Whose Voice Is It Anyway? Understanding AI Customization and Responsibility Attribution in Human-AI Collaboration
Bibliographic record
Abstract
This research examines how AI customization influences psychological ownership and responsibility attribution in human-AI collaboration. Through an online experiment with 507 participants using an AI assistant (ChatGPT 4o-mini), we found that customization enhances psychological ownership of the AI assistant that later transfers to ownership of the AI assistant’s work. This process leads to higher satisfaction that persists even after receiving negative reviews on the output. Additionally, we found that participants who customized their AI attributed greater personal responsibility for negative outcomes. Interestingly, this effect was moderated by reviewer identity (a human expert reviewer, a human non-expert reviewer, and an AI reviewer). Notably, participants treated AI reviewers similarly to human experts in terms of perceived expertise and responsibility attribution, viewing both as more legitimate feedback sources than human non-experts. While customization can enhance human-AI collaboration through psychological ownership, it also reveals potential risks where individuals might create echo chambers of validation, potentially compromising critical evaluation of AI-generated work. This study is among the first to examine the dual pathway through which customization influences psychological ownership of both AI systems and their outputs, and uniquely demonstrates that AI reviewers are perceived as equivalent to human experts in evaluative contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".