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Künstliche Intelligenz im Management

Chancen und Risiken von Künstlicher Intelligenz als Entscheidungsunterstützung

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Praxisbeispiele der Digitalisierung

Zusammenfassung

Menschliche Entscheidungen sind fehleranfällig und unterliegen oft kognitiven Verzerrungen. Insbesondere bei Entscheidungen, die von Unsicherheit, Dringlichkeit und Komplexität gekennzeichnet sind, ist dies der Fall. Hierbei gilt es zwischen Fehlern, die durchaus bedeutsam für den Erkenntnisgewinn sein können und dem Irrtum zu differenzieren. Letzteres basiert auf einer inkorrekten Einschätzung und kann nicht immer als solche bestimmt werden. Diverse Managemententscheidungen unterliegen ebenfalls Fehlern und kommen als Verzerrungen in Personalentscheidungen oder im strategisch organisationalen Kontext zu tragen. Der Einsatz von Künstlicher Intelligenz (KI) im Management kann menschlichen Verzerrungen entgegenwirken und Transparenz in Entscheidungsprozessen bringen. Zudem kann der Einsatz von KI die zunehmende Komplexität, Ambiguität und Unsicherheiten im Umgang mit großen Datenstrukturen reduzieren. Dabei gilt es jedoch auf potentielle Fallstricke zu achten, da eine KI durchaus auch fehleranfällig sein kann und diese strukturellen Fehler (z. B. verzerrte Trainingsdaten) dementsprechend in praktischen Szenarien anwendet. Darüber hinaus gilt es ethische und moralische Aspekte in der Interaktion zwischen Menschen und KI in symbiotischen Entscheidungsprozessen zu berücksichtigen und zu implementieren. Dieses Kapitel beleuchtet den Einsatz von KI in Managemententscheidungen und den damit verbundenen Vorteilen sowie Herausforderungen, die dem aktuellen Stand der Technologie zugrunde legen.

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Literatur

  • Abbass, H. A. (2019). Social integration of artificial intelligence: Functions, automation allocation logic and human-autonomy trust. Cognitive Computation, 11(2), 159–171.

    Article  Google Scholar 

  • Acciarini, C., Brunetta, F., & Boccardelli, P. (2020). Cognitive biases and decision-making strategies in times of change: A systematic literature review. Management Decision, 59, 638–652.

    Google Scholar 

  • Barnes, J. H., Jr. (1984). Cognitive biases and their impact on strategic planning. Strategic Management Journal, 5(2), 129–137.

    Article  Google Scholar 

  • Benard, S., Paik, I., & Correll, S. J. (2008). Cognitive bias and the motherhood penalty. Hastings LawJournal, 59(6), 1359–1387.

    Google Scholar 

  • Bhatnagar, S., Alexandrova, A., Avin, S., Cave, S., Cheke, L., Crosby, M., & Hernández-Orallo, J., et al. (2017, November). Mapping intelligence: Requirements and possibilities. In 3rd Conference on" Philosophy and Theory of Artificial Intelligence (S. 117–135). Springer, Cham.

    Google Scholar 

  • Bostrom, N. (2011). The ethics of artificial intelligence. In Cambridge handbook of artificial intelligence. Cambridge University Press.

    Google Scholar 

  • Bourgin, D. D., Peterson, J. C., Reichman, D., Russell, S. J., & Griffiths, T. L. (2019, Mai). Cognitive model priors for predicting human decisions. In International conference on machine learning (S. 5133–5141). PMLR.

    Google Scholar 

  • Bowen, C. C., Swim, J. K., & Jacobs, R. R. (2000). Evaluating gender biases on actual job performance of real people: A meta-analysis 1. Journal of Applied Social Psychology, 30(10), 2194–2215.

    Article  Google Scholar 

  • Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186.

    Article  Google Scholar 

  • Chira, I., Adams, M., & Thornton, B. (2008). Behavioral bias within the decision making process. Journal of Business Economics Research 6(8), 11–20.

    Google Scholar 

  • Colson, E. (2019, 8. Juli). What AI-driven decision making looks like. Harvard Business Review. https://hbr.org/2019/07/what-ai-driven-decision-making-looks-like.

    Google Scholar 

  • Courtland R. (2018). Bias detectives: the researchers striving to make algorithms fair. Nature, 558(7710), 357–360. https://doi.org/10.1038/d41586-018-05469-3.

    Google Scholar 

  • Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114.

    Article  Google Scholar 

  • Cowgill, B., Dell'Acqua, F., Deng, S., Hsu, D., Verma, N., & Chaintreau, A. (2020, Juli). Biased programmers? or biased data? a field experiment in operationalizing ai ethics. In Proceedings of the 21st ACM Conference on Economics and Computation(S. 679–681).

    Google Scholar 

  • Crawford, K. (2013, 1. April). The hidden biases in big data.Harvard Business Review. https://hbr.org/2013/04/the-hidden-biases-in-big-data.

    Google Scholar 

  • Daugherty, P. R., Wilson, H. J., & Chowdhury, R. (2019). Using artificial intelligence to promote diversity. MIT Sloan Management Review, 60(2), 1.

    Google Scholar 

  • Deprez-Sims, A.-S., & Morris, S. B. (2010). Accents in the workplace: Their effects during a job interview. International Journal of Psychology, 45(6), 417–426.

    Article  Google Scholar 

  • Dezfouli, A., Nock, R., & Dayan, P. (2020). Adversarial vulnerabilities of human decision-making. Proceedings of the National Academy of Sciences, 117(46), 29221–29228.

    Article  Google Scholar 

  • Ding, D., Hill, F., Santoro, A., & Botvinick, M. (2020). Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures. arXiv, preprint arXiv:2012.08508.

    Google Scholar 

  • Dlamini, Z., Francies, F. Z., Hull, R., & Marima, R. (2020). Artificial intelligence (AI) and big data in cancer and precision oncology. Computational and Structural Biotechnology Journal, 18, 2300–2311.

    Google Scholar 

  • Ellis, A. (1976). The biological basis of human irrationality. Journal of Individual Psychology, 32, 145–168.

    Google Scholar 

  • Endres, M. L., Chowdhury, S., & Milner, M. (2009). Ambiguity tolerance and accurate assessment of self-efficacy in a complex decision task. Journal of Management & Organization, 15(1), 31–46.

    Google Scholar 

  • Evans, J. S. B., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8(3), 223–241.

    Article  Google Scholar 

  • Ferrer, X., van Nuenen, T., Such, J. M., Coté, M., & Criado, N. (2021). Bias and Discrimination in AI: a cross-disciplinary perspective. IEEE Technology and Society Magazine, 40(2), 72–80.

    Google Scholar 

  • Fjelland, R. (2020). Why general artificial intelligence will not be realized. Humanities and Social Sciences Communications, 7(1), 1–9.

    Article  Google Scholar 

  • Fukuda, K., Vogel, E., Mayr, U., & Awh, E. (2010). Quantity, not quality: The relationship between fluid intelligence and working memory capacity. Psychonomic Bulletin & Review, 17(5), 673–679.

    Article  Google Scholar 

  • Funder, D. C. (1987). Errors and mistakes: Evaluating the accuracy of social judgment. Psychological Bulletin, 101(1), 75–90.

    Google Scholar 

  • Gal, D. (2018). Why the most important idea in behav- ioral decision-making is a fallacy. Scientific American, 29(6), 52–54. https://doi.org/10.1038/scientificamerican mind1118–52.

    Google Scholar 

  • Gastounioti, A., & Kontos, D. (2020). Is it time to get rid of black boxes and cultivate trust in AI? Radiology: Artificial Intelligence, 2(3), e200088. https://doi.org/10.1148/ryai.2020200088.

    Google Scholar 

  • Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482.

    Article  Google Scholar 

  • Gil, D., Hobson, S., Mojsilović, A., Puri, R., & Smith, J. R. (2020). AI for management: An overview. In The future of management in an AI world (S. 3–19).

    Chapter  Google Scholar 

  • Hertwig, R., & Todd, P. M. (2003). More is not always better: The benefits of cognitive limits. In D. Hardman & L. Macchi (Hrsg.), Thinking: Psychological perspectives on reasoning, judgment and decision making (S. 213–231). Wiley.

    Google Scholar 

  • Hutson, M. (2021, 19. Januar). Who needs a teacher? Artificial intelligence designs lesson plans for itself. Science. https://www.sciencemag.org/news/2021/01/who-needs-teacher-artificial-intelligence-designs-lesson-plans-itself.

  • Ishfaq, M., Nazir, M. S., Qamar, M. A. J., & Usman, M. (2020). Cognitive bias and the extraversion personality shaping the behavior of investors. Frontiers in Psychology, 11, 556506. https://doi.org/10.3389/fpsyg.2020.556506.

    Google Scholar 

  • Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castaneda, A. G., et al. (2019). Human-level performance in 3D multiplayer games with population-based reinforcement learning. Science, 364(6443), 859–865.

    Article  Google Scholar 

  • Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586.

    Article  Google Scholar 

  • Johnson, D. D., Blumstein, D. T., Fowler, J. H., & Haselton, M. G. (2013). The evolution of error: Error management, cognitive constraints, and adaptive decision-making biases. Trends in Ecology & Evolution, 28(8), 474–481.

    Article  Google Scholar 

  • Kahneman, D., & Tversky, A. (1979). Intuitive prediction: Biases and corrective procedures. Management Science, 12, 313–327.

    Google Scholar 

  • Kahneman, D., & Tversky, A. (1983). Can irrationality be intelligently discussed? Behavioral and Brain Sciences, 6(3), 509–510.

    Article  Google Scholar 

  • Kahneman, D., Slovic, S. P., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge University Press.

    Book  Google Scholar 

  • Keil, M., Depledge, G., & Rai, A. (2007). Escalation: The role of problem recognition and cognitive bias. Decision Sciences, 38(3), 391–421.

    Article  Google Scholar 

  • Kienzler, M. (2018). Value-based pricing and cognitive biases: An overview for business markets. Industrial Marketing Management, 68, 86–94.

    Article  Google Scholar 

  • Koch, A. J., D’Mello, S. D., & Sackett, P. R. (2015). A meta-analysis of gender stereotypes and bias in experimental simulations of employment decision making. Journal of Applied Psychology, 100(1), 128–161.

    Google Scholar 

  • de Kock, F. S., & Hauptfleisch, D. B. (2018). Reducing racial similarity bias in interviews by increasing structure: A quasi-experiment using multilevel analysis. International Perspectives in Psychology, 7(3), 137–154.

    Article  Google Scholar 

  • Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40,e253.

    Google Scholar 

  • Lieto, A., Bhatt, M., Oltramari, A., & Vernon, D. (2018). The role of cognitive architectures in general artificial intelligence. Elsevier.

    Book  Google Scholar 

  • Lin, Y. T., Hung, T. W., & Huang, L. T. L. (2021). Engineering equity: How AI can help reduce the harm of implicit bias. Philosophy & Technology, 34(1), 65–90.

    Google Scholar 

  • Liu, B. (2021). „Weak AI“ is likely to never become “Strong AI”, so what is its greatest value for us? arXiv, preprint arXiv:2103.15294.

    Google Scholar 

  • Lohia, P. K., Ramamurthy, K. N., Bhide, M., Saha, D., Varshney, K. R., & Puri, R. (2019, Mai). Bias mitigation post-processing for individual and group fairness. In Icassp 2019-2019 ieee international conference on acoustics, speech and signal processing (icassp)(S. 2847–2851). IEEE.

    Google Scholar 

  • Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390(6657), 279–281.

    Article  Google Scholar 

  • Luzadis, R., Wesolowski, M., & Snavely, B. K. (2008). Understanding criterion choice in hiring decisions from a prescriptive gender bias perspective. Journal of Managerial Issues, 20(4), 468–484.

    Google Scholar 

  • Marcus, G. (2018). Deep learning: A critical appraisal. arXiv, preprint arXiv:1801.00631.

    Google Scholar 

  • McCarthy, J. M., Van Iddekinge, C. H., & Campion, M. A. (2010). Are highly structured job interviews resistant to demographic similarity effects? Personnel Psychology, 63(2), 325–359.

    Article  Google Scholar 

  • McIlroy-Young, R., Sen, S., Kleinberg, J., & Anderson, A. (2020, August). Aligning superhuman ai with human behavior: Chess as a model system. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (S. 1677–1687).

    Google Scholar 

  • McKay, C. (2020). Predicting risk in criminal procedure: actuarial tools, algorithms, AI and judicial decision-making. Current Issues in Criminal Justice, 32(1), 22–39.

    Article  Google Scholar 

  • Milosavljevic, M., Navalpakkam, V., Koch, C., & Rangel, A. (2012). Relative visual saliency differences induce sizable bias in consumer choice. Journal of Consumer Psychology, 22(1), 67–74.

    Article  Google Scholar 

  • Mujtaba, D. F., & Mahapatra, N. R. (2019, November). Ethical considerations in AI-based recruitment. In 2019 IEEE International Symposium on Technology and Society (ISTAS) (S. 1–7). IEEE.

    Google Scholar 

  • Narayan Banerjee, D., & Sekhar Chanda, S. (2020). AI failures: A review of underlying issues. arXiv, e-prints, arXiv: 2008.04073.

    Google Scholar 

  • Oaksford, M., & Hall, S. (2016). On the source of human irrationality. Trends in Cognitive Sciences, 20(5), 336–344.

    Article  Google Scholar 

  • Pingitore, R., Dugoni, B. L., Tindale, R. S., & Spring, B. (1994). Bias against overweight job applicants in a simulated employment interview. Journal of Applied Psychology, 79(6), 909–918.

    Google Scholar 

  • Power, D. J. (2008). Decision support systems: A historical overview. In Handbook on decision support systems (Bd. 1, S. 121–140). Springer.

    Chapter  Google Scholar 

  • Roselli, D., Matthews, J., & Talagala, N. (2019, Mai). Managing bias in AI. In Companion Proceedings of The 2019 World Wide Web Conference (S. 539–544).

    Google Scholar 

  • Rost, M. (2018). Künstliche Intelligenz. Datenschutz und Datensicherheit, 42(9), 558–565.

    Article  Google Scholar 

  • Santos, L. R., & Rosati, A. G. (2015). The evolutionary roots of human decision making. Annual Review of Psychology, 66, 321–347.

    Google Scholar 

  • Siau, K., & Wang, W. (2020). Artificial intelligence (AI) ethics: Ethics of AI and ethical AI. Journal of Database Management (JDM), 31(2), 74–87.

    Article  Google Scholar 

  • Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al. (2017). Mastering the game of go without human knowledge. Nature, 550(7676), 354–359.

    Article  Google Scholar 

  • Taniguchi, H., Sato, H., & Shirakawa, T. (2018). A machine learning model with human cognitive biases capable of learning from small and biased datasets. Scientific Reports, 8(1), 1–13.

    Article  Google Scholar 

  • Thomas, D. E., Eden, L., Hitt, M. A., & Miller, S. R. (2007). Experience of emerging market firms: The role of cognitive bias in developed market entry and survival. Management International Review, 47(6), 845–867.

    Article  Google Scholar 

  • Thomas, O. (2018). Two decades of cognitive bias research in entrepreneurship: What do we know and where do we go from here? Management Review Quarterly, 68(2), 107–143.

    Article  Google Scholar 

  • Tomasello, M. (2014). The ultra-social animal. European Journal of Social Psychology, 44(3), 187–194.

    Article  Google Scholar 

  • Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.

    Article  Google Scholar 

  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

    Article  Google Scholar 

  • Vallverdú, J. (2020). Approximate and situated causality in deep learning. Philosophies, 5(2), 1–12.

    Google Scholar 

  • Van Esch, P., Black, J. S., & Ferolie, J. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215–222.

    Article  Google Scholar 

  • van Esch, P., Black, J. S., & Arli, D. (2021). Job candidates’ reactions to AI-enabled job application processes. AI and Ethics, 1(2), 119–130.

    Google Scholar 

  • Vives, M.-L., & FeldmanHall, O. (2018). Tolerance to ambiguous uncertainty predicts prosocial behavior. Nature Communications, 9(1), 1–9.

    Article  Google Scholar 

  • Wamba-Taguimdje, S. L., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924.

    Google Scholar 

  • Yampolskiy, R. V. (2020). Unexplainability and incomprehensibility of AI. Journal of Artificial Intelligence and Consciousness, 7(02), 277–291.

    Article  Google Scholar 

  • Zou, J., & Schiebinger, L. (2018). AI can be sexist and racist – It’s time to make it fair. Nature Publishing Group.

    Book  Google Scholar 

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Kalimeris, J., Renz, S., Hofreiter, S., Spörrle, M. (2022). Künstliche Intelligenz im Management. In: Harwardt, M., Niermann, P.FJ., Schmutte, A.M., Steuernagel, A. (eds) Praxisbeispiele der Digitalisierung. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-37903-2_4

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