Fairness cues reorganize responsibility beyond system capability in social mixed-ability human–automation interaction
- 1Department of Psychology, Japan Women’s University
Abstract
Automation increasingly augments human performance, creating mixed-ability settings where people using different automated systems differ in performance capability, raising questions about how social fairness, defined here as whether people use the same or different systems, shapes the sense of responsibility for the outcomes of one’s actions. We conducted two experiments using a continuous drawing task where an automated system operated under either auto-correction (improving accuracy) or delay (impairing control). When participants acted alone (Experiment 1), the manipulation of the automated system’s performance capability worked as intended, with auto-correction enhancing the sense of agency, subjective performance, drawing speed, and accuracy, whereas responsibility ratings were unaffected by system capability. In Experiment 2, participants performed the same task while aware of another person using either the same or a different system, introducing a social context that changed responsibility judgments. Participants felt most responsible when both agents used the same system rather than different systems, regardless of actual performance differences. Trial-level analyses showed that this fairness effect was not explained by performance-related variations. Together, the findings indicate that social fairness cues can influence responsibility judgments beyond people’s own performance evaluations in human–automation interaction, motivating further research on fairness-aware design of collaborative technologies.
Related
- Clean Hands: From Semmelweis to Surgical Responsibility-Historical Foundations and Contemporary Implications for Infection Control.
- Editorial: 4Rs in ethnopharmacology - reduce, refine, replace and responsibility using animal models
- Environmental responsibility in the Sterile Processing Department: nursing leadership in sustainable development.
- MTDRA-Net: graph-temporal neural network for dynamic evaluation of college students’ responsibility
- Zero-Cost Digital Automation for Scalable Assessment in a National Mechanical Ventilation Curriculum
BibTeX
@article{ueda2026fairness,
title = {{Fairness cues reorganize responsibility beyond system capability in social mixed-ability human–automation interaction}},
author = {Sayako Ueda},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-59703-4},
url = {https://doi.org/10.1038/s41598-026-59703-4},
note = {CC BY},
}
RIS
TY - JOUR
TI - Fairness cues reorganize responsibility beyond system capability in social mixed-ability human–automation interaction
AU - Sayako Ueda
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-59703-4
UR - https://doi.org/10.1038/s41598-026-59703-4
AB - Automation increasingly augments human performance, creating mixed-ability settings where people using different automated systems differ in performance capability, raising questions about how social fairness, defined here as whether people use the same or different systems, shapes the sense of responsibility for the outcomes of one’s actions. We conducted two experiments using a continuous drawing task where an automated system operated under either auto-correction (improving accuracy) or delay (impairing control). When participants acted alone (Experiment 1), the manipulation of the automated system’s performance capability worked as intended, with auto-correction enhancing the sense of agency, subjective performance, drawing speed, and accuracy, whereas responsibility ratings were unaffected by system capability. In Experiment 2, participants performed the same task while aware of another person using either the same or a different system, introducing a social context that changed responsibility judgments. Participants felt most responsible when both agents used the same system rather than different systems, regardless of actual performance differences. Trial-level analyses showed that this fairness effect was not explained by performance-related variations. Together, the findings indicate that social fairness cues can influence responsibility judgments beyond people’s own performance evaluations in human–automation interaction, motivating further research on fairness-aware design of collaborative technologies.
SN - 2045-2322
ER -