Skin-Deep Bias : How Avatar Appearances Shape Perceptions of AI Hiring
(2026) 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026 In Conference on Human Factors in Computing Systems - Proceedings p.1-20- Abstract
Artificial intelligence is increasingly used in hiring, raising concerns about how applicants perceive these systems. While prior work on algorithmic fairness has emphasized technical bias mitigation, little is known about how avatar identity cues influence applicants' justice attributions in an interview context. We conducted a crowdsourcing study with 215 participants who completed an interview with photorealistic AI avatars varied in phenotypic traits (race and sex), followed by a standardized rejection. Using self-reports, sentiment analysis, and eye tracking, we measured perceptions of trust, fairness, and bias. Results show that racial mismatch heightened perceptions of ethnic bias, while partial match (sharing only one identity)... (More)
Artificial intelligence is increasingly used in hiring, raising concerns about how applicants perceive these systems. While prior work on algorithmic fairness has emphasized technical bias mitigation, little is known about how avatar identity cues influence applicants' justice attributions in an interview context. We conducted a crowdsourcing study with 215 participants who completed an interview with photorealistic AI avatars varied in phenotypic traits (race and sex), followed by a standardized rejection. Using self-reports, sentiment analysis, and eye tracking, we measured perceptions of trust, fairness, and bias. Results show that racial mismatch heightened perceptions of ethnic bias, while partial match (sharing only one identity) reduced fairness judgments compared to both full and no match. This work extends the Computers-Are-Social-Actors paradigm by demonstrating that avatar appearances shape justice-related evaluations of AI. We contribute to HCI by revealing how identity cues influence fairness attributions and offer actionable insights for designing equitable AI interview systems.
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- author
- Lau, Ka Hei Carrie ; Stark, Philipp LU ; Bozkir, Efe and Kasneci, Enkelejda
- organization
- publishing date
- 2026-04-13
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- crowdsourcing, fairness, generative AI, social identity
- host publication
- CHI '26 : Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems - Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
- series title
- Conference on Human Factors in Computing Systems - Proceedings
- editor
- Oliver, Nuria ; Shamma, David A. ; Candello, Heloisa ; Cesar, Pablo ; Lopes, Pedro ; Bozzon, Alessandro ; Kosch, Thomas ; Liao, Vera ; Ma, Xiaojuan ; Artizzu, Valentino ; Draxler, Fiona ; Lopez, Gustavo ; Reinschluessel, Anke V. ; Tong, Xin and Toups Dugas, Phoebe O.
- article number
- 125
- pages
- 1 - 20
- publisher
- Association for Computing Machinery
- conference name
- 2026 CHI Conference on Human Factors in Computing Systems, CHI 2026
- conference location
- Barcelona, Spain
- conference dates
- 2026-04-13 - 2026-04-17
- external identifiers
-
- scopus:105038743372
- ISBN
- 9798400722783
- DOI
- 10.1145/3772318.3790379
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2026 Copyright held by the owner/author(s).
- id
- 405ef168-8a56-49f4-bd35-55891c4e2125
- date added to LUP
- 2026-06-09 18:49:48
- date last changed
- 2026-06-15 08:14:10
@inproceedings{405ef168-8a56-49f4-bd35-55891c4e2125,
abstract = {{<p>Artificial intelligence is increasingly used in hiring, raising concerns about how applicants perceive these systems. While prior work on algorithmic fairness has emphasized technical bias mitigation, little is known about how avatar identity cues influence applicants' justice attributions in an interview context. We conducted a crowdsourcing study with 215 participants who completed an interview with photorealistic AI avatars varied in phenotypic traits (race and sex), followed by a standardized rejection. Using self-reports, sentiment analysis, and eye tracking, we measured perceptions of trust, fairness, and bias. Results show that racial mismatch heightened perceptions of ethnic bias, while partial match (sharing only one identity) reduced fairness judgments compared to both full and no match. This work extends the Computers-Are-Social-Actors paradigm by demonstrating that avatar appearances shape justice-related evaluations of AI. We contribute to HCI by revealing how identity cues influence fairness attributions and offer actionable insights for designing equitable AI interview systems.</p>}},
author = {{Lau, Ka Hei Carrie and Stark, Philipp and Bozkir, Efe and Kasneci, Enkelejda}},
booktitle = {{CHI '26 : Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems}},
editor = {{Oliver, Nuria and Shamma, David A. and Candello, Heloisa and Cesar, Pablo and Lopes, Pedro and Bozzon, Alessandro and Kosch, Thomas and Liao, Vera and Ma, Xiaojuan and Artizzu, Valentino and Draxler, Fiona and Lopez, Gustavo and Reinschluessel, Anke V. and Tong, Xin and Toups Dugas, Phoebe O.}},
isbn = {{9798400722783}},
keywords = {{crowdsourcing; fairness; generative AI; social identity}},
language = {{eng}},
month = {{04}},
pages = {{1--20}},
publisher = {{Association for Computing Machinery}},
series = {{Conference on Human Factors in Computing Systems - Proceedings}},
title = {{Skin-Deep Bias : How Avatar Appearances Shape Perceptions of AI Hiring}},
url = {{http://dx.doi.org/10.1145/3772318.3790379}},
doi = {{10.1145/3772318.3790379}},
year = {{2026}},
}