The authors are careful to stress that they are not advocating blind automation. Rights- and process-based constraints such as contestability, nondiscrimination, recourse, and accountable oversight can legitimately limit any decision system, whether human or machine. Their claim is instead one of symmetry. When audited evidence shows AI outperforming humans on endorsed criteria and the machine can satisfy core procedural safeguards, the justificatory burden shifts. At that point, they argue, asking only whether AI is safe enough to deploy leaves the harder question unasked: whether human control is safe enough to keep.

The argument rests on a substantial body of empirical research showing that human judgment is often less reliable than we assume. Decades of work dating back to Paul Meehl’s 1954 analysis of clinical versus statistical prediction, and the 2000 meta-analysis by William Grove and colleagues in Psychological Assessment, have repeatedly found that simple mechanical rules match or beat expert clinical judgment. Daniel Kahneman and colleagues’ work on noise documents how inconsistent human judgments are, even among trained professionals, while research on time-of-day effects shows that memory performance and other cognitive functions fluctuate with factors as mundane as the hour of the day. Ego depletion research further suggests that self-control, a resource central to careful decision-making, is limited and unreliable.

Aeon and Gruda trace the human gold standard fallacy to four recurring psychological drivers. The first is omission bias, the well-documented tendency to judge harms caused by action more harshly than equal or greater harms caused by inaction. When an algorithm makes a bad decision, the harm feels like something we did; when a human makes the same or worse decision, the harm is reframed as something that simply happened. Meta-analytic work by Sek Yeung, Timur Yay, and Gilad Feldman in 2022 confirmed robust omission-commission asymmetries in moral judgment, and philosophical analysis by Pinky Willemsen and Katia Reuter questions whether the omission effect is even coherent.

The second driver is agent-relative ethics, the philosophical position that what one is permitted to do differs from what one would prefer to see happen, making the identity of the decider morally salient. Research by David McNaughton and Piers Rawling on agent-relativity and the doing-happening distinction underpins this tradition, and the authors argue that such considerations make status-quo human authority feel morally safer even when it performs worse by our stated standards. The third driver they call intuitive exceptionalism, the deep-seated intuition that human minds occupy a special category, related to findings by Yoel Inbar and colleagues on people’s aversion to machines making moral decisions, and to taboo trade-off research by Philip Tetlock and colleagues showing that people react strongly when sacred values are weighed against mere utilities.

The fourth driver is the control premium: the value people place on retaining control for its own sake. Ellen Langer’s classic 1975 work on the illusion of control demonstrated that people systematically overestimate their influence over outcomes, and studies of decision-making under uncertainty show a propensity to under-delegate even when delegation would yield gains. Recent work by Anna Grundke on status threat suggests that outperforming machines evoke a threatened sense of human standing, while research by Nicolas Spatola and Antoine Normand documents the psychological costs of being compared to a superior artificial agent. Together, these four forces, the authors argue, make human authority feel better than machine authority even when evidence favors the machine.

The paper situates this argument within the current regulatory landscape, particularly the European Union’s Artificial Intelligence Act, which mandates human oversight for high-risk systems. The authors cite work by Jakob Laux on institutionalized distrust and democratic AI governance, by Leander Enqvist on what human oversight under the AI Act actually requires and by whom, and by Ben Green on the flaws of policies requiring human oversight of government algorithms. Legal scholarship on hybrid human-algorithmic decision-making by Tomas Enarsson, Leonhard Enqvist, and Marko Naarttijärvi, together with critiques of moral crumple zones by Madeleine Clare Elish, suggests that human-in-the-loop requirements often place humans in positions where they rubber-stamp automated outputs or absorb blame without exercising genuine control.

The empirical evidence on performance gaps is, in some domains, already striking. Generative AI has been shown to increase worker productivity in field experiments published in Science and the Quarterly Journal of Economics. Randomized controlled trials have found that AI tutoring outperforms in-class active learning in authentic educational settings. Large language models have been reported to outperform experts on challenging biology benchmarks, and work on AI-assisted prostate cancer MRI diagnosis examines whether domain experts can rely on AI appropriately. Studies of perceived fairness by Christoph Kern and colleagues and of public attitudes by Kevin Bansak and Elizabeth Paulson show that public acceptance of algorithmic decision-makers often depends on perceived performance, suggesting that intuitions may shift as evidence accumulates.

The authors also engage with the classic objection that delegating decisions to machines creates a responsibility gap. Andreas Matthias’s influential 2004 analysis of learning automata, subsequent work by Filip Santoni De Sio and Jero van den Hoven on meaningful human control, and Jilles Davidovic’s 2023 argument on the purpose of meaningful human control all grapple with the problem that machines may act in ways no one intended. Aeon and Gruda do not dismiss this concern, but they note that accountability research by Claude Novelli, Mariarosaria Taddeo, and Luciano Floridi, and by Atoosa Kasirzadeh and colleagues in the fairness, accountability, and transparency literature, suggests that institutional structures can attribute responsibility across human and machine actors. Meanwhile, legal scholars such as Diane Desierto and Frank Pasquale have long argued for due process rights in scored, automated societies, indicating that procedural protections need not presuppose human superiority.

The article’s central provocation is ultimately about epistemic fairness in policy design. If a society claims to care about accuracy, fairness, and welfare, then its governance frameworks should be symmetric in how they treat human and machine decision-makers: both should be audited, both should be subject to procedural constraints, and neither should enjoy an unearned presumption of legitimacy. The authors acknowledge that some decisions may remain legitimately reserved for humans on the basis of rights and dignity, but they insist that such reservations must be articulated and defended rather than smuggled in as defaults. Their closing challenge is likely to reverberate through AI governance debates for years: the burden of proof does not rest permanently on the side of automation. Once machines demonstrably serve our stated values better than we do, the burden falls on those who would keep control in human hands to explain why.

Subject of Research: The ethics of human versus AI decision-making authority and the burden of proof in AI governance

Article Title: When AI outperforms humans, who bears the burden of proof?

Article References: Aeon, B., & Gruda, D. (2026). When AI outperforms humans, who bears the burden of proof?. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03375-1

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03375-1

Keywords: artificial intelligence, AI governance, algorithm aversion, human oversight, decision-making, ethics of technology, omission bias, algorithmic accountability, AI Act, burden of proof, automation, moral psychology