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Social Responsibility of Algorithms: An Overview

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EURO Working Group on DSS

Part of the book series: Integrated Series in Information Systems ((ISIS))

Abstract

Should we be concerned by the massive use of devices and algorithms which automatically handle an increasing number of everyday activities within our societies? This chapter makes a short overview of the scientific investigation around this topic, showing that the development, existence and use of such autonomous artefacts are much older than the recent interest in machine learning monopolised artificial intelligence. We then categorise the impact of using such artefacts to the whole process of data collection, structuring, manipulation as well as in recommendation and decision making. The suggested framework allows to identify a number of challenges for the whole community of decision analysts, both researchers and practitioners.

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Notes

  1. 1.

    The best known controversy is the “COMPASS” case: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

  2. 2.

    https://www.wsj.com/articles/SB10001424127887323777204578189391813881534

References

  1. Åström, K., & Murray, R. (2008). Feedback Systems: An Introduction for Scientists and Engineers. Princeton: Princeton University Press.

    Book  Google Scholar 

  2. Abu-Elyounes, D. (2020). Contextual fairness: A legal and policy analysis of algorithmic fairness. Journal of Law, Technology and Policy, 2020, 1–54.

    Google Scholar 

  3. Aggarwal, C. (2016). Recommender Systems: The Textbook. Berlin: Springer.

    Book  Google Scholar 

  4. Arrow, K. (1951). Social Choice and Individual Values (2nd ed., 1963). New York: Wiley.

    Google Scholar 

  5. Bonnefon, J., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science, 352(6293), 1573–1576.

    Article  Google Scholar 

  6. Bouyssou, D., Marchant, T., Pirlot, M., Perny, P., Tsoukiàs, A., & Vincke, P. (2000). Evaluation and Decision Models: A Critical Perspective. Dordrecht: Kluwer Academic.

    Book  Google Scholar 

  7. Brandt, F., Conitzer, V., Endriss, U., Lang, J., & Procaccia, A. (2016). Handbook of Computational Social Choice. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  8. Casteluccia, C., & Le Métayer, D. (2019). Understanding Algorithmic Decision Making: Opportunities and Challenges. (EPRS Study, 104pp). Brussels: European Parliament.

    Google Scholar 

  9. Christophides, V., Efthymiou, V., Palpanas, T., Papadakis, G., & Stefanidis, K. (2020). End-to-end entity resolution for big data: A survey. Technical report, ARXIV. https://arxiv.org/pdf/1905.06397.pdf

  10. Colorni, A., & Tsoukiàs, A. (2013). What is a decision problem? Preliminary statements. In Proceedings of ADT 2013 (LNAI 8176, pp. 139–153). Berlin: Springer.

    Google Scholar 

  11. Cross, R. (1997). Revenue Management: Hard-Core Tactics for Market Domination. New York: Broadway Books.

    Google Scholar 

  12. Davenport, T., & Harris, J. (2007). Competing on Analytics: The New Science of Winning. Harvard: Harvard Business School Press.

    Google Scholar 

  13. Davenport, T., & Harris, J. G. (2005). Automated decision making comes of age. MIT Sloan Management Review, 46, 11.

    Google Scholar 

  14. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In Proceedings of ITCS’12 (pp. 214–226).

    Google Scholar 

  15. Friedler, S., Scheidegger, C., & Venkatasubramanian, S. (2016). On the (im)possibility of fairness. Technical report, CoRR abs/1609.07236.

    Google Scholar 

  16. Fürnkranz, J., & Hüllermeier, E. (2010). Preference Learning. Berlin: Springer.

    Google Scholar 

  17. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Boston: MIT Press.

    Google Scholar 

  18. Gory, G., & Scott-Morton, M. S. (1971). A framework for management information systems. MIT Sloan Management Review, 13, 55–70.

    Google Scholar 

  19. Greene, J., Rossi, F., Tasioulas, J., Venable, K., & Williams, B. (2016). Embedding ethical principles in collective decision support systems. In Proceedings of the AAAI 2016 (pp. 4147–4151).

    Google Scholar 

  20. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys (CSUR), 51(5), 93. https://doi.org/10.1145/3236009.

    Google Scholar 

  21. Hajian, S., & Domingo-Ferrer, J. (2013). A methodology for direct and indirect discrimination prevention in data mining. IEEE Transactions on Knowledge and Data Engineering, 25, 1445–1459.

    Article  Google Scholar 

  22. Hunt, V. (1985). Smart Robots: A Handbook of Intelligent Robotic Systems. London: Chapman and Hall.

    Book  Google Scholar 

  23. Hurwicz, L., & Reiter, S. (2006). Designing Economic Mechanisms. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  24. Keats Citron, D. (2008). Technological due process. Washington University Law Review, 85, 1249–1313.

    Google Scholar 

  25. Keen, P., & Scott-Morton, M. (1978). Decision Support Systems: An Organizational Perspective. Reading: Addison Wesley.

    Google Scholar 

  26. Kroll, J., Huey, J., Barocas, S., Felten, E., Reidenberg, J., Robinson, D., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165, 633–705.

    Google Scholar 

  27. Landry, M., Pascot, D., & Briolat, D. (1983). Can DSS evolve without changing our view of the concept of problem? Decision Support Systems, 1, 25–36.

    Article  Google Scholar 

  28. Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018). Fair, transparent, and accountable algorithmic decision-making processes. Philosophy and Technology, 31, 611–627.

    Article  Google Scholar 

  29. Lepri, B., Staiano, J., Sangokoya, D., Letouzé, E., & Oliver, N. (2017). The tyranny of data? The bright and dark sides of data-driven decision-making for social good. In T. Cerquitelli, D. Quercia, & F. Pasquale (Eds.), Transparent Data Mining for Big and Small Data (Studies in Big Data, Vol. 32, pp. 3–24). Cham: Springer.

    Chapter  Google Scholar 

  30. Mas-Colell, A., Whinston, M., & Green, J. (1995). Microeconomic Theory. New York: Oxford University Press.

    Google Scholar 

  31. Maskin, E. (2008). How to implement social goals mechanism design. American Economic Review, 98, 567–576.

    Article  Google Scholar 

  32. Mason, R., & Mitroff, I. (1973). A program for research on management information systems. Management Science, 19, 475–487.

    Article  Google Scholar 

  33. Mauri, A. (2007). Yield management and perception of fairness in the hotel business. International Review of Economics, 54, 284–293.

    Article  Google Scholar 

  34. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Technical report, bitcoin.org.

    Google Scholar 

  35. Narayanan, A., Bonneau, J., Felten, E., Miller, A., & Goldfeder, S. (2016). Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton: Princeton University Press.

    Google Scholar 

  36. O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown.

    Google Scholar 

  37. Pearl, J. (2009). Causality. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  38. Rae, D. (1971). The Political Consequences of Electoral Laws. Yale: Yale University Press.

    Google Scholar 

  39. Rahwan, I. (2020). Society-in-the-loop: programming the algorithmic social contract. Ethics and Information Technology, 20, 5–14.

    Article  Google Scholar 

  40. Reynolds, A., Reill, B., & Ellis, A. (2005). Electoral System Design. The New International IDEA Handbook. Stockholm: IDEA

    Google Scholar 

  41. Rhaiem, K., & Amara, N. (2019). Learning from innovation failures: A systematic review of the literature and research agenda. Review of Managerial Science. In press. https://doi.org/10.1007/s11846-019-00339-2.

  42. Ricci, F., Rokach, L., Shapira, B., & Kantor, P. (2011). Recommender Systems Handbook. Berlin: Springer.

    Book  Google Scholar 

  43. Roberts, F. (1979). Measurement Theory, with Applications to Decision Making, Utility and the Social Sciences. Boston: Addison-Wesley.

    Google Scholar 

  44. Russel, S., & Norvig, P. (1995). Artificial Intelligence: A Modern Approach. New York: Prentice Hall.

    Google Scholar 

  45. Sen, A. (1986). Social choice theory. In K. Arrow & M. Intriligator (Eds.), Handbook of Mathematical Economics (Vol. 3, pp. 1073–1181). Amsterdam: North-Holland.

    Google Scholar 

  46. Smith, B., Leimkuhler, J., & Darrow, R. (1992). Yield management at American Airlines. Journal on Applied Analytics, 22, 8–31.

    Google Scholar 

  47. Sprague, R., & Carlson, E. (1982). Building Effective Decision Support Systems. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  48. Stevens, B., & Lewis, F. (1992). Aircraft Control and Simulation. New York: Wiley.

    Google Scholar 

  49. Tsoukiàs, A. (2007). On the concept of decision aiding process. Annals of Operations Research, 154, 3–27.

    Article  Google Scholar 

  50. Wiener, N. (1948). Cybernetics. Cambridge: MIT Press.

    Google Scholar 

  51. Wieringa, M. (2020). What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. In Proceedings of FAT 2020 (pp. 1–18).

    Google Scholar 

  52. Winograd, T., & Flores, F. (1986). Understanding Computers and Cognition. Norwood: Ablex.

    Google Scholar 

  53. Wooldridge, M. (2002). An Introduction to Multi-Agent Systems. New York: Wiley.

    Google Scholar 

Download references

Acknowledgements

This document follows discussions which took place during the two workshops about this topic in Paris, December 2017 and 2019 (www.lamsade.dauphine.fr/sra), and I am indebted to the participants for their contributions. Although several ideas are due to these discussions, I remain the sole responsible for this essay.

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Authors and Affiliations

  1. CNRS-LAMSADE, PSL, Universite Paris Dauphine, Paris, France

    Alexis Tsoukias

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  1. Alexis Tsoukias
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Correspondence to Alexis Tsoukias .

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Editors and Affiliations

  1. Department of Business Administration, University of Macedonia, Thessaloniki, Greece

    Jason Papathanasiou

  2. University Toulouse 1 Capitole - IRIT, Toulouse University, Toulouse, France

    Pascale Zaraté

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    Jorge Freire de Sousa

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Tsoukias, A. (2021). Social Responsibility of Algorithms: An Overview. In: Papathanasiou, J., Zaraté, P., Freire de Sousa, J. (eds) EURO Working Group on DSS. Integrated Series in Information Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-70377-6_9

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