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TRUVACE RECORD VERSION
record: TRV-2026-2772
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:21:39.526558Z
status: published
lens: p_space
sector: health
headline: Human–AI collaboration in high-stakes decisions: a meta-analysis of healthcare and public sectors
dek: This meta-analysis of 146 experiments in the healthcare and public sectors examines human–AI synergy versus augmentation amid substantial heterogeneity. We find that AI augmentation reliably improves human performance (Hedges’ g = 0.622), whereas synergy effects are generally negative, with AI alone often outperforming human–AI teams (Hedges’ g = −0.380), although publication bias favours positive augmentation results. Additionally, task type, AI transparency, and user expertise significantly moderate outcomes.…
gain_title: (none)
problem_title: A meta-analysis of 146 healthcare and public-sector experiments found that human-AI teams generally did worse than AI working alone (Hedges' g = −0.380).
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: A meta-analysis of 146 healthcare and public-sector experiments found that human-AI teams generally did worse than AI working alone (Hedges' g = −0.380).
problem_evidence: synergy effects are generally negative, with AI alone often outperforming human–AI teams (Hedges’ g = −0.380)
quick_read: A meta-analysis of 146 experiments in healthcare and public-sector settings found that AI augmentation improves human performance (Hedges' g = 0.622). Teams of humans and AI, however, tended to do worse than AI alone, with a synergy effect of g = −0.380.

The pooled results caution against assuming that pairing people with AI is automatically beneficial in high-stakes decisions. The authors say task type, AI transparency and user expertise significantly changed the outcomes, and they point to selective automation of structured tasks with human oversight for ethically complex decisions.

The analysis found substantial heterogeneity across the experiments, and publication bias favours positive augmentation results, so the benefits of augmentation may be overstated.
limitation: The experiments showed substantial heterogeneity, and publication bias favours positive augmentation results, which may inflate the estimated benefit.
tag: Evidence-backed problem
key_points: The analysis pooled 146 experiments from healthcare and public-sector settings. | AI augmentation raised human performance with a Hedges' g of 0.622. | Synergy effects were generally negative, with a Hedges' g of −0.380 for human-AI teams against AI alone. | Task type, AI transparency and user expertise significantly moderated outcomes. | Publication bias favours positive augmentation results. | The authors support selective automation of structured tasks and human oversight for ethically complex decisions.
rundown: The paper separates two questions: whether AI assistance improves human performance (augmentation) and whether a human-AI team beats both the human and the AI alone (synergy). Augmentation showed a reliable gain, with Hedges' g of 0.622 across the pooled experiments.

Synergy came out negative overall, with AI alone often outperforming human-AI teams (g = −0.380). The effects varied with task type, how transparent the AI was and how expert the users were.

From these results the authors recommend selective automation of structured tasks and keeping human oversight for ethically complex decisions, aimed at policymakers and organisational leaders.
sources:
- peer_reviewed | Applied Economics Letters | https://doi.org/10.1080/13504851.2025.2586160 | 2025-11-09
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