AI involvement scenarios and perceived responsibility attribution in a human–AI legal decision vignette: associations with psychological ownership and sense of control

Background Artificial intelligence (AI) is increasingly used in consequential decisions, including legal and administrative judgments. When an AI-assisted decision has an adverse outcome, observers may find it difficult to determine how responsibility should be attributed to the human and AI actors. Objective This randomized vignette experiment examined perceived responsibility attribution across three bundled AI-assisted legal-decision scenarios. The scenarios varied AI functionality, human decision authority, and opportunities to intervene together. Post-vignette psychological ownership and perceived control were examined as condition-proximal perceptions associated with responsibility judgments. Methods Three hundred adults were retained for analysis (100 per condition). Each participant read one vignette describing an already completed legal decision and assessed the perceived responsibility of a fictional human reviewer and an AI system after an adverse outcome. Participants did not make a sentencing decision or interact with a functioning AI system. Randomized group contrasts were the principal comparisons. Secondary, exploratory mediation models used two indicator variables, with the low-AI scenario as the reference; post-vignette AI trust was excluded from the primary exploratory specification. Parallel and reverse-order models were examined as sensitivity analyses. Results The manipulation check differed across conditions, F (2, 297) = 1065.77, p < 0.001, η 2 = 0.878. Perceived human responsibility declined from the low- to the moderate- and high-AI scenarios, whereas perceived AI responsibility increased. The randomized mean differences in perceived human responsibility were −1.12 (moderate versus low) and −1.69 (high versus low). Exploratory indirect associations were negative via psychological ownership ( B = −0.385 and −0.713), perceived control ( B = −0.146 and −0.168), and the proposed ownership-to-control sequence ( B = −0.190 and −0.353), respectively; corresponding confidence intervals are reported in the Results. The indirect-effect intervals were calculated using delta-method approximations. Conclusion The randomized scenario comparisons showed differences in perceived responsibility attribution. Because the scenarios jointly varied multiple features, these findings do not isolate the causal effects of AI functionality, human authority, or opportunities to intervene. Ownership and control ratings partly reflected information supplied in the vignettes and therefore cannot establish distinct causal mediating mechanisms or their temporal sequence.