pith. machine review for the scientific record. sign in

arxiv: 2604.06187 · v1 · submitted 2026-02-16 · 💻 cs.HC

Recognition: no theorem link

Skin-Deep Bias: How Avatar Appearances Shape Perceptions of AI Hiring

Ka Hei Carrie Lau , Philipp Stark , Efe Bozkir , Enkelejda Kasneci
Authors on Pith no claims yet

Pith reviewed 2026-05-15 21:53 UTC · model grok-4.3

classification 💻 cs.HC
keywords AI hiringavatar identityperceived fairnessethnic biasphenotypic traitshuman-computer interactionalgorithmic justice
0
0 comments X

The pith

Racial mismatch between AI avatars and applicants heightens perceptions of ethnic bias in hiring interviews.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether the visible race and sex of photorealistic AI avatars used as interviewers affect how applicants judge the fairness and bias of an automated hiring process. In a study of 215 participants who experienced a standardized rejection, racial mismatch increased reports of ethnic bias, while sharing only one identity trait produced lower fairness ratings than either full matches or complete mismatches. These patterns emerged in self-reports, sentiment analysis of open responses, and eye-tracking data. The findings indicate that identity cues in avatar design continue to shape justice attributions even when the decision-maker is known to be algorithmic.

Core claim

Photorealistic AI avatars varied in phenotypic traits (race and sex) were deployed in simulated interviews; results showed that racial mismatch between avatar and participant increased perceptions of ethnic bias, while partial identity matches (sharing only race or only sex) reduced fairness judgments relative to both complete matches and complete mismatches.

What carries the argument

Photorealistic AI avatars whose race and sex were systematically varied, with perceptions of trust, fairness, and bias measured through self-reports, sentiment analysis, and eye tracking after a fixed rejection.

If this is right

  • Avatar design choices in AI hiring tools directly influence applicants' attributions of bias and fairness.
  • The Computers-Are-Social-Actors effect extends to justice evaluations of algorithmic decision systems.
  • Equitable AI interview systems require deliberate attention to how avatar identity cues align with or diverge from applicants.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same avatar-identity effects may appear in other high-stakes AI domains such as loan or medical triage interfaces.
  • Randomizing or neutralizing avatar appearances could serve as a practical mitigation if the partial-match penalty proves robust.
  • Regulators might require disclosure of avatar characteristics used in automated interviews to allow applicants to anticipate cue-based reactions.

Load-bearing premise

Observed differences in fairness and bias reports are caused by the avatars' manipulated phenotypic traits rather than other uncontrolled features of the interview scenario or participant expectations.

What would settle it

A replication study that holds all interview elements constant while removing variation in avatar race and sex and finds no corresponding differences in fairness or bias perceptions would falsify the claim.

Figures

Figures reproduced from arXiv: 2604.06187 by Efe Bozkir, Enkelejda Kasneci, Ka Hei Carrie Lau, Philipp Stark.

Figure 1
Figure 1. Figure 1: Study overview. We recruited participants via crowdsourcing and assigned them to a 2 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Participants accessed the study through a video [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: High-level system architecture of the experimental [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Definition of AOI (Area of Interest). Face and body [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Experimental procedure. After presurvey measures [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Avatar conditions were assigned according to our match [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 6
Figure 6. Figure 6: (a) Average perceived affective and cognitive trust with no significance between any matching condition, rated on a [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Implicit behavioral measures: (a) Sentiment polarity scores (range: -1 to +1; higher values indicate more positive [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Screens shown before the scripted rejection: a load [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
read the original 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) 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 justicerelated 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The paper reports a crowdsourced study with 215 participants who completed a simulated hiring interview with photorealistic AI avatars that varied in phenotypic traits (race and sex), followed by a standardized rejection. Perceptions of trust, fairness, and bias were measured via self-reports, sentiment analysis, and eye tracking. Key results indicate that racial mismatch increased perceptions of ethnic bias, while partial identity match (sharing only one trait) lowered fairness judgments relative to full match or no match. The work claims to extend the Computers-Are-Social-Actors paradigm by showing how avatar appearances shape justice attributions and offers design implications for equitable AI interview systems.

Significance. If the central findings hold after addressing controls, the non-monotonic pattern of identity matching on fairness perceptions would provide novel empirical evidence for how visual cues influence justice evaluations in AI-mediated hiring. This could inform HCI design guidelines for avatar selection in recruitment tools and strengthen the CASA framework with multimodal data from self-reports, sentiment, and eye tracking.

major comments (2)
  1. [Methods] Methods section: The description of the avatar manipulation does not confirm that non-phenotypic features (voice, facial animation, lighting, attractiveness, or interview script wording) were held constant across full-match, partial-match, and no-match conditions. Without explicit controls or stimulus validation, the reported differences in fairness and bias cannot be confidently attributed to the intended race/sex manipulations rather than confounds.
  2. [Results] Results section: The manuscript reports the non-monotonic pattern but lacks effect sizes, confidence intervals, exact statistical tests, and power analysis for the key comparisons (racial mismatch on ethnic bias; partial match on fairness). These details are necessary to evaluate the robustness of the 215-participant findings.
minor comments (3)
  1. [Abstract] Abstract: The term 'justicerelated evaluations' is missing a hyphen and should read 'justice-related evaluations'.
  2. [Introduction] Introduction: Additional citations to recent HCI work on avatar bias in virtual interviews or algorithmic fairness perceptions would strengthen the positioning relative to prior CASA studies.
  3. [Discussion] Discussion: The actionable design insights for equitable AI systems are stated at a high level; more concrete recommendations (e.g., specific avatar selection criteria or testing protocols) would improve utility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive feedback on our manuscript. We address each major comment below and have made revisions to improve clarity and rigor where appropriate.

read point-by-point responses

  1. Referee: [Methods] Methods section: The description of the avatar manipulation does not confirm that non-phenotypic features (voice, facial animation, lighting, attractiveness, or interview script wording) were held constant across full-match, partial-match, and no-match conditions. Without explicit controls or stimulus validation, the reported differences in fairness and bias cannot be confidently attributed to the intended race/sex manipulations rather than confounds.

    Authors: We appreciate this point and agree that explicit confirmation is needed. The avatars were generated from the same base model with identical voice synthesis, facial animation parameters, lighting conditions, and interview script wording; only the phenotypic traits (skin tone, facial features for race; and secondary sex characteristics) were varied. We have revised the Methods section to detail these controls and added a brief report of our pre-experiment stimulus validation pilot (n=30) confirming no significant differences in perceived attractiveness or other non-target attributes across conditions. revision: yes

  2. Referee: [Results] Results section: The manuscript reports the non-monotonic pattern but lacks effect sizes, confidence intervals, exact statistical tests, and power analysis for the key comparisons (racial mismatch on ethnic bias; partial match on fairness). These details are necessary to evaluate the robustness of the 215-participant findings.

    Authors: We agree these details strengthen the reporting. The key comparisons used mixed ANOVA with post-hoc Tukey tests; we have now inserted effect sizes (partial η² and Cohen’s d), 95% confidence intervals, exact p-values, and a sensitivity power analysis (achieved power = 0.82 for the observed medium effects at α=0.05 with n=215) into the Results section and a new supplementary table. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical measurements from participant data

full rationale

This is a standard empirical HCI study reporting direct measurements of self-reported fairness, bias perceptions, sentiment, and eye-tracking metrics from 215 participants exposed to manipulated avatar conditions. No equations, model derivations, fitted parameters, or predictions are present that could reduce results to inputs by construction. The abstract and methods describe a controlled experiment with standardized rejection and phenotypic variation; findings are presented as observed patterns rather than derived from prior self-citations or ansatzes. The analysis is self-contained against external benchmarks (participant responses), with no load-bearing self-citation chains or renaming of known results as new derivations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that self-reported perceptions and eye-tracking data validly capture bias attributions and that avatar phenotypic traits were the primary manipulated variable without major confounds.

axioms (1)
  • domain assumption Self-report scales and sentiment analysis reliably measure subjective perceptions of bias and fairness
    Invoked when interpreting survey responses and text analysis as evidence of justice attributions.

pith-pipeline@v0.9.0 · 5468 in / 1199 out tokens · 45441 ms · 2026-05-15T21:53:49.854663+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

100 extracted references · 100 canonical work pages

  1. [1]

    Elham Albaroudi, Taha Mansouri, and Ali Alameer. 2024. A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring.AI5, 1 (2024), 383–404. doi:10.3390/ai5010019

    work page doi:10.3390/ai5010019 2024

  2. [2]

    Anna Aumüller, Andreas Winklbauer, Beatrice Schreibmaier, Bernad Batinic, and Martina Mara. 2024. Rethinking feminized service bots: user responses to abstract and gender-ambiguous chatbot avatars in a large-scale interaction study.Personal and Ubiquitous Computing28, 6 (01 Dec 2024), 1021–1032. doi:10.1007/s00779- 024-01830-8

    work page doi:10.1007/s00779- 2024

  3. [3]

    Jasmin Baake, Josephine Schmitt, and Julia Metag. 2025. Balancing Realism and Trust: AI Avatars In Science Communication.Journal of Science Communication 24 (04 2025). doi:10.22323/2.24020203

    work page doi:10.22323/2.24020203 2025

  4. [4]

    Gérard Bailly, Stephan Raidt, and Frédéric Elisei. 2010. Gaze, conversational agents and face-to-face communication.Speech Communication52, 6 (2010), 598–

    work page 2010

  5. [5]

    doi:10.1016/j.specom.2010.02.015 Speech and Face-to-Face Communication

    work page doi:10.1016/j.specom.2010.02.015 2010

  6. [6]

    Marianne Bertrand and Sendhil Mullainathan. 2004. Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Mar- ket Discrimination.The American Economic Review94, 4 (2004), 991–1013. http://www.jstor.org/stable/3592802

    work page arXiv 2004

  7. [7]

    Hi. I’m Molly, Your Virtual Interviewer!

    Shreyan Biswas, Ji-Youn Jung, Abhishek Unnam, Kuldeep Yadav, Shreyansh Gupta, and Ujwal Gadiraju. 2024. “Hi. I’m Molly, Your Virtual Interviewer!” Exploring the Impact of Race and Gender in AI-Powered Virtual Interview Ex- periences.Proceedings of the AAAI Conference on Human Computation and Crowdsourcing12, 1 (Oct. 2024), 12–22. doi:10.1609/hcomp.v12i1.31596

    work page doi:10.1609/hcomp.v12i1.31596 2024

  8. [8]

    Pieter Blignaut and Daniël Wium. 2014. Eye-tracking data quality as affected by ethnicity and experimental design.Behavior Research Methods46, 1 (01 Mar 2014), 67–80. doi:10.3758/s13428-013-0343-0

    work page doi:10.3758/s13428-013-0343-0 2014

  9. [9]

    Kelly, Vonetta Coakley, Tamar Gordon, Shola Thomp- son, Erika Levy, Andrea Cassells, Jonathan N

    Elizabeth Brondolo, Kim P. Kelly, Vonetta Coakley, Tamar Gordon, Shola Thomp- son, Erika Levy, Andrea Cassells, Jonathan N. Tobin, Monica Sweeney, and Richard J. Contrada. 2005. The Perceived Ethnic Discrimination Question- naire: Development and Preliminary Validation of a Community Version.Jour- nal of Applied Social Psychology35, 2 (2005), 335–365. doi...

    work page doi:10.1111/j.1559- 2005

  10. [10]

    Merve Bulut and Burak Erdeniz. 2020. The Other-Race and Other-Species Effect during a Sex Categorization Task: An Eye Tracker Study.Behavioral Sciences10, 1 (2020), 10 pages. doi:10.3390/bs10010024

    work page doi:10.3390/bs10010024 2020

  11. [11]

    Zana Buçinca, Chau Minh Pham, Maurice Jakesch, Marco Tulio Ribeiro, Alexan- dra Olteanu, and Saleema Amershi. 2023. AHA!: Facilitating AI Impact As- sessment by Generating Examples of Harms. arXiv:2306.03280 [cs.HC] https: //arxiv.org/abs/2306.03280

    work page arXiv 2023

  12. [12]

    Campion, David K

    Michael A. Campion, David K. Palmer, and James E. Campion. 1997. A review of structure in the selection interview.Personnel Psychology50, 3 (1997), 655–702. doi:10.1111/j.1744-6570.1997.tb00709.x

    work page doi:10.1111/j.1744-6570.1997.tb00709.x 1997

  13. [13]

    Stepanova, Benjamin Tag, Monica Perusquia- Hernandez, Alexandra Kitson, Arindam Dey, Sven Mayer, and Abdallah El Ali

    Francesco Chiossi, Ekaterina R. Stepanova, Benjamin Tag, Monica Perusquia- Hernandez, Alexandra Kitson, Arindam Dey, Sven Mayer, and Abdallah El Ali

    work page

  14. [14]

    InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI EA ’24)

    PhysioCHI: Towards Best Practices for Integrating Physiological Signals in HCI. InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI EA ’24). Association for Computing Machinery, New York, NY, USA, Article 485, 7 pages. doi:10.1145/3613905.3636286

    work page doi:10.1145/3613905.3636286

  15. [15]

    Colquitt

    Jason A. Colquitt. 2001. On the dimensionality of organizational justice: A construct validation of a measure.Journal of Applied Psychology86, 3 (2001), 386–400. doi:10.1037/0021-9010.86.3.386

    work page doi:10.1037/0021-9010.86.3.386 2001

  16. [16]

    Michela Cortini, Teresa Galanti, and Massimiliano Barattucci. 2019. The Effect of Different Rejection Letters on Applicants’ Reactions.Behavioral Sciences9, 10 (2019), 1–15. https://www.mdpi.com/2076-328X/9/10/102

    work page 2019

  17. [17]

    2019.Bias and Productivity in Humans and Machines

    Bo Cowgill. 2019.Bias and Productivity in Humans and Machines. Upjohn Institute Working Paper 19-309. W.E. Upjohn Institute for Employment Research. https://ssrn.com/abstract=3433737

    work page 2019

  18. [18]

    Birk, Youssef Shiban, Knut Schnell, and Regan L

    Martin Johannes Dechant, Max V. Birk, Youssef Shiban, Knut Schnell, and Regan L. Mandryk. 2021. How Avatar Customization Affects Fear in a Game-based Digital Exposure Task for Social Anxiety.Proc. ACM Hum.-Comput. Interact.5, CHI PLAY, Article 248 (Oct. 2021), 27 pages. doi:10.1145/3474675

    work page doi:10.1145/3474675 2021

  19. [19]

    Do, Juanita Benjamin, Camille Isabella Protko, and Ryan P

    Tiffany D. Do, Juanita Benjamin, Camille Isabella Protko, and Ryan P. McMahan

    work page

  20. [20]

    doi:10.1109/TVCG

    Cultural Reflections in Virtual Reality: The Effects of User Ethnicity in Avatar Matching Experiences on Sense of Embodiment.IEEE Transactions on Visualization and Computer Graphics30, 11 (2024), 7408–7418. doi:10.1109/TVCG. 2024.3456196

    work page doi:10.1109/tvcg 2024

  21. [21]

    Chad Edwards, Autumn Edwards, Brett Stoll, Xialing Lin, and Noelle Massey

    work page

  22. [22]

    Evaluations of an artificial intelligence instructor’s voice: Social Identity Theory in human-robot interactions.Computers in Human Behavior90 (2019), 357–362

    work page 2019

  23. [23]

    Elkin, Matthew Kay, James J

    Lisa A. Elkin, Matthew Kay, James J. Higgins, and Jacob O. Wobbrock. 2021. An Aligned Rank Transform Procedure for Multifactor Contrast Tests. InThe 34th Annual ACM Symposium on User Interface Software and Technology(Virtual Event, USA)(UIST ’21). Association for Computing Machinery, New York, NY, USA, 754–768. doi:10.1145/3472749.3474784

    work page doi:10.1145/3472749.3474784 2021

  24. [24]

    Andrew Gambino, Jesse Fox, and Rabindra A Ratan. 2020. Building a Stronger CASA: Extending the Computers Are Social Actors Paradigm.Human- Machine Communication1 (2020), 71–85. https://search.informit.org/doi/10. 3316/INFORMIT.097034846749023

    work page 2020

  25. [25]

    Gilliland

    Stephen W. Gilliland. 1993. The Perceived Fairness of Selection Systems: An Organizational Justice Perspective.The Academy of Management Review18, 4 (1993), 694–734. http://www.jstor.org/stable/258595

    work page 1993

  26. [26]

    Lorentsa Gkinko and Amany Elbanna. 2023. The appropriation of conversational AI in the workplace: A taxonomy of AI chatbot users.International Journal of Information Management69 (2023), 102568. doi:10.1016/j.ijinfomgt.2022.102568

    work page doi:10.1016/j.ijinfomgt.2022.102568 2023

  27. [27]

    Li Gong. 2008. How social is social responses to computers? The function of the degree of anthropomorphism in computer representations.Computers in Human Behavior24, 4 (2008), 1494–1509. doi:10.1016/j.chb.2007.05.007 Including the Special Issue: Integration of Human Factors in Networked Computing

    work page doi:10.1016/j.chb.2007.05.007 2008

  28. [28]

    2024.Chipotle Introduces New AI Hiring Plat- form to Support Its Accelerated Growth

    Chipotle Mexican Grill. 2024.Chipotle Introduces New AI Hiring Plat- form to Support Its Accelerated Growth. Chipotle Mexican Grill. Re- trieved August 20, 2025 from https://newsroom.chipotle.com/2024-10-22- CHIPOTLE-INTRODUCES-NEW-AI-HIRING-PLATFORM-TO-SUPPORT-ITS- ACCELERATED-GROWTH

    work page 2024

  29. [29]

    2010.Social Desirability Bias

    Pamela Grimm. 2010.Social Desirability Bias. John Wiley & Sons, Ltd, Hoboken, NJ, USA. doi:10.1002/9781444316568.wiem02057 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781444316568.wiem02057

    work page doi:10.1002/9781444316568.wiem02057 2010

  30. [30]

    Haensel, Tim J

    Jennifer X. Haensel, Tim J. Smith, and Atsushi Senju. 2022. Cultural differences in mutual gaze during face-to-face interactions: A dual head-mounted eye-tracking study.Visual Cognition30, 1-2 (2022), 100–115. doi:10.1080/13506285.2021.1928354 arXiv:https://doi.org/10.1080/13506285.2021.1928354

    work page doi:10.1080/13506285.2021.1928354 2022

  31. [31]

    Marshall, Jennifer Booker, Houda Elmimouni, Imani Walker, and Jennifer A

    David Hankerson, Andrea R. Marshall, Jennifer Booker, Houda Elmimouni, Imani Walker, and Jennifer A. Rode. 2016. Does Technology Have Race?. InProceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems(San Jose, California, USA)(CHI EA ’16). Association for Computing Machinery, New York, NY, USA, 473–486. doi:10.1145/2...

    work page doi:10.1145/2851581.2892578 2016

  32. [32]

    Sumin Heo, Erika R Chen, and Jasmine Khuu. 2025. Exploring Gender Biases in LLM-based Voice Chatbots for Job Interviews. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’25). Association for Computing Machinery, New York, NY, USA, Article 899, 8 pages. doi:10.1145/3706599.3719281

    work page doi:10.1145/3706599.3719281 2025

  33. [33]

    Evelien Heyselaar. 2023. The CASA theory no longer applies to desktop comput- ers.Scientific Reports13, 1 (11 Nov 2023), 19693. doi:10.1038/s41598-023-46527-9

    work page doi:10.1038/s41598-023-46527-9 2023

  34. [34]

    Hills and J

    Peter J. Hills and J. Michael Pake. 2013. Eye-tracking the own-race bias in face recognition: Revealing the perceptual and socio-cognitive mechanisms.Cognition 129, 3 (2013), 586–597. doi:10.1016/j.cognition.2013.08.012

    work page doi:10.1016/j.cognition.2013.08.012 2013

  35. [35]

    2025.HireVue: AI-driven Hiring Platform

    HireVue. 2025.HireVue: AI-driven Hiring Platform. HireVue. Retrieved July 7, 2025 from https://www.hirevue.com/

    work page 2025

  36. [36]

    Hogg and Scott A

    Michael A. Hogg and Scott A. Reid. 2006. Social Identity, Self-Categorization, and the Communication of Group Norms.Communication Theory16 (2006), 7–30. https://api.semanticscholar.org/CorpusID:16594940

    work page 2006

  37. [37]

    Md Sajjad Hosain, Mohammad Bin Amin, Gouranga Chandra Debnath, and Md Atikur Rahaman. 2025. The use of Artificial Intelligence (AI) in the hiring process: Job applicants’ perceptions of procedural justice.Computers in Human Behavior Reports19 (2025), 100713. doi:10.1016/j.chbr.2025.100713

    work page doi:10.1016/j.chbr.2025.100713 2025

  38. [38]

    Edwin Ip. 2025. Fair AI in hiring: Experimental evidence on how biased hir- ing algorithms and different debiasing methods affect the quality and diversity of applicants.Behavioral Science & Policy11, 1 (2025), 44–54. doi:10.1177/ 23794607251353585 arXiv:https://doi.org/10.1177/23794607251353585

    work page doi:10.1177/23794607251353585 2025

  39. [39]

    Sheree Josephson and Michael E. Holmes. 2008. Cross-race recognition deficit and visual attention: do they all look (at faces) alike?. InProceedings of the 2008 Symposium on Eye Tracking Research & Applications(Savannah, Georgia) (ETRA ’08). Association for Computing Machinery, New York, NY, USA, 157–164. doi:10.1145/1344471.1344513

    work page doi:10.1145/1344471.1344513 2008

  40. [40]

    Kang and Galen V

    Sonia K. Kang and Galen V. Bodenhausen. 2015. Multiple Identities in Social Perception and Interaction: Challenges and Opportunities.Annual Review of Psychology66, Volume 66, 2015 (2015), 547–574. doi:10.1146/annurev-psych- 010814-015025

    work page doi:10.1146/annurev-psych- 2015

  41. [41]

    Krzysztof Krejtz, Andrew Duchowski, Izabela Krejtz, Agnieszka Szarkowska, and Agata Kopacz. 2016. Discerning Ambient/Focal Attention with Coefficient K.ACM Trans. Appl. Percept.13, 3, Article 11 (May 2016), 20 pages. doi:10.1145/2896452

    work page doi:10.1145/2896452 2016

  42. [42]

    Vera Liao, and Chenhao Tan

    Vivian Lai, Chacha Chen, Alison Smith-Renner, Q. Vera Liao, and Chenhao Tan. 2023. Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies. InProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency(Chicago, IL, USA)(FAccT ’23). Association for Computing Machinery, New York,...

    work page doi:10.1145/3593013.3594087 2023

  43. [43]

    König, and Victoria Hemsing

    Markus Langer, Cornelius J. König, and Victoria Hemsing. 2020. Is anybody listening? The impact of automatically evaluated job interviews on impression management and applicant reactions.Journal of Man- agerial Psychology35, 4 (02 2020), 271–284. doi:10.1108/JMP-03-2019- 0156 arXiv:https://www.emerald.com/jmp/article-pdf/35/4/271/1560299/jmp-03- 2019-0156.pdf

    work page doi:10.1108/jmp-03-2019- 2020

  44. [44]

    Hartwell, Frederick P

    Julia Levashina, Christopher J. Hartwell, Frederick P. Morgeson, and Michael A. Campion. 2014. The structured employment interview: Narrative and quantitative review of the research literature.Personnel Psychology67, 1 (2014), 241–293. doi:10.1111/peps.12052

    work page doi:10.1111/peps.12052 2014

  45. [45]

    2022.Attention measurement with eye-tracking & K-coefficient – explained

    Beata Lewandowska and Kasia Wisiecka. 2022.Attention measurement with eye-tracking & K-coefficient – explained. RealEye. Retrieved August 21, 2025 from https://www.realeye.io/blog/143-attention-measurements-k-coefficient Blog post in collaboration with SWPS University

    work page 2022

  46. [46]

    Lan Li, Tina Lassiter, Joohee Oh, and Min Kyung Lee. 2021. Algorithmic Hiring in Practice: Recruiter and HR Professional’s Perspectives on AI Use in Hiring. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society(Virtual Event, USA)(AIES ’21). Association for Computing Machinery, New York, NY, USA, 166–176. doi:10.1145/3461702.3462531

    work page doi:10.1145/3461702.3462531 2021

  47. [47]

    Vera Liao, Daniel Gruen, and Sarah Miller

    Q. Vera Liao, Daniel Gruen, and Sarah Miller. 2020. Questioning the AI: Informing Design Practices for Explainable AI User Experiences. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–15. doi:10.1145/3313831.3376590

    work page doi:10.1145/3313831.3376590 2020

  48. [48]

    Vera Liao and Kush R

    Q. Vera Liao and Kush R. Varshney. 2022. Human-Centered Explainable AI (XAI): From Algorithms to User Experiences. arXiv:2110.10790 [cs.AI] https: //arxiv.org/abs/2110.10790

    work page arXiv 2022

  49. [49]

    Sally Lindsay and Anne-Marie DePape. 2015. Exploring differences in the content of job interviews between youth with and without a physical disability.PLoS One10, 3 (March 2015), e0122084

    work page 2015

  50. [50]

    Sebastian Linxen, Christian Sturm, Florian Brühlmann, Vincent Cassau, Klaus Opwis, and Katharina Reinecke. 2021. How WEIRD is CHI?. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems(Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 143, 14 pages. doi:10.1145/3411764.3445488

    work page doi:10.1145/3411764.3445488 2021

  51. [51]

    Kate Loveys, Gabrielle Sebaratnam, Mark Sagar, and Elizabeth Broadbent. 2020. The Effect of Design Features on Relationship Quality with Embodied Conversa- tional Agents: A Systematic Review.International Journal of Social Robotics12, 6 (01 Dec 2020), 1293–1312. doi:10.1007/s12369-020-00680-7

    work page doi:10.1007/s12369-020-00680-7 2020

  52. [52]

    Brenda Major and Sarah S. M. Townsend. 2012. Meaning Making in Response to Unfairness.Psychological Inquiry23, 4 (2012), 361–366. doi:10.1080/1047840X. How Avatar Appearances Shape Perceptions of AI Hiring CHI ’26, April 13–17, 2026, Barcelona, Spain 2012.722785 arXiv:https://doi.org/10.1080/1047840X.2012.722785

    work page doi:10.1080/1047840x 2012

  53. [53]

    Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021. A Survey on Bias and Fairness in Machine Learning.ACM Comput. Surv.54, 6, Article 115 (July 2021), 35 pages. doi:10.1145/3457607

    work page doi:10.1145/3457607 2021

  54. [54]

    Clifford Nass and Youngme Moon. 2000. Machines and Mindlessness: Social Responses to Computers.Journal of Social Issues56 (03 2000), 81–103. doi:10. 1111/0022-4537.00153

    work page arXiv 2000

  55. [55]

    Clifford Nass, Jonathan Steuer, and Ellen R. Tauber. 1994. Computers are social actors. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(Boston, Massachusetts, USA)(CHI ’94). Association for Computing Machinery, New York, NY, USA, 72–78. doi:10.1145/191666.191703

    work page doi:10.1145/191666.191703 1994

  56. [56]

    2025.Neufast: AI Recruitment Platform

    Neufast. 2025.Neufast: AI Recruitment Platform. Neufast. Retrieved July 7, 2025 from https://www.neufast.com/

    work page 2025

  57. [57]

    Rock Yuren Pang, Hope Schroeder, Kynnedy Simone Smith, Solon Barocas, Ziang Xiao, Emily Tseng, and Danielle Bragg. 2025. Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Mach...

    work page doi:10.1145/3706598.3713726 2025

  58. [58]

    Peck, Jessica J

    Tabitha C. Peck, Jessica J. Good, and Katharina Seitz. 2021. Evidence of Racial Bias Using Immersive Virtual Reality: Analysis of Head and Hand Motions During Shooting Decisions.IEEE Transactions on Visualization and Computer Graphics 27, 5 (2021), 2502–2512. doi:10.1109/TVCG.2021.3067767

    work page doi:10.1109/tvcg.2021.3067767 2021

  59. [59]

    Kristi Pham, Krishna Chaitanya Rao Kathala, and Shashank Palakurthi. 2025. Reddit Sentiment Analysis on the Impact of AI Using VADER, TextBlob, and BERT. Procedia Computer Science258 (2025), 886–892. doi:10.1016/j.procs.2025.04.326 International Conference on Machine Learning and Data Engineering

    work page doi:10.1016/j.procs.2025.04.326 2025

  60. [60]

    Simon Provoost, Jeroen Ruwaard, Ward van Breda, Heleen Riper, and Tibor Bosse

    work page

  61. [61]

    Validating Automated Sentiment Analysis of Online Cognitive Behavioral Therapy Patient Texts: An Exploratory Study.Frontiers in Psychology10 (2019),

    work page 2019

  62. [62]

    doi:10.3389/fpsyg.2019.01065

    work page doi:10.3389/fpsyg.2019.01065 2019

  63. [63]

    Valerie Purdie-Vaughns and Richard P. Eibach. 2008. Intersectional Invisibility: The Distinctive Advantages and Disadvantages of Multiple Subordinate-Group Identities.Sex Roles59, 5 (01 Sep 2008), 377–391. doi:10.1007/s11199-008-9424-4

    work page doi:10.1007/s11199-008-9424-4 2008

  64. [64]

    Cassidy Pyle, Kat Roemmich, and Nazanin Andalibi. 2024. U.S. Job-Seekers’ Organizational Justice Perceptions of Emotion AI-Enabled Interviews.Proc. ACM Hum.-Comput. Interact.8, CSCW2, Article 454 (Nov. 2024), 42 pages. doi:10.1145/ 3686993

    work page 2024

  65. [65]

    Tereza Raisova. 2012. The Comparison between the Effectiveness of the Com- petency based Interview and the behavioral event interview.Human Resources Management & Ergonomics6, 1 (2012), 52–63

    work page 2012

  66. [66]

    Neil Rathi, Dan Jurafsky, and Kaitlyn Zhou. 2025. Humans overrely on over- confident language models, across languages. arXiv:2507.06306 [cs.CL] https: //arxiv.org/abs/2507.06306

    work page arXiv 2025

  67. [67]

    2025.Participant Quality Stats Explained

    RealEye. 2025.Participant Quality Stats Explained. RealEye. Retrieved August 21, 2025 from https://support.realeye.io/participant-quality-stats-explained

    work page 2025

  68. [68]

    1996.The Media Equation: How People Treat Com- puters, Television, and New Media Like Real People and Places

    Byron Reeves and Clifford Nass. 1996.The Media Equation: How People Treat Com- puters, Television, and New Media Like Real People and Places. CSLI Publications, Stanford, CA

    work page 1996

  69. [69]

    Afsheen Rezai. 2022. Fairness in classroom assessment: development and val- idation of a questionnaire.Language Testing in Asia12, 1 (01 Jun 2022), 17. doi:10.1186/s40468-022-00162-9

    work page doi:10.1186/s40468-022-00162-9 2022

  70. [70]

    Ann Marie Ryan and Robert E Ployhart. 2000. Applicants’ perceptions of selection procedures and decisions: a critical review and agenda for the future.Journal of Management26, 3 (2000), 565–606. doi:10.1016/S0149-2063(00)00041-6

    work page doi:10.1016/s0149-2063(00)00041-6 2000

  71. [71]

    Sayan Sacar, Cosmin Munteanu, Jaisie Sin, Christina Wei, Sergio Sayago, Wei Zhao, and Jenny Waycott. 2024. Designing Age-Inclusive Interfaces: Emerging Mobile, Conversational, and Generative AI to Support Interactions across the Life Span. InProceedings of the 6th ACM Conference on Conversational User Interfaces (Luxembourg, Luxembourg)(CUI ’24). Associat...

    work page doi:10.1145/3640794.3669998 2024

  72. [72]

    Fernando Salvetti, Barbara Bertagni, and Ianna Contardo. 2024. Fostering In- clusive Recruitment Interviews with Intelligent Digital Humans: A Diversity and Inclusion Training Initiative.International Journal of Advanced Corporate Learning (iJAC)17 (05 2024), 78–84. doi:10.3991/ijac.v17i3.45431

    work page doi:10.3991/ijac.v17i3.45431 2024

  73. [73]

    Rohan Charudatt Salvi and Nigel Bosch. 2025. Investigating Perception of Gender Stereotypes in Large Language Models: A Computational Grounded Theory Approach.ACM J. Responsib. Comput.2, 2, Article 9 (Aug. 2025), 29 pages. doi:10.1145/3737882

    work page doi:10.1145/3737882 2025

  74. [74]

    2025.spacytextblob: A TextBlob sentiment analysis pipeline compo- nent for spaCy

    Sam Edwardes. 2025.spacytextblob: A TextBlob sentiment analysis pipeline compo- nent for spaCy. spaCy. Retrieved August 21, 2025 from https://spacy.io/universe/ project/spacy-textblob

    work page 2025

  75. [75]

    Keith Edwards, and Rebecca E

    Ari Schlesinger, W. Keith Edwards, and Rebecca E. Grinter. 2017. Intersectional HCI: Engaging Identity through Gender, Race, and Class. InProceedings of the 2017 CHI Conference on Human Factors in Computing Systems(Denver, Colorado, USA)(CHI ’17). Association for Computing Machinery, New York, NY, USA, 5412–5427. doi:10.1145/3025453.3025766

    work page doi:10.1145/3025453.3025766 2017

  76. [76]

    Katie Seaborn. 2025. Social Identity in Human-Agent Interaction: A Primer.J. Hum.-Robot Interact.(Aug. 2025). doi:10.1145/3760500 Just Accepted

    work page doi:10.1145/3760500 2025

  77. [77]

    Ruoxi Shang, Gary Hsieh, and Chirag Shah. 2025. Trusting Your AI Agent Emo- tionally and Cognitively: Development and Validation of a Semantic Differential Scale for AI Trust. InProceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society(San Jose, California, USA)(AIES ’24). AAAI Press, Palo Alto, CA, USA, 1343–1356

    work page 2025

  78. [78]

    Daniel Shank. 2012. Perceived Justice and Reactions to Coercive Computers. Sociological Forum27 (06 2012), 372–391. doi:10.2307/23262113

    work page doi:10.2307/23262113 2012

  79. [79]

    Skitka, Jennifer Winquist, and Susan Hutchinson

    Linda J. Skitka, Jennifer Winquist, and Susan Hutchinson. 2003. Are Outcome Fairness and Outcome Favorability Distinguishable Psychological Constructs? A Meta-Analytic Review.Social Justice Research16, 4 (01 Dec 2003), 309–341. doi:10.1023/A:1026336131206

    work page doi:10.1023/a:1026336131206 2003

  80. [80]

    Vibha Soni. 2024. AI in Job Matching and Recruitment: Analyzing the Efficiency and Equity of Automated Hiring Processes. In2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), Vol. 1. IEEE, Chikkaballapur, India, 1–5. doi:10.1109/ICKECS61492.2024.10617325

    work page doi:10.1109/ickecs61492.2024.10617325 2024

Showing first 80 references.