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

TL;DR

This study investigates how AI-human teaming influences responsibility attribution, finding that humans are perceived as more responsible when paired with AI, due to perceptions of AI as an autonomous agent.

Contribution

It reveals that AI-human collaboration increases perceived human responsibility, challenging assumptions that AI reduces human accountability in decision-making.

Findings

Humans attribute more responsibility to themselves when paired with AI.

AI is perceived as an autonomous agent, increasing human responsibility attribution.

The AIHR effect persists across different harm levels and self-serving blame scenarios.

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.