The Power of Disagreement: A Field Experiment to Investigate Human–Algorithm Collaboration in Loan Evaluations

References

• Abdel-Karim BM, Pfeuffer N, Carl KV, Hinz O (2023) How AI-based systems can induce reflections: The case of AI-augmented diagnostic work. MIS Quart. 47(4):1395–1424.Crossref, Google Scholar
• Allen R, Choudhury P (2022) Algorithm-augmented work and domain experience: The countervailing forces of ability and aversion. Organ. Sci. 33(1):149–169.Link, Google Scholar
• Amit A, Sagiv L (2013) The role of epistemic motivation in individuals’ response to decision complexity. Organ. Behav. Human Decision Processes 121(1):104–117.Crossref, Google Scholar
• Bai B, Dai H, Zhang DJ, Zhang F, Hu H (2022) The impacts of algorithmic work assignment on fairness perceptions and productivity: Evidence from field experiments. Manufacturing Service Oper. Management 24(6):3060–3078.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
• Brynjolfsson E, Mitchell T (2017) What can machine learning do? Workforce implications. Science 358(6370):1530–1534.Crossref, Google Scholar
• Buçinca Z, Barbara Malaya M, Gajos KZ (2021) To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proc. ACM Human Comput. Interaction 5:1–21.Crossref, Google Scholar
• Cabitza F, Campagner A, Angius R, Natali C, Reverberi C (2023) AI shall have no dominion: On how to measure technology dominance in AI-supported human decision-making. Shaer O, Isbister K, Fitzpatrick G, Yatani K, eds. Proc. 2023 CHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York), 1–20.Google Scholar
• Camerer CF (2018) Artificial intelligence and behavioral economics. Agrawal A, Gans J, Goldfarb A, eds. The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago, IL), 587–608.Google Scholar
• Cao S, Jiang W, Wang JL, Yang B (2024) From man vs. machine to man + machine: The art and AI of stock analyses. J. Financial Econ. 160:103910.Google Scholar
• Castelo N, Bos MW, Lehmann DR (2019) Task-dependent algorithm aversion. J. Marketing Res. 56(5):809–825.Crossref, Google Scholar
• Chapman LJ, Chapman JP (1967) Genesis of popular but erroneous psychodiagnostic observations. J. Abnormal Psych. 72(3):193–204.Crossref, Google Scholar
• Chen Z, Chan J (2024) Large language model in creative work: The role of collaboration modality and user expertise. Management Sci. 70(12):9101–9117.Google Scholar
• Chen V, Liao QV, Wortman Vaughan J, Bansal G (2023) Understanding the role of human intuition on reliance in human-AI decision-making with explanations. Preprint, submitted January 18, https://arxiv.org/abs/2301.07255.Google Scholar
• Cheng X, Zhang X, Cohen J, Mou J (2022) Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms. Inform. Processing Management 59(3):102940.Crossref, Google Scholar
• Choudhury P, Starr E, Agarwal R (2020) Machine learning and human capital complementarities: Experimental evidence on bias mitigation. Strategic Management J. 41(8):1381–1411.Crossref, Google Scholar
• Coleman PT, Ferguson R (2014) Making Conflict Work: Harnessing the Power of Disagreement (Houghton Mifflin Harcourt, Boston).Google Scholar
• Correia A-P (2020) Dealing with conflict in learning teams immersed in technology-rich environments: A mixed-methods study. Ed. Inform. Tech. 25(3):2049–2071.Crossref, Google Scholar
• De Langhe B, Puntoni S, Larrick RP (2017) Linear thinking in a nonlinear world. Harvard Bus. Rev. 2017:11.Google Scholar
• Diether KB, Malloy CJ, Scherbina A (2002) Differences of opinion and the cross section of stock returns. J. Finance 57(5):2113–2141.Crossref, Google Scholar
• Dietvorst BJ, Simmons JP, Massey C (2015) Algorithm aversion: People erroneously avoid algorithms after seeing them err. J. Experiment. Psych. General 144(1):114–126.Crossref, Google Scholar
• Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Human Factors 37(1):85–104.Google Scholar
• Ferrario A, Loi M (2022) How explainability contributes to trust in AI. Gummadi KP, Katell M, Levy K-Y, eds. Proc. 2022 ACM Conf. Fairness Accountability Transparency (Association for Computing Machinery, New York), 1457–1466.Google Scholar
• Fu R, Huang Y, Singh PV (2021) Crowds, lending, machine, and bias. Inform. Systems Res. 32(1):72–92.Link, Google Scholar
• Fügener A, Grahl J, Gupta A, Ketter W (2021) Will humans-in-the-loop become borgs? Merits and pitfalls of working with AI. MIS Quart. 45(3b):1527–1556.Crossref, Google Scholar
• Fügener A, Grahl J, Gupta A, Ketter W (2022) Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation. Inform. Systems Res. 33(2):678–696.Link, Google Scholar
• Goh KH, Wang L, Yeow AYK, Poh H, Li K, Yeow JJL, Tan GYH (2021) Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nature Comm. 12(1):1–10.Crossref, Google Scholar
• Goldberg LR (1970) Man versus model of man: A rationale, plus some evidence, for a method of improving on clinical inferences. Psych. Bull. 73(6):422–432.Crossref, Google Scholar
• Goodyear K, Parasuraman R, Chernyak S, Madhavan P, Deshpande G, Krueger F (2016) Advice taking from humans and machines: An fMRI and effective connectivity study. Frontiers Human Neuroscience 10:542.Crossref, Google Scholar
• Hamm P, Felix Wittmann H, Klesel M (2021) Explain it to me and I will use it: A proposal on the impact of explainable AI on use behavior. Proc. 2021 Int. Conf. Inform. Systems (ICIS), vol. 9 (Association for Information Systems, Austin, TX).Google Scholar
• Hartley J, Sørensen E, Torfing J (2013) Collaborative innovation: A viable alternative to market competition and organizational entrepreneurship. Public Admin. Rev. 73(6):821–830.Crossref, Google Scholar
• Heaven WD (2021) Bias isn’t the only problem with credit scores—And no, AI can’t help. MIT Technology Review Online (June 17), https://www.technologyreview.com/2021/06/17/1026519/racial-bias-noisy-data-credit-scores-mortgage-loans-fairness-machine-learning/.Google Scholar
• Hodges BH, Geyer AL (2006) A nonconformist account of the Asch experiments: Values, pragmatics, and moral dilemmas. Personality Soc. Psych. Rev. 10(1):2–19.Crossref, Google Scholar
• Hou T, Li M, Tan Y, Zhao H (2024) Physician adoption of AI assistant. Manufacturing Service Oper. Management 26(5):1639–1655.Google Scholar
• Hu X, Huang Y, Li B, Lu T (2025) Human–algorithmic bias: Source, evolution, and impact. Management Sci. Forthcoming.Google Scholar
• Ibrahim R, Kim S-H, Tong J (2021) Eliciting human judgment for prediction algorithms. Management Sci. 67(4):2314–2325.Link, Google Scholar
• Jacovi A, Marasović A, Miller T, Goldberg Y (2021) Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in AI. Wilson C, Hoffmann A, eds. Proc. 2021 ACM Conf. Fairness Accountability Transparency (Association for Computing Machinery, New York), 624–635.Google Scholar
• John-Mathews J-M (2021) Critical empirical study on black-box explanations in AI. Preprint, submitted September 29, https://arxiv.org/abs/2109.15067.Google Scholar
• Jussupow E, Spohrer K, Heinzl A, Gawlitza J (2021) Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence. Inform. Systems Res. 32(3):713–735.Link, Google Scholar
• Kilduff GJ, Willer R, Anderson C (2016) Hierarchy and its discontents: Status disagreement leads to withdrawal of contribution and lower group performance. Organ. Sci. 27(2):373–390.Link, Google Scholar
• Krakowski S, Luger J, Raisch S (2022) Artificial intelligence and the changing sources of competitive advantage. Strategic Management J. 44(6):1425–1452.Crossref, Google Scholar
• Kumar P, Sharma M (2022) Data, machine learning, and human domain experts: None is better than their collaboration. Internat. J. Human Comput. Interaction 38(14):1307–1320.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
• Leenders RTA, Van Engelen JM, Kratzer J (2007) Systematic design methods and the creative performance of new product teams: Do they contradict or complement each other? J. Product Innovation Management 24(2):166–179.Crossref, Google Scholar
• Lehmann CA, Haubitz CB, Fügener A, Thonemann UW (2022) The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice. Production Oper. Management 31(9):3419–3434.Crossref, Google Scholar
• Liu M, Tang X, Xia S, Zhang S, Zhu Y, Meng Q (2023) Algorithm aversion: Evidence from ridesharing drivers. Management Sci., ePub ahead of print October 3, https://doi.org/10.1287/mnsc.2022.02475.Google Scholar
• Logg JM, Minson JA, Moore DA (2019) Algorithm appreciation: People prefer algorithmic to human judgment. Organ. Behav. Human Decision Processes 151:90–103.Crossref, Google Scholar
• Lu T, Zhang Y (2025) 1+ 1 > 2? Information, humans, and machines. Inform. Systems Res. 36(1):394–418.Link, Google Scholar
• Lu T, Li B, Zhang Y (2023) Profit vs. equality? The case of financial risk assessment and a new perspective on alternative data. MIS Quart. 47(4):1517–1556.Crossref, Google Scholar
• Lu J, Lee D, Kim TW, Danks D (2020) Good explanation for algorithmic transparency. Preprint, submitted January 7, https://dx.doi.org/10.2139/ssrn.3503603.Google Scholar
• Lu T, Lu X, Huang Y, Wang H (2025) Augmented algorithms, adaptive humans? Evidence from a natural experiment. Preprint, submitted June 15, https://dx.doi.org/10.2139/ssrn.4298793.Google Scholar
• Luo X, Shaojun Qin M, Fang Z, Qu Z (2021) Artificial intelligence coaches for sales agents: Caveats and solutions. J. Marketing 85(2):14–32.Crossref, Google Scholar
• Ma L, Zhao X, Zhou Z, Liu Y (2018) A new aspect on P2P online lending default prediction using meta-level phone usage data in China. Decision Support Systems 111:60–71.Crossref, Google Scholar
• Martinez A (2019) Considering AI in hiring? As its use grows, so do the legal implications for employers. Forbes Online (December 5), https://www.forbes.com/sites/alonzomartinez/2019/12/05/considering-ai-in-hiring-as-its-use-grows-so-do-the-legal-implications-for-employers/.Google Scholar
• Mehnen L, Gruarin S, Vasileva M, Knapp B (2023) ChatGPT as a medical doctor? A diagnostic accuracy study on common and rare diseases. Preprint, submitted April 27, https://www.medrxiv.org/content/10.1101/2023.04.20.23288859v2.Google Scholar
• Miller T (2019) Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267:1–38.Crossref, Google Scholar
• Mohseni S, Yang F, Pentyala SK, Du M, Liu Y, Lupfer N, Hu X, Ji S, Ragan ED (2021) Machine learning explanations to prevent overtrust in fake news detection. Proc. Internat. AAAI Conf. Web Soc. Media, 421–431.Google Scholar
• Osser DN (2009) Why physicians do not follow some guidelines and algorithms. Drug Benefit Trends 21(11).Google Scholar
• Palazzolo J (2016) Court: Judges can consider predictive algorithms in sentencing. The Wall Street Journal Online (July 13), https://www.wsj.com/articles/BL-LB-54145.Google Scholar
• Paletz SB, Chan J, Schunn CD (2016) Uncovering uncertainty through disagreement. Appl. Cognitive Psych. 30(3):387–400.Crossref, Google Scholar
• Renwick PA (1975) Impact of topic and source of disagreement on conflict management. Organ. Behav. Human Performance 14(3):416–425.Crossref, Google Scholar
• Sammy N, Anstiss PA, Moore LJ, Freeman P, Wilson MR, Vine SJ (2017) The effects of arousal reappraisal on stress responses, performance and attention. Anxiety Stress Coping 30(6):619–629.Crossref, Google Scholar
• Sanneman L, Shah JA (2022) The situation awareness framework for explainable AI (SAFE-AI) and human factors considerations for XAI systems. Internat. J. Human Comput. Interaction 38(18–20):1772–1788.Crossref, Google Scholar
• Satariano A, Pifarré RT (2024) An algorithm told police she was safe. Then her husband killed her. The New York Times Online (July 18), https://www.nytimes.com/interactive/2024/07/18/technology/spain-domestic-violence-viogen-algorithm.html.Google Scholar
• Siau K, Wang W (2018) Building trust in artificial intelligence, machine learning, and robotics. Cutter Bus. Tech. J. 31(2):47–53.Google Scholar
• Strandberg K, Himmelroos S, Grönlund K (2019) Do discussions in like-minded groups necessarily lead to more extreme opinions? Deliberative democracy and group polarization. Internat. Political Sci. Rev. 40(1):41–57.Crossref, Google Scholar
• Sun J, Zhang DJ, Hu H, Van Mieghem JA (2021) Predicting human discretion to adjust algorithmic prescription: A large-scale field experiment in warehouse operations. Management Sci. 68(2):846–865.Link, Google Scholar
• Tanjitpiyanond P, Jetten J, Peters K (2022) How economic inequality shapes social class stereotyping. J. Experiment. Soc. Psych. 98:104248.Crossref, Google Scholar
• Terry M, Kulkarni C, Wattenberg M, Dixon L, Morris MR (2023) AI alignment in the design of interactive AI: Specification alignment, process alignment, and evaluation support. Preprint, submitted October 23, https://arxiv.org/abs/2311.00710.Google Scholar
• Thomas KW (1992) Conflict and negotiation processes in organizations. Dunnette MD, Hough LM, eds. Handbook of Industrial and Organizational Psychology (Consulting Psychologists Press, Palo Alto, CA).Google Scholar
• von Walter B, Kremmel D, Jäger B (2022) The impact of lay beliefs about AI on adoption of algorithmic advice. Marketing Lett. 33(1):143–155.Crossref, Google Scholar
• Wang W, Gao G, Agarwal R (2023) Friend or foe? Teaming between artificial intelligence and workers with variation in experience. Management Sci. 70(9):5753–5775.Google Scholar
• Watney C (2017) When it comes to criminal justice AI, we need transparency and accountability. R Street Online (December 1), https://www.rstreet.org/commentary/when-it-comes-to-criminal-justice-ai-we-need-transparency-and-accountability/.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
• Zeide E (2019) Artificial intelligence in higher education: Applications, promise and perils, and ethical questions. EDUCAUSE Review Online (August 26), https://er.educause.edu/articles/2019/8/artificial-intelligence-in-higher-education-applications-promise-and-perils-and-ethical-questions.Google Scholar
• Zhou J, Wang C, Ren F, Chen G (2021) Inferring multi-stage risk for online consumer credit services: An integrated scheme using data augmentation and model enhancement. Decision Support Systems 149:113611.Crossref, Google Scholar