Friend or Foe? Teaming Between Artificial Intelligence and Workers with Variation in Experience

References

• Agrawal AK, Gans JS, Goldfarb A (2021) AI adoption and system-wide change. NBER Working Paper No. 28811, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Ahn D, Almaatouq A, Gulabani M, Hosanagar K (2021) Will we trust what we don’t understand? Impact of model interpretability and outcome feedback on trust in AI. Preprint, submitted November 16, https://arxiv.org/abs/2111.08222.Google Scholar
• AI HLEG (2019) Ethics guidelines for trustworthy AI. Report No. B-1049, Brussels.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
• Appari A, Johnson ME, Anthony DL (2018) Health IT and inappropriate utilization of outpatient imaging: A cross-sectional study of US hospitals. Internat. J. Medical Informatics 109:87–95.Crossref, Google Scholar
• Atasoy H, Chen PY, Ganju K (2017) The spillover effects of health IT investments on regional healthcare costs. Management Sci. 64(6):2515–2534.Link, Google Scholar
• Bai B, Dai H, Zhang D, 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.Google Scholar
• Baniecki H, Kretowicz W, Piatyszek P, Wisniewski J, Biecek P (2021) Dalex: Responsible machine learning with interactive explainability and fairness in Python. J. Machine Learn. Res. 22(214):1–7.Google Scholar
• Bansal G, Nushi B, Kamar E, Weld DS, Lasecki WS, Horvitz E (2019) Updates in human-AI teams: Understanding and addressing the performance/compatibility tradeoff. Proc. Conf. AAAI Artificial Intelligence 33(01):2429–2437.Crossref, Google Scholar
• Bhatt U, Andrus M, Weller A, Xiang A (2020) Machine learning explainability for external stakeholders. Preprint, submitted July 10, https://arxiv.org/abs/2007.05408.Google Scholar
• Bogert E, Schecter A, Watson RT (2021) Humans rely more on algorithms than social influence as a task becomes more difficult. Sci. Rep. 11(1):1–9.Crossref, Google Scholar
• Borman WC, Hanson MA, Oppler SH, Pulakos ED, White LA (1993) Role of early supervisory experience in supervisor performance. J. Appl. Psych. 78(3):443–449.Crossref, Google Scholar
• Bradley JV (1981) Overconfidence in ignorant experts. Bull. Psychonomic Soc. 17(2):82–84.Crossref, Google Scholar
• Bresnahan T (2021) Artificial intelligence technologies and aggregate growth prospects. Diamond J, Zodrow G, eds. Prospects for Economic Growth in the United States (Cambridge University Press, Cambridge, UK), 132–152.Crossref, Google Scholar
• Bresnahan TF, Brynjolfsson E, Hitt LM (2002) Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence. Quart. J. Econom. 117(1):339–376.Crossref, Google Scholar
• Brynjolfsson E (2022) The Turing trap: The promise & peril of human-like artificial intelligence. Daedalus 151(2):272–287.Google Scholar
• Brynjolfsson E, Hui X, Liu M (2018a) Does machine translation affect international trade? Evidence from a large digital platform. NBER Working Paper No. 24917, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Brynjolfsson E, Rock D, Syverson C (2018b) Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. Agrawa A, Gans J, Goldfarb A, eds. The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago), 23–57.Google Scholar
• Brynjolfsson E, Rock D, Syverson C (2019) Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. Agrawa A, Gans J, Goldfarb A, eds. The Economics of Artificial Intelligence (University of Chicago Press, Chicago), 23–60.Crossref, Google Scholar
• Bundorf MK, Polyakova M, Tai-Seale M (2022) How do humans interact with algorithms? Experimental evidence from health insurance. NBER Working Paper No. 25976, National Bureau of Economic Research, Cambridge, MA.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
• Card D, Krueger AB (1993) Minimum wages and employment: A case study of the fast food industry in New Jersey and Pennsylvania. NBER Working Paper No. 4509, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Carroll M, Shah R, Ho MK, Griffiths T, Seshia S, Abbeel P, Dragan A (2019) On the utility of learning about humans for human-AI coordination. Adv. Neural Inform. Processing Systems, vol. 32, 5174–5185.Google Scholar
• Case A, Deaton A (2017) Mortality and morbidity in the 21st century. Brookings Papers Econom. Activity 2017:397–476.Crossref, Google Scholar
• Cellan-Jones R (2014) Stephen Hawking warns artificial intelligence could end mankind. BBC News (December 2), https://www.bbc.com/news/technology-30290540.Google Scholar
• Chakraborti T, Kambhampati S, Scheutz M, Zhang Y (2017) AI challenges in human-robot cognitive teaming. Preprint, submitted July 15, https://arxiv.org/abs/1707.04775.Google Scholar
• Clement J, Ren Y, Curley S (2020) Disregarding, modifying or adopting: How medical experts incorporate AI recommendations into patient care decisions. Agarwal R, Gao GG, Crowley K, McCullough J, eds. Conf. Health IT Analytics (CHITA), 32.Google Scholar
• Crawford M (2013) The truth about computer-assisted coding. J. AHIMA 84(7):24–27.Google Scholar
• Dai T, Singh S (2020) Conspicuous by its absence: Diagnostic expert testing under uncertainty. Marketing Sci. 39(3):540–563.Link, Google Scholar
• Dai T, Singh S (2023) Artificial intelligence on call: The physician’s decision of whether to use AI in clinical practice. Preprint, submitted January 13, http://dx.doi.org/10.2139/ssrn.3987454.Google Scholar
• Danziger S, Levav J, Avnaim-Pesso L (2011) Extraneous factors in judicial decisions. Proc. Natl. Acad. Sci. USA 108(17):6889–6892.Crossref, Google Scholar
• Davenport TH, Ronanki R (2018) Artificial intelligence for the real world. Harvard Bus. Rev. 96(1):108–116.Google Scholar
• David H (2015) Why are there still so many jobs? The history and future of workplace automation. J. Econom. Perspect. 29(3):3–30.Crossref, Google Scholar
• Davis E, Marcus G (2015) Commonsense reasoning and commonsense knowledge in artificial intelligence. Comm. ACM 58(9):92–103.Crossref, Google Scholar
• De Martino B, Kumaran D, Seymour B, Dolan RJ (2006) Frames, biases, and rational decision-making in the human brain. Science 313(5787):684–687.Crossref, Google Scholar
• Decker M, Fischer M, Ott I (2017) Service robotics and human labor: A first technology assessment of substitution and cooperation. Robotics Autonomous Systems 87:348–354.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
• Dimick C (2010) Achieving coding consistency. J. AHIMA 81(7):24–28.Google Scholar
• Dinan E, Logacheva V, Malykh V, Miller A, Shuster K, Urbanek J, Kiela D, et al. (2020) The second conversational intelligence challenge (convai2). The NeurIPS’18 Competition: From Machine Learning to Intelligent Conversations (Springer International Publishing, Berlin), 187–208.Google Scholar
• Dong W, Li J, Yao R, Li C, Yuan T, Wang L (2016) Characterizing driving styles with deep learning. Preprint, submitted October 8, https://arxiv.org/abs/1607.03611.Google Scholar
• Dunlosky J, Hertzog C (2000) Updating knowledge about encoding strategies: A componential analysis of learning about strategy effectiveness from task experience. Psych. Aging 15(3):462–474.Crossref, Google Scholar
• Ekins S (2016) The next era: Deep learning in pharmaceutical research. Pharmaceutical Res. 33(11):2594–2603.Crossref, Google Scholar
• Fjelland R (2020) Why general artificial intelligence will not be realized. Humanities Soc. Sci. Comm. 7(1):1–9.Google Scholar
• Ford JK, Sego D, Quinones M, Speer J (1991) The construct of experience: A review of the literature and needed research directions. Sixth Annual Conf. Soc. Indust. Organ. Psych. (Society for Industrial and Organizational Psychology, Columbus, OH).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(3):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
• Ganju KK, Pavlou PA, Banker RD (2016) Does information and communication technology lead to the well-being of nations? A country-level empirical investigation. MIS Quart. 40(2):417–430.Crossref, Google Scholar
• Ge R, Zheng Z, Tian X, Liao L (2021) Human–robot interaction: When investors adjust the usage of robo-advisors in peer-to-peer lending. Inform. Systems Res. 32(3):774–785.Link, Google Scholar
• Goldin C, Katz LF (1998) The origins of technology-skill complementarity. Quart. J. Econom. 113(3):693–732.Crossref, Google Scholar
• Goodman PS, Leyden DP (1991) Familiarity and group productivity. J. Appl. Psych. 76(4):578–586.Google Scholar
• Grand View Research (2018) Medical coding market size, share & trends analysis report by classification system (International Classification of Diseases, Healthcare Common Procedure Code System), by component, and segment forecasts, 2018–2025. Report, Grand View Research, San Francisco.Google Scholar
• Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410.Crossref, Google Scholar
• Guo Y, Yin X, Liu D, Xu SX (2020) “She is not just a computer”: Gender role of AI chatbots in debt collection. Internat. Conf. Inform. Systems 2020 (ICIS) (IEEE, Piscataway, NJ).Google Scholar
• Hainc N, Federau C, Stieltjes B, Blatow M, Bink A, Stippich C (2017) The bright, artificial intelligence-augmented future of neuroimaging reading. Frontiers Neurology 8:489.Crossref, Google Scholar
• Hayes-Roth F, Waterman DA, Lenat DB (1983) Building Expert Systems (Addison-Wesley Longman Publishing Co., Inc., Boston).Google Scholar
• He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on I mageNet classification. Proc. IEEE Internat. Conf. Comput. Vision (IEEE, Piscataway, NJ), 1026–1034.Google Scholar
• Henry-Nickie M (2017) AI should worry skilled knowledge workers too. Brookings.edu (November 8), https://www.brookings.edu/blog/techtank/2017/11/08/ai-should-worry-skilled-knowledge-workers-too/.Google Scholar
• Hosanagar K (2019) A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control (Viking, New York).Google Scholar
• Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ (2018) Artificial intelligence in radiology. Nature Rev. Cancer 18(8):500–510.Crossref, Google Scholar
• Huang L, Bras RL, Bhagavatula C, Choi Y (2019) Cosmos QA: Machine reading comprehension with contextual commonsense reasoning. Proc. 2019 Conf. Empirical Methods Natural Language Processing and the 9th Internat. Joint Conf. Natural Language Processing (EMNLP-IJCNLP) (Association for Computational Linguistics), 2391–2401.Google Scholar
• Jabbour S, Fouhey D, Kazerooni E, Sjoding MW, Wiens J (2020) Deep learning applied to chest X-rays: Exploiting and preventing shortcuts. Doshi-Velez F, Fackler J, Jung K, Kale D, Ranganath R, Wallace B, Wiens J, eds. Machine Learn. Healthcare Conf. (PMLR, New York), 750–782.Google Scholar
• Jajal TD (2018) Distinguishing between narrow AI, general AI and super AI. Medium (May 21), https://medium.com/mapping-out-2050/distinguishing-between-narrow-ai-general-ai-and-super-ai-a4bc44172e22.Google Scholar
• Jha S, Topol EJ (2016) Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA 316(22):2353–2354.Crossref, Google Scholar
• Johnson M, Vera A (2019) No AI is an island: The case for teaming intelligence. AI Magazine 40(1):16–28.Crossref, Google Scholar
• Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Science 349(6245):255–260.Crossref, Google Scholar
• Judge TA (1994) Person–organization fit and the theory of work adjustment: Implications for satisfaction, tenure, and career success. J. Vocational Behav. 44(1):32–54.Crossref, 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
• Kahneman D (2011) Thinking, Fast and Slow (Macmillan, New York).Google Scholar
• Kahneman D, Tversky A (1979) Prospect theory: An analysis of decision under risk. Econometrica 47(2):263–291.Crossref, Google Scholar
• Karahanna E, Chen A, Liu QB, Serrano C (2019) Capitalizing on health information technology to enable advantage in us hospitals. MIS Quart. 43(1):113–140.Crossref, Google Scholar
• Klein KJ, Dansereau F, Hall RJ (1994) Levels issues in theory development, data collection, and analysis. Acad. Management Rev. 19(2):195–229.Crossref, Google Scholar
• Knight W (2017) The dark secret at the heart of AI. MIT Tech. Rev. (April 11), https://www.technologyreview.com/2017/04/11/5113/the-dark-secret-at-the-heart-of-ai/.Google Scholar
• Kohn ML, Schooler C (1982) Job conditions and personality: A longitudinal assessment of their reciprocal effects. Amer. J. Sociol. 87(6):1257–1286.Crossref, Google Scholar
• Korinek A, Stiglitz JE (2017) Artificial intelligence and its implications for income distribution and unemployment. NBER Working Paper No. 24174, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Kose I, Gokturk M, Kilic K (2015) An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance. Appl. Soft Comput. 36(C):283–299.Crossref, Google Scholar
• Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T (2017) Artificial intelligence in precision cardiovascular medicine. J. Amer. College Cardiology 69(21):2657–2664.Crossref, Google Scholar
• Krusell P, Ohanian LE, Ríos‐Rull JV, Violante GL (2000) Capital‐skill complementarity and inequality: A macroeconomic analysis. Econometrica 68(5):1029–1053.Crossref, Google Scholar
• Lance CE, Hedge JW, Alley WE (1989) Joint relationships of task proficiency with aptitude, experience, and task difficulty: A cross-level, interactional study. Human Performance 2(4):249–272.Crossref, Google Scholar
• Lebovitz S (2019) Diagnostic doubt and artificial intelligence: An inductive field study of radiology work. ICIS 2019 Proc. (IEEE, Piscataway, NJ).Google Scholar
• Lebovitz S, Levina L, Lifshitz-Assa H (2021) Is AI ground truth really true? The dangers of training and evaluating AI tools based on experts’ know-what. MIS Quart. 45(3):1501–1526.Crossref, Google Scholar
• LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444.Crossref, Google Scholar
• Li P, Kim MM, Doshi JA (2010) Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality. BMC Health Services Res. 10(1):245.Crossref, Google Scholar
• Li X, Hess TJ, Valacich JS (2008) Why do we trust new technology? A study of initial trust formation with organizational information systems. J. Strategic Inform. Systems 17(1):39–71.Crossref, Google Scholar
• Liu X, Stoutenborough J, Vedlitz A (2017) Bureaucratic expertise, overconfidence, and policy choice. Governance (Oxford) 30(4):705–725.Crossref, Google Scholar
• Longoni C, Bonezzi A, Morewedge CK (2019) Resistance to medical artificial intelligence. J. Consumer Res. 46(4):629–650.Crossref, Google Scholar
• Lou B, Wu L (2021) AI on drugs: Can artificial intelligence accelerate drug development? Evidence from a large-scale examination of bio-pharma firms. MIS Quart. 45(3):1451–1482.Google Scholar
• Lum K (2017) Limitations of mitigating judicial bias with machine learning. Nature Human Behav. 1(7):0141.Crossref, Google Scholar
• Luo X, Qin MS, Fang Z, Qu Z (2021) Artificial intelligence coaches for sales agents: Caveats and solutions. J. Marketing 85(2):14–32.Crossref, Google Scholar
• Manyika J, Lund S, Chui M, Bughin J, Woetzel J, Batra P, Ko R, Sanghvi S (2017) Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Inst. (November 28), https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages.Google Scholar
• Maruthappu M, Gilbert BJ, El-Harasis MA, Nagendran M, McCulloch P, Duclos A, Carty MJ (2015) The influence of volume and experience on individual surgical performance: A systematic review. Ann. Surgery 261(4):642–647.Crossref, Google Scholar
• Mayer RC, Davis JH, Schoorman FD (1995) An integrative model of organizational trust. Acad. Management Rev. 20(3):709–734.Crossref, Google Scholar
• McCarthy J (2007) From here to human-level AI. Artificial Intelligence 171(18):1174–1182.Crossref, Google Scholar
• McCauley CD, Ruderman MN, Ohlott PJ, Morrow JE (1994) Assessing the developmental components of managerial jobs. J. Appl. Psych. 79(4):544–560.Crossref, Google Scholar
• McCradden MD, Joshi S, Mazwi M, Anderson JA (2020) Ethical limitations of algorithmic fairness solutions in health care machine learning. Lancet Digital Health 2(5):e221–e223.Crossref, Google Scholar
• McDaniel MA, Schmidt FL, Hunter JE (1988) Job experience correlates of job performance. J. Appl. Psych. 73(2):327.Crossref, Google Scholar
• McEnrue MP (1988) Length of experience and the performance of managers in the establishment phase of their careers. Acad. Management J. 31(1):175–185.Crossref, Google Scholar
• McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, et al. (2020) International evaluation of an AI system for breast cancer screening. Nature 577(7788):89–94.Crossref, Google Scholar
• McKnight DH, Choudhury V, Kacmar C (2002) Developing and validating trust measures for e-commerce: An integrative typology. Inform. Systems Res. 13(3):334–359.Link, Google Scholar
• McKnight DH, Carter M, Thatcher JB, Clay PF (2011) Trust in a specific technology: An investigation of its components and measures. ACM Trans. Management Inform. Systems 2(2):1–25.Crossref, Google Scholar
• Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533.Crossref, Google Scholar
• Mortimer JT, Lorence J (1979) Work experience and occupational value socialization: A longitudinal study. Amer. J. Sociol. 84(6):1361–1385.Crossref, Google Scholar
• Ng TW, Feldman DC (2010) Organizational tenure and job performance. J. Management 36(5):1220–1250.Google Scholar
• Nikolaidis S, Shah J (2013) Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy. 2013 8th ACM/IEEE Internat. Conf. Human-Robot Interaction (HRI) (IEEE, Piscataway, NJ), 33–40.Google Scholar
• Ostroff C, Ford JK (1989) Assessing training needs: Critical levels of analysis. Goldstein IL, ed. Training and Development in Organizations (Jossey-Bass, San Francisco), 25–62.Google Scholar
• Pakdemirli E (2019) Artificial intelligence in radiology: Friend or foe? Where are we now and where are we heading? Acta Radiologica Open 8(2):2058460119830222.Crossref, Google Scholar
• Pasquale F (2015) The Black Box Society (Harvard University Press, Cambridge, MA).Crossref, Google Scholar
• Pope GC, Kautter J, Ellis RP, Ash AS, Ayanian JZ, Iezzoni LI, Ingber MJ, Levy JM, Robst J (2004) Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financial Rev. 25(4):119–141.Google Scholar
• Puig X, Shu T, Li S, Wang Z, Liao YH, Tenenbaum JB, Fidler S, Torralba A (2020) Watch-and-help: A challenge for social perception and human-AI collaboration. Preprint, submitted May 3, https://arxiv.org/abs/2010.09890.Google Scholar
• PwC (2017) Sizing the prize: What’s the real value of AI for your business and how can you capitalise? Report. https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf.Google Scholar
• Quinones MA, Ford JK, Teachout MS (1995) The relationship between work experience and job performance: A conceptual and meta‐analytic review. Personality Psych. 48(4):887–910.Crossref, Google Scholar
• Raisch S, Krakowski S (2021) Artificial intelligence and management: The automation-augmentation paradox. Acad. Management Rev. 46(1):192–210.Crossref, Google Scholar
• Ribeiro MT, Singh S, Guestrin C (2016) “Why should I trust you?” Explaining the predictions of any classifier. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1135–1144.Google Scholar
• Ricard S (2020) The year of the knowledge worker. Forbes (December 10), https://www.forbes.com/sites/forbestechcouncil/2020/12/10/the-year-of-the-knowledge-worker/?sh=1fdb242d7fbb.Google Scholar
• Rossi F (2018) Building trust in artificial intelligence. J. Internat. Affairs 72(1):127–134.Google Scholar
• Rudin P (2017) Thoughts on human learning vs. machine learning. Singularity2030.ch (January 13), https://singularity2030.ch/thoughts-on-human-learning-vs-machine-learning/.Google Scholar
• Sadigh D, Landolfi N, Sastry SS, Seshia SA, Dragan AD (2018) Planning for cars that coordinate with people: Leveraging effects on human actions for planning and active information gathering over human internal state. Autonomous Robots 42(7):1405–1426.Crossref, Google Scholar
• Sarkar D (2018) The importance of human interpretable machine learning. Medium (May 24), https://towardsdatascience.com/human-interpretable-machine-learning-part-1-the-need-and-importance-of-model-interpretation-2ed758f5f476.Google Scholar
• Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG (2010) Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): Architecture, component evaluation and applications. J. Amer. Medical Inform. Assoc. 17(5):507–513.Crossref, Google Scholar
• Schmidt FL, Hunter JE, Outerbridge AN (1986) Impact of job experience and ability on job knowledge, work sample performance, and supervisory ratings of job performance. J. Appl. Psych. 71(3):432–439.Crossref, Google Scholar
• Seymour M, Kriemer K, Yuan L, Dennis A (2021) Beyond deep fakes: Conceptual framework and research agenda for neural rendering of realistic digital faces. Proc. 54th Hawaii Internat. Conf. System Sci. HICSS (IEEE, Piscataway, NJ), 4859–4868.Google Scholar
• Siu HC, Pena JD, Chen E, Zhou Y, Lopez VJ, Palko K, Chang KC, Allen RE (2021) Evaluation of human-AI teams for learned and rule-based agents in Hanabi. Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Wortman Vaughan J, eds. Conf. Neural Inform. Processing Systems (NeurIPS), vol. 34 (NeurIPS, San Diego), 16183–16195.Google Scholar
• Slocum JW Jr, Cron WL, Hansen RW, Rawlings S (1985) Business strategy and the management of plateaued employees. Acad. Management J. 28(1):133–154.Crossref, Google Scholar
• Sturm T, Gerlach JP, Pumplun L, Mesbah N, Peters F, Tauchert C, Nan N, Buxmann P (2021) Coordinating human and machine learning for effective organizational learning. MIS Quart. 45(3):1581–1602.Crossref, Google Scholar
• Sun C, Shi ZJ, Liu X, Ghose A, Li X, Xiong F (2021) The effect of voice AI on consumer purchase and search behavior. Working paper, NYU Stern School of Business, New York.Google Scholar
• Swap W, Leonard D (2014) Artificial intelligence can’t replace hard-earned knowledge—Yet. Harvard Bus. Rev. (November 17), https://hbr.org/2014/11/artificial-intelligence-cant-replace-hard-earned-knowledge-yet.Google Scholar
• Syverson C (2017) Challenges to mismeasurement explanations for the US productivity slowdown. J. Econom. Perspect. 31(2):165–186.Crossref, Google Scholar
• Taddy M (2018) The technological elements of artificial intelligence. Agrawal A, Gans J, Goldfarb A, ed. The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, Chicago), 61–88.Google Scholar
• Talley EM, Newman D, Mimno D, Herr BW II, Wallach HM, Burns GA, Leenders AM, McCallum A (2011) Database of NIH grants using machine-learned categories and graphical clustering. Nature Methods 8(6):443–444.Crossref, Google Scholar
• Tan TF, Netessine S (2020) At your service on the table: Impact of tabletop technology on restaurant performance. Management Sci. 66(10):4496–4515.Link, Google Scholar
• Tandon N, Varde AS, de Melo G (2018) Commonsense knowledge in machine intelligence. SIGMOD Record 46(4):49–52.Crossref, Google Scholar
• Teodorescu MH, Morse L, Awwad Y, Kane GC (2021) Failures of fairness in automation require a deeper understanding of human-ML augmentation. MIS Quart. 45(3):1483–1500.Crossref, Google Scholar
• Tesluk PE, Jacobs RR (1998) Toward an integrated model of work experience. Personality Psych. 51(2):321–355.Crossref, Google Scholar
• Tversky A, Kahneman D (1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157):1124–1131.Crossref, Google Scholar
• Tzeng OJ (1973) Positive recency effect in a delayed free recall. J. Verbal Learn. Verbal Behav. 12(4):436–439.Crossref, Google Scholar
• Vance RJ, Coovert MD, MacCallum RC, Hedge JW (1989) Construct models of task performance. J. Appl. Psych. 74(3):447–455.Crossref, Google Scholar
• van den Broek E, Sergeeva A, Huysman M (2021) WHEN the machine meets the expert: An ethnography of developing AI for hiring. MIS Quart. 45(3):1557–1580.Crossref, Google Scholar
• Veiga JF (1981) Plateaued versus nonplateaued managers: Career patterns, attitudes, and path potential. Acad. Management J. 24(3):566–578.Crossref, Google Scholar
• Wagner JA III, Ferris GR, Fandt PM, Wayne SJ (1987) The organizational tenure—Job involvement relationship: A job‐career experience explanation. J. Organ. Behav. 8(1):63–70.Crossref, Google Scholar
• Welleck S, Weston J, Szlam A, Cho K (2018) Dialogue natural language inference. Preprint, submitted January 18, https://arxiv.org/abs/1811.00671.Google Scholar
• Williams C (2015) AI guru Ng: Fearing a rise of killer robots is like worrying about overpopulation on Mars. The Register, https://www.theregister.com/2015/03/19/andrew_ng_baidu_ai/.Google Scholar
• Wu L, Kane GC (2021) Network-biased technical change: How modern digital collaboration tools overcome some biases but exacerbate others. Organ. Sci. 32(2):273–292.Link, Google Scholar
• Zhang R, McNeese NJ, Freeman G, Musick G (2020) ‘An ideal human’ expectations of AI teammates in human-AI teaming. Proc. ACM Human-Comput. Interaction 4(CSCW3) (ACM, New York), 1–25.Google Scholar
• Zysman J, Nitzberg M (2020) Governing AI: Understanding the limits, possibility, and risks of AI in an era of intelligent tools and systems. Preprint, submitted October 14, http://dx.doi.org/10.2139/ssrn.3681088.Google Scholar