shift due to algorithmic review, study finds

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv examines how human decision-makers adjust their behavior when they know their choices are subject to algorithmic review. Researchers analyzed Hawk-Eye data from professional tennis matches and found that human line judges increased their 'in' call error rate by 8.6 percentage points while decreasing 'out' call errors by 4.1 points when calls were within 20mm of the line. The study suggests that the presence of algorithmic correction can alter human error composition and that holding human decisions fixed may misrepresent the true organizational benefits of review systems. AI

IMPACT This research highlights how AI systems can influence human behavior, suggesting a need to account for these effects in system design and evaluation.

RANK_REASON Research paper published on arXiv detailing findings on human-algorithmic interaction. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Human tennis calls shift due to algorithmic review, study finds

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Research paper published on arXiv detailing findings on human-algorithmic interaction. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · David Almog, Romain Gauriot, Lionel Page, Daniel Martin ·

    Human Responses to Algorithmic Correction: Evidence from Centre Court

    arXiv:2401.16754v5 Announce Type: replace Abstract: We study how algorithmic review changes error composition using Hawk-Eye records from professional tennis. Within 20 mm of the line, mistaken "in" call rates increase by 8.6 percentage points, while mistaken "out" call rates dec…

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