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IFIP Conference on Human-Computer Interaction

INTERACT 2021: Human-Computer Interaction – INTERACT 2021 pp 300–320Cite as

ObjectivAIze: Measuring Performance and Biases in Augmented Business Decision Systems

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12934)

Abstract

Business process management organizes flows of information and decisions in large organizations. These systems now integrate algorithmic decision aids leveraging machine learning: each time a stakeholder needs to make a decision, such as a purchase, a quote, or hiring someone, the software leverages the inputs and outcomes of similar past decisions to provide guidance, as a recommendation. If the confidence is high, the process may be automated. Otherwise, it may still help provide consistency in the decisions. Yet, we may question how these aids affect task performance. Can we measure an improvement? Can hidden biases influence decision makers negatively? What is the impact of various presentation options? To address those issues, we propose metrics of performance, automation bias and resistance. We validated those measures with an online study. Our aim is to instrument those systems to secure their benefits. In a first experiment, we study effective collaboration. Faced with a decision, subjects alone have a success rate of 72%; Aided by a recommender that has a 75% success rate, their success rate reaches 76%. The human-system collaboration had thus a greater success rate than each taken alone. However, we noted a complacency/authority bias that degraded the quality of decisions by 5% when the recommender was wrong. This suggests that any lingering algorithmic bias may be amplified by decision aids. In a second experiment, we evaluated the effectiveness of 5 presentation variants in reducing complacency bias. We found that optional presentation increases subjects’ resistance to wrong recommendations. We intend to leverage these findings to guide the design of human-algorithm collaboration in financial compliance alert filtering.

Keywords

  • Business decision systems
  • Decision theory
  • Cognitive biases

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Notes

  1. 1.

    https://www.kaggle.com/c/titanic.

References

  1. Alberdi, E., Strigini, L., Povyakalo, A.A., Ayton, P.: Why are people’s decisions sometimes worse with computer support? In: Buth, B., Rabe, G., Seyfarth, T. (eds.) SAFECOMP 2009. LNCS, vol. 5775, pp. 18–31. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04468-7_3

    CrossRef  Google Scholar 

  2. Alexander, V., Blinder, C., Zak, P.J.: Why trust an algorithm? Performance, cognition, and neurophysiology. Comput. Hum. Behav. 89, 279–288 (2018). ISSN 0747-5632. https://doi.org/10.1016/j.chb.2018.07.026

  3. Amershi, S., et al.: Guidelines for human-AI interaction. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI 2019), Paper 3, pp. 1–13. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3290605.3300233

  4. Anderson, C.: The psychology of doing nothing: forms of decision avoidance result from reason and emotion. Psychol. Bull. 129(1), 139–167 (2003). https://doi.org/10.1037/0033-2909.129.1.139. PMID 12555797. SSRN 895727

    CrossRef  Google Scholar 

  5. Elin Bahner, J., Hüper, A.-D., Manzey, D.: Misuse of automated decision aids: complacency, automation bias and the impact of training experience. Int. J. Hum.-Comput. Stud. 66(9), 688–699 (2008). ISSN 1071-5819. https://doi.org/10.1016/j.ijhcs.2008.06.001

  6. Bellamy, R.K., et al.: AI Fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943 (2018)

  7. Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., Shadbolt, N.: ‘It’s reducing a human being to a percentage’: perceptions of justice in algorithmic decisions. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI 2018), Paper 377, pp. 1–14. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3173574.3173951

  8. Bonaccio, S., Dalal, R.S.: Advice taking and decision-making: an integrative literature review, and implications for the organizational sciences. Organ. Behav. Hum. Decis. Process. 101(2), 127–151 (2006). ISSN 0749-5978. https://doi.org/10.1016/j.obhdp.2006.07.001

  9. Burton, J.W., Stein, M.-K., Jensen, T.B.: A systematic review of algorithm aversion in augmented decision making. J. Behav. Dec. Making 33, 220–239 (2020). https://doi.org/10.1002/bdm.2155

    CrossRef  Google Scholar 

  10. Business Rules Journal: A brief history of the business rule approach, 3rd edn. Bus. Rules J. 9(11) (2008). http://www.brcommunity.com/a2008/b448.html

  11. Cabitza, F.: Biases affecting human decision making in AI-supported second opinion settings. In: Torra, V., Narukawa, Y., Pasi, G., Viviani, M. (eds.) MDAI 2019. LNCS (LNAI), vol. 11676, pp. 283–294. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26773-5_25

    CrossRef  Google Scholar 

  12. Cai, C.J., et al.: Human-centered tools for coping with imperfect algorithms during medical decision-making. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI 2019), Paper 4, pp. 1–14. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3290605.3300234

  13. Castelo, N., Bos, M.W., Lehmann, D.R.: Task-dependent algorithm aversion. J. Mark. Res. 56(5), 809–825 (2019)

    CrossRef  Google Scholar 

  14. Chalmers (ed.): The Extended Mind, Philosophy of Mind: Classical and Contemporary Readings. Oxford University Press (2002)

    Google Scholar 

  15. Char, D.S., Shah, N.H., Magnus, D.: Implementing machine learning in health care - addressing ethical challenges. N. Engl. J. Med. 378(11), 981–983 (2018). https://doi.org/10.1056/NEJMp1714229

    CrossRef  Google Scholar 

  16. Coglianese, C., Lehr, D.: Regulating by Robot: Administrative Decision Making in the Machine-Learning Era (2017). Faculty Scholarship at Penn Law. 1734. https://scholarship.law.upenn.edu/faculty_scholarship/1734

  17. Dijksterhuis, A., Bos, M.W., Nordgren, L.F., Van Baaren, R.B.: On making the right choice: the deliberation-without-attention effect. Science 311(5763), 1005–1007 (2006)

    CrossRef  Google Scholar 

  18. Dimara, E., Bailly, G., Bezerianos, A., Franconeri, S.: Mitigating the attraction effect with visualizations. IEEE Trans. Vis. Comput. Graph. Inst. Electr. Electron. Eng. 25(1), 850–860 (2019). TVCG 2019 (InfoVis 2018). https://doi.org/10.1109/TVCG.2018.2865233. ⟨hal-01845004v2⟩

  19. Dimara, E., Franconeri, S., Plaisant, C., Bezerianos, A., Dragicevic, P.: A task-based taxonomy of cognitive biases for information visualization. IEEE Trans. Vis. Comput. Graph. 26(2), 1413–1432 (2020). https://doi.org/10.1109/TVCG.2018.2872577

  20. Dimara, E., Bezerianos, A., Dragicevic, P.: Conceptual and methodological issues in evaluating multidimensional visualizations for decision support. IEEE Trans. Vis. Comput. Graph. (2018)

    Google Scholar 

  21. Endsley, M.R.: From here to autonomy: lessons learned from human-automation research. Hum. Factors 59(1), 5–27 (2017). https://doi.org/10.1177/0018720816681350

    CrossRef  Google Scholar 

  22. Frees, E.: Longitudinal and Panel Data: Analysis and Applications in the Social Sciences. Cambridge University Press, New York (2004)

    CrossRef  Google Scholar 

  23. Gigerenzer, G., Gaissmaier, W.: Decision making: nonrational theories. In: Wright, J.D. (ed.) International Encyclopedia of the Social & Behavioral Sciences, 2nd edn., pp. 911–916. Elsevier (2015). ISBN 9780080970875. https://doi.org/10.1016/B978-0-08-097086-8.26017-0

  24. Gombolay, M.C., Gutierrez, R.A., Clarke, S.G., Sturla, G.F., Shah, J.A.: Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams. Auton. Robot. 39(3), 293–312 (2015). https://doi.org/10.1007/s10514-015-9457-9

    CrossRef  Google Scholar 

  25. Hafenbrädl, S., Waeger, D., Marewski, J.N., Gigerenzer, G.: Applied decision making with fast-and-frugal heuristics. J. Appl. Res. Mem. Cogn. 5(2), 215–231 (2016). ISSN 2211-3681. https://doi.org/10.1016/j.jarmac.2016.04.011

  26. Hastie, R., Dawes, R.: Rational Choice in an Uncertain World, The Psychology of Judgment and Decision Making, 2nd edn. Sage Publications (2009)

    Google Scholar 

  27. Hirshleifer, D., Levi, Y., Lourie, B., Teoh, S.H.: Decision fatigue and heuristic analyst forecasts. J. Financ. Econ. 133(1), 83–98 (2019)

    CrossRef  Google Scholar 

  28. HLEG-AI. Ethics guidelines for trustworthy AI. European Commision report, April 2019. https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai

  29. Jeston, J., Nelis, J.: Business Process Management. Routledge, 21 January 2014. ISBN 9781136172984

    Google Scholar 

  30. Kahneman, D., Klein, G.: Conditions for intuitive expertise, a failure to disagree. Am. Psychol. 64(6), 515–526 (2009). https://doi.org/10.1037/a0016755

    CrossRef  Google Scholar 

  31. Khenissi, S.: Modeling and counteracting exposure bias in recommender systems. Electronic theses and dissertations. Paper 3182 (2019). https://doi.org/10.18297/etd/3182

  32. Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., et al.: Explaining the user experience of recommender systems. User Model. User-Adap. Inter. 22, 441–504 (2012). https://doi.org/10.1007/s11257-011-9118-4

    CrossRef  Google Scholar 

  33. Knijnenburg, B.P., Reijmer, N.J.M., Willemsen, M.C.: Each to his own: how different users call for different interaction methods in recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys 2011), pp. 141–148. Association for Computing Machinery, New York (2011). https://doi.org/10.1145/2043932.2043960

  34. Lee, M.K., Kusbit, D., Metsky, E., Dabbish, L.: Working with machines: the impact of algorithmic and data-driven management on human workers. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI 2015), pp. 1603–1612. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2702123.2702548

  35. Lemaire, A.: LOI n° 2016-1321 du 7 octobre 2016 pour une République numérique. https://en.wikipedia.org/wiki/Loi_pour_une_R%C3%A9publique_num%C3%A9rique

  36. Logg, J.M., Minson, J.A., Moore, D.A.: Algorithm appreciation: people prefer algorithmic to human judgment. Organ. Behav. Hum. Decis. Process. 151, 90–103 (2019). ISSN 0749-5978. https://doi.org/10.1016/j.obhdp.2018.12.005

  37. Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_31

    CrossRef  Google Scholar 

  38. March, C.: The Behavioral Economics of Artificial Intelligence: Lessons from Experiments with Computer Players. CESifo Working Paper Series 7926, CESifo (2019). https://ideas.repec.org/p/ces/ceswps/_7926.html

  39. Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1–38 (2019). https://doi.org/10.1016/j.artint.2018.07.007

    CrossRef  MathSciNet  MATH  Google Scholar 

  40. Institut Montaigne: Algorithms: mind the bias! Report of the Institut Montaigne think-tank, March 2020. https://www.institutmontaigne.org/en/publications/algorithms-please-mind-bias

  41. Onnasch, L.: Crossing the boundaries of automation—function allocation and reliability. Int. J. Hum.-Comput. Stud. 76, 12–21 (2015). ISSN 1071-5819. https://doi.org/10.1016/j.ijhcs.2014.12.004

  42. Parasuraman, R., Manzey, D.H.: Complacency and bias in human use of automation: an attentional integration. Hum. Factors 52(3), 381–410 (2010). https://doi.org/10.1177/0018720810376055

    CrossRef  Google Scholar 

  43. Prahl, A., Van Swol, L.: Understanding algorithm aversion: when is advice from automation discounted? J. Forecast. 36, 691–702 (2017). https://doi.org/10.1002/for.2464

    CrossRef  MathSciNet  Google Scholar 

  44. Rader, E., Cotter, K., Cho, J.: Explanations as mechanisms for supporting algorithmic transparency. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Paper 103, pp. 1–13. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3173574.3173677

  45. Romanov, D., Kazantsev, N., Edgeeva, E.: The presence of order-effect bias in Moscow administration. In: Di Ciccio, C., et al. (eds.) BPM 2019. LNBIP, vol. 361, pp. 337–341. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30429-4_26

    CrossRef  Google Scholar 

  46. Frederick, S.: Cognitive reflection and decision making. J. Econ. Perspect. 19(4), 25–42 (2005). https://doi.org/10.1257/089533005775196732

    CrossRef  Google Scholar 

  47. Taddeo, M., Floridi, L.: How AI can be a force for good. Science 361(6404), 751–752 (2018). https://doi.org/10.1126/science.aat5991

  48. Morris, R., Mintz, S.: Cognitive processes and decision making in accounting, Chapter 2. In: Ethical Obligations and Decision-Making in Accounting: Text and Cases, 4th edn. McGraw Hill (2017). ISBN10: 1259543471

    Google Scholar 

  49. Tissandier, E., Baudel, T.: AIDA: Automatiser la prise de décisions métier en gardant l’humain dans la boucle. 31e conférence francophone sur l’Interaction Homme-Machine (IHM 2019), December 2019, Grenoble, France, pp. 2:1–2:6 (2019). ⟨hal-02407617⟩

    Google Scholar 

  50. Shneiderman, B.: Human-centered artificial intelligence: three fresh ideas. AIS Trans. Hum.-Comput. Interact. 12(3), 109–124 (2020). https://doi.org/10.17705/1thci.00131

  51. Von Halle, B.: Business Rules Applied. Wiley (2001). ISBN 0-471-41293-7

    Google Scholar 

  52. Yeomans, M., Shah, A., Mullainathan, S., Kleinberg, J.: Making sense of recommendations. J. Behav. Dec. Making 32, 403–414 (2019). https://doi.org/10.1002/bdm.2118

    CrossRef  Google Scholar 

  53. Yetgin, E., Jensen, M., Shaft, T.: Complacency and intentionality in IT use and continuance. AIS Trans. Hum.-Comput. Interact. 7(1), 17–42 (2015)

    CrossRef  Google Scholar 

  54. Green, B., Chen, Y.: The principles and limits of algorithm-in-the-loop decision making. Proc. ACM Hum.-Comput. Interact. CSCW, Article 50, 3, 24 (2019). https://doi.org/10.1145/3359152

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Acknowledgments

We thank Grégoire Colombet and François Jaquin for introducing us to their client’s decision problems that led to this research work. We also thank Pranivan Baudouin and Christopher Dolloff for their precious assistance along this project.

Author information

Authors and Affiliations

  1. France Lab, IBM, Orsay, France

    Thomas Baudel, Manon Verbockhaven, Victoire Cousergue, Guillaume Roy & Rida Laarach

  2. ENSAE, Palaiseau, France

    Manon Verbockhaven

  3. Université Paris-Dauphine, Paris, France

    Victoire Cousergue

  4. Mines ParisTech, Paris, France

    Victoire Cousergue

  5. ENSAI, Rennes, France

    Guillaume Roy

  6. Telecom ParisTech, Palaiseau, France

    Rida Laarach

Authors
  1. Thomas Baudel
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  2. Manon Verbockhaven
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  3. Victoire Cousergue
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  4. Guillaume Roy
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  5. Rida Laarach
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Corresponding author

Correspondence to Thomas Baudel .

Editor information

Editors and Affiliations

  1. Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy

    Carmelo Ardito

  2. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Rosa Lanzilotti

  3. Computer Science Department, University of Pisa, Pisa, Italy

    Alessio Malizia

  4. Department of Computer Science, University of York, York, UK

    Helen Petrie

  5. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Antonio Piccinno

  6. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Dr. Giuseppe Desolda

  7. Microsoft Research, Redmond, WA, USA

    Kori Inkpen

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Baudel, T., Verbockhaven, M., Cousergue, V., Roy, G., Laarach, R. (2021). ObjectivAIze: Measuring Performance and Biases in Augmented Business Decision Systems. In: Ardito, C., et al. Human-Computer Interaction – INTERACT 2021. INTERACT 2021. Lecture Notes in Computer Science(), vol 12934. Springer, Cham. https://doi.org/10.1007/978-3-030-85613-7_22

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