AI-Induced Human Responsibility (AIHR) in AI-Human teams

arXiv cs.AI / 4/13/2026

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Key Points

The paper studies how responsibility is assigned in AI-human team workflows where blame is unclear, using an AI-assisted lending scenario involving discrimination, irresponsible lending, and filing errors.
Across four experiments totaling N=1,801 participants, people attributed significantly more responsibility to the human decision maker when paired with AI than when paired with another human (about a 10-point increase on a 0–100 scale).
The AI-Induced Human Responsibility (AIHR) effect appeared in both high- and low-harm situations and persisted even in cases where participants might be expected to blame-shift self-servingly.
Process evidence suggests AIHR is driven by perceptions of AI autonomy—AI is viewed as a constrained implementer—making the human the default locus of discretionary responsibility rather than mind-perception or self-threat explanations.
The authors argue the results extend work on algorithm aversion and responsibility gaps by showing AI-human teaming can increase human accountability, informing how to design accountability in AI-enabled organizations.

Abstract

As organizations increasingly deploy AI as a teammate rather than a standalone tool, morally consequential mistakes often arise from joint human-AI workflows in which causality is ambiguous. We ask how people allocate responsibility in these hybrid-agent settings. Across four experiments (N = 1,801) in an AI-assisted lending context (e.g., discriminatory rejection, irresponsible lending, and low-harm filing errors), participants consistently attributed more responsibility to the human decision maker when the human was paired with AI than when paired with another human (by an average of 10 points on a 0-100 scale across studies). This AI-Induced Human Responsibility (AIHR) effect held across high and low harm scenarios and persisted even where self-serving blame-shifting (when the human in question was the self) would be expected. Process evidence indicates that AIHR is explained by inferences of agent autonomy: AI is seen as a constrained implementer, which makes the human the default locus of discretionary responsibility. Alternative mechanisms (mind perception; self-threat) did not account for the effect. These findings extend research on algorithm aversion, hybrid AI-human organizational behavior and responsibility gaps in technology by showing that AI-human teaming can increase (rather than dilute) human responsibility, with implications for accountability design in AI-enabled organizations.