From Opacity to Transparency: User Behavior and Downstream Effects in Algorithmic Evaluation

References

• Abbasi A, Parsons J, Pant G, Sheng ORL, Sarker S (2024) Pathways for design research on artificial intelligence. Inform. Systems Res. 35(2):441–459.Link, Google Scholar
• Aggarwal P, McGill AL (2007) Is that car smiling at me? Schema congruity as a basis for evaluating anthropomorphized products. J. Consumer Res. 34(4):468–479.Crossref, Google Scholar
• Agnew R (2013) When criminal coping is likely: An extension of general strain theory. Deviant Behav. 34(8):653–670.Crossref, Google Scholar
• Aguinis H, Villamor I, Ramani RS (2021) MTurk research: Review and recommendations. J. Management 47(4):823–837.Google Scholar
• Ajunwa I (2021) Automated video interviewing as the new phrenology. Berkeley Tech. Law J. 36(3):1173–1225.Google Scholar
• Albani A, Ambrosini F, Mancini G, Passini S, Biolcati R (2023) Trait emotional intelligence and self-regulated learning in university students during the COVID-19 pandemic: The mediation role of intolerance of uncertainty and COVID-19 perceived stress. Personality Individual Differences 203:111999.Crossref, Google Scholar
• Ananny M, Crawford K (2018) Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media Soc. 20(3):973–989.Crossref, Google Scholar
• Arrieta AB, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, García S, Gil-López S, Molina D, Benjamins R (2020) Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inform. Fusion 58:82–115.Crossref, Google Scholar
• Awad NF, Krishnan MS (2006) The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Quart. 30(1):13–28.Crossref, Google Scholar
• Basch JM, Melchers KG (2019) Fair and flexible?! Explanations can improve applicant reactions toward asynchronous video interviews. Personnel Assessment Decisions 5(3):2.Crossref, Google Scholar
• Basch JM, Melchers KG, Kegelmann J, Lieb L (2020) Smile for the camera! The role of social presence and impression management in perceptions of technology-mediated interviews. J. Managerial Psych. 35(4):285–299.Crossref, Google Scholar
• Bauer K, Gill A (2024) Mirror, mirror on the wall: Algorithmic assessments, transparency, and self-fulfilling prophecies. Inform. Systems Res. 35(1):226–248.Link, Google Scholar
• Bauer K, von Zahn M, Hinz O (2023) Expl(AI)ned: The impact of explainable artificial intelligence on users’ information processing. Inform. Systems Res. 34(4):1582–1602.Link, Google Scholar
• Bernstein ES (2012) The transparency paradox: A role for privacy in organizational learning and operational control. Admin. Sci. Quart. 57(2):181–216.Crossref, Google Scholar
• Binns R, Van Kleek M, Veale M, Lyngs U, Zhao J, Shadbolt N (2018) ‘It’s reducing a human being to a percentage’: Perceptions of justice in algorithmic decisions. Proc. 2018 CHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York), 377.Google Scholar
• Bonezzi A, Ostinelli M, Melzner J (2022) The human black-box: The illusion of understanding human better than algorithmic decision-making. J. Experiment. Psych. General 151(9):2250–2258.Crossref, Google Scholar
• Bourdage JS, Roulin N, Levashina J (2017) Impression management and faking in job interviews. Front. Psychol. 8:1294.Crossref, Google Scholar
• Bourdage JS, Roulin N, Tarraf R (2018) “I (might be) just that good”: Honest and deceptive impression management in employment interviews. Personnel Psych. 71(4):597–632.Crossref, Google Scholar
• Burt A (2019) The AI transparency paradox. Harvard Bus. Rev. (December 13), https://hbr.org/2019/12/the-ai-transparency-paradox.Google Scholar
• Chamorro-Premuzic T, Akhtar R (2019) Should companies use AI to assess job candidates? Harvard Bus. Rev. (May 17), https://hbr.org/2019/05/should-companies-use-ai-to-assess-job-candidates.Google Scholar
• Choudhury P, Wang D, Carlson NA, Khanna T (2019) Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles. Strategic Management J. 40(11):1705–1732.Crossref, Google Scholar
• Chun JS, De Cremer D, Oh E-J, Kim Y (2024) What algorithmic evaluation fails to deliver: Respectful treatment and individualized consideration. Sci. Rep. 14(1):25996.Crossref, Google Scholar
• Colquitt JA (2008) From the Editors publishing laboratory research in AMJ: A question of when, not if. Acad. Management J. 51(4):616–620.Google Scholar
• Cumming G (2014) The new statistics: Why and how. Psych. Sci. 25(1):7–29.Crossref, Google Scholar
• DePaulo BM, Lindsay JJ, Malone BE, Muhlenbruck L, Charlton K, Cooper H (2003) Cues to deception. Psych. Bull. 129(1):74–118.Crossref, Google Scholar
• Dhinakaran A (2021) Overcoming AI transparency paradox. Forbes (September 10), https://www.forbes.com/sites/aparnadhinakaran/2021/09/10/overcoming-ais-transparency-paradox/.Google Scholar
• Ehsan U, Liao QV, Muller M, Riedl MO, Weisz JD (2021) Expanding explainability: Towards social transparency in AI systems. Proc. 2021 CHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York), 82.Google Scholar
• Ekman P (1997) Should we call it expression or communication? Innovation Eur. J. Soc. Sci. Res. 10(4):333–344.Crossref, Google Scholar
• Ekman P, O’Sullivan M (2006) From flawed self-assessment to blatant whoppers: The utility of voluntary and involuntary behavior in detecting deception. Behav. Sci. Law. 24(5):673–686.Crossref, Google Scholar
• Ekman P, Friesen WV, O’Sullivan M (1988) Smiles when lying. J. Personality Soc. Psych. 54(3):414–420.Crossref, Google Scholar
• Erickson T, Kellogg WA (2000) Social translucence: An approach to designing systems that support social processes. ACM Trans. Comput. Human Interaction 7(1):59–83.Crossref, Google Scholar
• Feeney JR, McCarthy JM, Goffin R (2015) Applicant anxiety: Examining the sex‐linked anxiety coping theory in job interview contexts. Internat. J. Selection Assessment 23(3):295–305.Crossref, Google Scholar
• Feiler AR, Powell DM (2016) Behavioral expression of job interview anxiety. J. Bus. Psych. 31(1):155–171.Crossref, Google Scholar
• Gierlich-Joas M, Baiyere A, Hess T (2024) Inverse transparency and the quest for empowerment through the design of digital workplace technologies. J. Assoc. Inform. Systems 25(5):1212–1239.Google Scholar
• Gillespie T (2020) Content moderation, AI, and the question of scale. Big Data Soc. 7(2):1–5.Crossref, Google Scholar
• Glikson E, Woolley AW (2020) Human trust in artificial intelligence: Review of empirical research. Acad. Management Ann. 14(2):627–660.Crossref, Google Scholar
• Goldsmith DJ (2001) A normative approach to the study of uncertainty and communication. J. Comm. 51(3):514–533.Crossref, Google Scholar
• Gorwa R, Binns R, Katzenbach C (2020) Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data Soc. 7(1):1–15.Crossref, Google Scholar
• Grote G, Parker SK, Crowston K (2026) Taming artificial intelligence: A theory of control-accountability alignment among AI developers and users. Acad. Management Rev. 51(2):278–299.Crossref, Google Scholar
• Guadagno RE, Cialdini RB (2007) Persuade him by email, but see her in person: Online persuasion revisited. Comput. Human Behav. 23(2):999–1015.Crossref, Google Scholar
• Guerin B (1999) Social behaviors as determined by different arrangements of social consequences: Social loafing, social facilitation, deindividuation, and a modified social loafing. Psych. Record 49:565–577.Crossref, Google Scholar
• Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D (2018) A survey of methods for explaining black box models. ACM Comput. Surveys 51(5):93.Google Scholar
• Gutwin C, Greenberg S (2002) A descriptive framework of workspace awareness for real-time groupware. Comput. Supported Cooperative Work CSCW 11(3–4):411–446.Crossref, Google Scholar
• Henchy T, Glass DC (1968) Evaluation apprehension and the social facilitation of dominant and subordinate responses. J. Personality Soc. Psych. 10(4):446–454.Crossref, Google Scholar
• Hosanagar K, Jair V (2018) We need transparency in algorithms, but too much can backfire. Harvard Bus. Rev. (July 23), https://hbr.org/2018/07/we-need-transparency-in-algorithms-but-too-much-can-backfire.Google Scholar
• Jacksch V, Klehe UC (2016) Unintended consequences of transparency during personnel selection: Benefitting some candidates, but harming others? Internat. J. Selection Assessment 24(1):4–13.Crossref, Google Scholar
• James TL, Wallace L, Deane JK (2019) Using organismic integration theory to explore the associations between users’ exercise motivations and fitness technology feature set use. MIS Quart. 43(1):287–312.Crossref, Google Scholar
• Jhaver S, Karpfen Y, Antin J (2018) Algorithmic anxiety and coping strategies of Airbnb hosts. Proc. 2018 CHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York), 421.Google Scholar
• Kellogg KC, Valentine MA, Christin A (2020) Algorithms at work: The new contested terrain of control. Acad. Management Ann. 14(1):366–410.Crossref, Google Scholar
• Kizilcec RF (2016) How much information? Effects of transparency on trust in an algorithmic interface. Proc. 2016 CHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York), 2390–2395.Google Scholar
• König CJ, Melchers KG, Kleinmann M, Richter GM, Klehe UC (2007) Candidates’ ability to identify criteria in nontransparent selection procedures: Evidence from an assessment center and a structured interview. Internat. J. Selection Assessment 15(3):283–292.Crossref, Google Scholar
• Lakhiwal A, Bala H, Léger P-M (2023) Ambivalence is better than indifference: Behavioral and neurophysiological assessment of ambivalence in online environments. MIS Quart. 47(2):705–732.Crossref, Google Scholar
• Langer M, König CJ, Fitili A (2018) Information as a double-edged sword: The role of computer experience and information on applicant reactions towards novel technologies for personnel selection. Comput. Human Behav. 81:19–30.Crossref, Google Scholar
• Langer M, Baum K, König CJ, Hähne V, Oster D, Speith T (2021) Spare me the details: How the type of information about automated interviews influences applicant reactions. Internat. J. Selection Assessment 29(2):154–169.Crossref, Google Scholar
• Larson RB (2019) Controlling social desirability bias. Internat. J. Market Res. 61(5):534–547.Crossref, Google Scholar
• Lebovitz S, Lifshitz-Assaf H, Levina N (2022) To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organ. Sci. 33(1):126–148.Link, Google Scholar
• Lee MK (2018) Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data Soc. 5(1):1–16.Crossref, Google Scholar
• Lehmann CA, Haubitz CB, Fügener A, Thonemann UW (2021) The risk of algorithm transparency: How algorithm complexity drives the effects on use of advice. Production Oper. Management 31(9):3419–3434.Crossref, Google Scholar
• Leonardi P (2023) Helping employees succeed with generative AI. Harvard Bus. Rev. (November/December), https://hbr.org/2023/11/helping-employees-succeed-with-generative-ai.Google Scholar
• LePine JA, LePine MA, Jackson CL (2004) Challenge and hindrance stress: Relationships with exhaustion, motivation to learn, and learning performance. J. Appl. Psych. 89(5):883–891.Crossref, Google Scholar
• Levashina J, Campion MA (2007) Measuring faking in the employment interview: Development and validation of an interview faking behavior scale. J. Appl. Psych. 92(6):1638–1656.Crossref, Google Scholar
• Liebman N, Gergle D (2016) It’s (not) simply a matter of time: The relationship between CMC Cues and interpersonal affinity. Proc. 19th ACM Conf. Comput. Supported Cooperative Work Social Comput. (Association for Computing Machinery, New York), 570–581.Google Scholar
• Lipton ZC (2018) The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3):31–57.Crossref, Google Scholar
• Lukacik E-R, Bourdage JS, Roulin N (2022) Into the void: A conceptual model and research agenda for the design and use of asynchronous video interviews. Human Resource Management Rev. 32(1):100789.Crossref, Google Scholar
• MacKinnon DP, Fairchild AJ, Fritz MS (2007) Mediation analysis. Annu. Rev. Psych. 58(1):593–614.Crossref, Google Scholar
• McCarthy J, Goffin R (2004) Measuring job interview anxiety: Beyond weak knees and sweaty palms. Personnel Psych. 57(3):607–637.Crossref, Google Scholar
• McCarthy JM, Truxillo DM, Bauer TN, Erdogan B, Shao Y, Wang M, Liff J, Gardner C (2021) Distressed and distracted by COVID-19 during high-stakes virtual interviews: The role of job interview anxiety on performance and reactions. J. Appl. Psych. 106(8):1103–1117.Crossref, Google Scholar
• McDermott R, Hatemi PK (2020) Ethics in field experimentation: A call to establish new standards to protect the public from unwanted manipulation and real harms. Proc. Natl. Acad. Sci. USA 117(48):30014–30021.Crossref, Google Scholar
• Mendoza SA, Gollwitzer PM, Amodio DM (2010) Reducing the expression of implicit stereotypes: Reflexive control through implementation intentions. Personality Soc. Psych. Bull. 36(4):512–523.Crossref, Google Scholar
• Meub L, Proeger TE (2015) Anchoring in social context. J. Behav. Experiment. Econom. 55:29–39.Crossref, Google Scholar
• Miller T (2019) Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267:1–38.Crossref, Google Scholar
• Mirowska A, Mesnet L (2022) Preferring the devil you know: Potential applicant reactions to artificial intelligence evaluation of interviews. Human Resource Management J. 32(2):364–383.Crossref, Google Scholar
• Möhlmann M, Alves de Lima Salge C, Marabelli M (2023) Algorithm sensemaking: How platform workers make sense of algorithmic management. J. Assoc. Inform. Systems 24(1):35–64.Google Scholar
• Möhlmann M, Zalmanson L, Henfridsson O, Gregory RW (2021) Algorithmic management of work on online labor platforms: When matching meets control. MIS Quart. 45(4):1999–2022.Crossref, Google Scholar
• Mosier KL, Skitka LJ, Burdick MD, Heers ST (1996) Automation bias, accountability, and verification behaviors. Proc. Human Factors Ergonomics Soc. Annual Meeting (SAGE Publications, Los Angeles), 204–208.Google Scholar
• O’Boyle EH Jr, Banks GC, Gonzalez-Mulé E (2017) The chrysalis effect: How ugly initial results metamorphosize into beautiful articles. J. Management 43(2):376–399.Google Scholar
• Ochmann J, Michels L, Tiefenbeck V, Maier C, Laumer S (2024) Perceived algorithmic fairness: An empirical study of transparency and anthropomorphism in algorithmic recruiting. Inform. Systems J. 34(2):384–414.Crossref, Google Scholar
• Ong AD, Bergeman CS, Bisconti TL, Wallace KA (2006) Psychological resilience, positive emotions, and successful adaptation to stress in later life. J. Personality Soc. Psych. 91(4):730–749.Crossref, Google Scholar
• Palan S, Schitter C (2018) Prolific.ac—A subject pool for online experiments. J. Behav. Experiment. Finance 17:22–27.Crossref, Google Scholar
• Peck JA, Levashina J (2017) Impression management and interview and job performance ratings: A meta-analysis of research design with tactics in mind. Front. Psych. 8:201.Google Scholar
• Phipps ST, Prieto LC, Deis MH (2015) The role of personality in organizational citizenship behavior: Introducing counterproductive work behavior and integrating impression management as a moderating factor. J. Organ. Culture Comm. Conflict 19(1):176–184.Google Scholar
• Porter S, Ten Brinke L, Wallace B (2012) Secrets and lies: Involuntary leakage in deceptive facial expressions as a function of emotional intensity. J. Nonverbal Behav. 36(1):23–37.Crossref, Google Scholar
• Powell DM, Bourdage JS, Bonaccio S (2021) Shake and fake: The role of interview anxiety in deceptive impression management. J. Bus. Psych. 36(5):829–840.Crossref, Google Scholar
• Rai A (2020) Explainable AI: From black box to glass box. J. Acad. Marketing Sci. 48(1):137–141.Crossref, Google Scholar
• Rai A, Constantinides P, Sarker S (2019) Next generation digital platforms: Toward human-AI hybrids. MIS Quart. 43(1):iii–iix.Crossref, Google Scholar
• Raveendhran R, Fast NJ, Carnevale PJ (2020) Virtual (freedom from) reality: Evaluation apprehension and leaders’ preference for communicating through avatars. Comput. Human Behav. 111:106415.Crossref, Google Scholar
• Rhue L (2024) The anchoring effect, algorithmic fairness, and the limits of information transparency for emotion artificial intelligence. Inform. Systems Res. 35(3):1479–1496.Link, Google Scholar
• Roulin N (2016) Individual differences predicting impression management detection in job interviews. Personnel Assessment Decisions 2(1):1.Crossref, Google Scholar
• Roulin N, Powell DM (2018) Identifying applicant faking in job interviews. J. Personnel Psych. 17(3):143–154.Crossref, Google Scholar
• Roulin N, Bangerter A, Levashina J (2014) Interviewers’ perceptions of impression management in employment interviews. J. Managerial Psych. 29(2):141–163.Crossref, Google Scholar
• Roulin N, Bangerter A, Levashina J (2015) Honest and deceptive impression management in the employment interview: Can it be detected and how does it impact evaluations? Personnel Psych. 68(2):395–444.Crossref, Google Scholar
• Sánchez-Monedero J, Dencik L, Edwards L (2020) What does it mean to ‘solve’ the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems. Proc. 2020 Conf. Fairness Accountability Transparency (Association for Computing Machinery, New York), 458–468.Google Scholar
• Schilke O, Reimann M (2025) The transparency dilemma: How AI disclosure erodes trust. Organ. Behav. Human Decision Processes 188:104405.Crossref, Google Scholar
• Schuler MS, Coffman DL, Stuart EA, Nguyen TQ, Vegetabile B, McCaffrey DF (2025) Practical challenges in mediation analysis: A guide for applied researchers. Health Services Outcomes Res. Methodology 25(1):57–84.Crossref, Google Scholar
• Serra-Garcia M, Gneezy U (2025) Improving human deception detection using algorithmic feedback. Management Sci. 71(12):10289–10307.Link, Google Scholar
• Shaha M, Pawar M (2018) Transfer learning for image classification. 2018 Second Internat. Conf. Electronics Commun. Aerospace Tech. ICECA (IEEE, Piscataway, NJ), 656–660.Google Scholar
• Stahl A (2021) How AI will impact the future of work and life. Forbes (March 10), https://www.forbes.com/sites/ashleystahl/2021/03/10/how-ai-will-impact-the-future-of-work-and-life/.Google Scholar
• Stohl C, Stohl M, Leonardi PM (2016) Managing opacity: Information visibility and the paradox of transparency in the digital age. Internat. J. Comm. 10:123–137.Google Scholar
• Stuart HC, Dabbish L, Kiesler S, Kinnaird P, Kang R (2012) Social transparency in networked information exchange: A theoretical framework. Proc. ACM 2012 Conf. Comput. Supported Cooperative Work (Association for Computing Machinery, New York), 451–460.Google Scholar
• Suen H-Y, Hung K-E (2024) Revealing the influence of AI and its interfaces on job candidates’ honest and deceptive impression management in asynchronous video interviews. Tech. Forecasting Soc. Change 198:123011.Crossref, Google Scholar
• Tambe P, Cappelli P, Yakubovich V (2019) Artificial intelligence in human resources management: Challenges and a path forward. California Management Rev. 61(4):15–42.Crossref, Google Scholar
• Uziel L (2007) Individual differences in the social facilitation effect: A review and meta-analysis. J. Res. Personality 41(3):579–601.Crossref, Google Scholar
• Vohs KD, Baumeister RF, Ciarocco NJ (2005) Self-regulation and self-presentation: Regulatory resource depletion impairs impression management and effortful self-presentation depletes regulatory resources. J. Personality Soc. Psych. 88(4):632–657.Crossref, Google Scholar
• Vrij A (2005) Criteria-based content analysis: A qualitative review of the first 37 studies. Psych. Public Policy Law 11(1):3–41.Crossref, Google Scholar
• Wang Q, Huang Y, Jasin S, Singh PV (2023) Algorithmic transparency with strategic users. Management Sci. 69(4):2297–2317.Link, Google Scholar
• Woods S, Dautenhahn K, Kaouri C (2005) Is someone watching me? Consideration of social facilitation effects in human-robot interaction experiments. 2005 Internat. Sympos. Comput. Intelligence Robotics Automation (IEEE, Piscataway, NJ), 53–60.Google Scholar
• You S, Yang CL, Li X (2022) Algorithmic versus human advice: Does presenting prediction performance matter for algorithm appreciation? J. Management Inform. Systems 39(2):336–365.Crossref, Google Scholar
• Zuiderwijk A, Janssen M, Dwivedi YK (2015) Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government Inform. Quart. 32(4):429–440.Crossref, Google Scholar