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Logg, Minson & Moore 2019 — lay people prefer algorithmic to human judgment; experts rely on algorithms less and lose accuracy
Across multiple studies, lay people preferred algorithmic advice to human advice for numeric estimates, song-popularity forecasting, and romantic-match prediction. Preference for the algorithm waned when participants had to choose between an algorithm's estimate and their own. Experienced professionals relied on algorithmic advice less than lay people did, which hurt their accuracy.
Reliance on algorithmic vs human advice across three forecasting domains (numeric estimates from visual stimuli; song-popularity forecasting; romantic-match prediction); accuracy difference between expert and lay populationsLay-population preference for algorithmic advice over human advice was significant across domains. Preference waned when the choice was between algorithm and self. Experienced professionals showed lower algorithm-reliance than lay people, with measurable accuracy penalty (exact effect sizes not extracted to verification).
- Sample
- Multiple experiments across three forecasting domains; lay-and-expert populations (exact per-experiment N not extracted to verification)
- Methodology
- Behavioral experiments comparing algorithmic-advice-acceptance to human-advice-acceptance in matched forecasting tasks; expert-vs-lay subgroup analyses.
What this means
- Inverts the earlier 'algorithm aversion' result (Dietvorst et al. 2015) — establishes that baseline reliance on algorithmic advice is higher than older skeptical literature predicted. Calibration of AI personalization inherits a heavier design burden because users will accept defaults more readily.
- Expert-vs-lay asymmetry is itself a calibration finding: deploying AI advice into expert workflows requires accounting for the expert's lower baseline reliance — and the measurable accuracy cost when that lower reliance is operating in domains where the algorithm is better calibrated.
- Algorithm-vs-self framing is the load-bearing one for conversational AI: when the user has their own view, the algorithm's pull is weaker. The implication is that AI personalization is most impactful in domains where the user is unanchored — exactly where the user is most vulnerable to drift.
Source
Algorithm appreciation: People prefer algorithmic to human judgment
Organizational Behavior and Human Decision Processes · Jennifer M. Logg et al. · 2019 · peer-reviewed
Context
- What came before
- Dietvorst, Simmons & Massey 2015 (Algorithm Aversion) had established that people erroneously avoid algorithms after seeing them err. Logg et al. partially inverts this — baseline appreciation is higher than aversion, but erodes under specific conditions.
- What comes next
- Verify exact effect sizes across the three forecasting domains; subgroup analyses for expert vs lay; quantify the accuracy penalty for experts who under-rely. Connect to the conversational-AI calibration literature where expert-vs-lay asymmetry has not been systematically measured.
- Where this lands
- Encyclopedia Part II (workforce — implications for AI deployment in expert vs lay knowledge work; the expert-under-reliance accuracy penalty is the load-bearing finding for HR-tech), Part VI (governance — user-trust-in-AI is a design parameter, not a free variable).