体育赛事投注记录

advertisement

Dangers of Bias in Data-Intensive Information Systems

  • Baekkwan Park
  • Dhana L. Rao
  • Venkat N. GudivadaEmail author
Conference paper
  • 35 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1162)

Abstract

体育赛事投注记录data-intensive information systems (dis) are pervasive and virtually affect people in all walks of life. artificial intelligence and machine learning technologies are the backbone of dis systems. various types of biases embedded into dis systems have serious significance and implications for individuals as well as the society at large. in this paper, we discuss various types of bias—both human and machine—and suggest ways to eliminate or minimize it. we also make a case for digital ethics education and outline ways to incorporate such education into computing curricula.

Keywords

Human bias Algorithmic bias Information systems Digital ethics 

References

  1. 1.
    Acquisti, A., Gross, R., Stutzman, F.: Faces of facebook: Privacy in the age of augmented reality. BlackHat USA 2, 1–20 (2011)
  2. 2.
    Alarie, B.: The path of the law: towards legal singularity. Univ. Toronto Law J. 66(4), 443–455 (2016)
  3. 3.
    Andrejevic, M.: Digital citizenship and surveillance| to pre-empt a thief. Int. J. Commun. 11, 18 (2017)
  4. 4.
    Asur, S., Huberman, B.A.: Predicting the future with social media. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01, pp. 492–499. IEEE Computer Society (2010)
  5. 5.
    Bakke, E.: Predictive policing: the argument for public transparency. NYU Ann. Surv. Am. L. 74, 131 (2018)
  6. 6.
    Bakshy, E., Messing, S., Adamic, L.A.: Exposure to ideologically diverse news and opinion on facebook. Science 348(6239), 1130–1132 (2015)
  7. 7.
    Barocas, S., Boyd, D.: Engaging the ethics of data science in practice. Commun. ACM 60(11), 23–25 (2017)
  8. 8.
    Barocas, S., Selbst, A.D.: Big data’s disparate impact. Calif. L. Rev. 104, 671 (2016)
  9. 9.
    Burrell, J.: How the machine ‘thinks’: understanding opacity in machine learning algorithms. Big Data Soc. 3(1), 2053951715622512 (2016)
  10. 10.
    Cadwalladr, C., Graham-Harrison, E.: The Cambridge analytics files. The Guardian (2018)
  11. 11.
    Camacho-Collados, M., Liberatore, F.: A decision support system for predictive police patrolling. Decis. Support Syst. 75, 25–37 (2015)
  12. 12.
    Chandler, S.: The AI chatbot will hire you now. Wired.com (2017)
  13. 13.
    Chen, W., Quan-Haase, A.: Big data ethics and politics: Toward new understandings. Soc. Sci. Comput. Rev., p. 0894439318810734 (2018)
  14. 14.
    Datta, A., Sen, S., Tschantz, M.C.: Correspondences between privacy and nondiscrimination: why they should be studied together. arXiv preprint (2018)
  15. 15.
    Eubanks, V.: Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press, New York, NY (2018)
  16. 16.
    Fang, H., Moro, A.: Theories of statistical discrimination and affirmative action: a survey. In: Benhabib, J., Jackson, M.O., Bisin, A. (eds.) Handbook of Social Economics, vol. 1a (2011)
  17. 17.
    Fry, H.: Hello World: Being Human in the Age of Algorithms. WW Norton & Company (2018)
  18. 18.
    Gandomi, A., Haider, M.: Beyond the hype: Big data concepts, methods, and analytics. Int. J. Inf. Manage. 35(2), 137–144 (2015)
  19. 19.
    Gayo-Avello, D.: A meta-analysis of state-of-the-art electoral prediction from twitter data. Soc. Sci. Comput. Rev. 31(6), 649–679 (2013)
  20. 20.
    Glaberson, S.K.: Coding over the cracks: predictive analytics and child protection. Fordham Urb. LJ 46, 307 (2019)
  21. 21.
    Goffman, A.: On the run: fugitive life in an American city. Picador (2015)
  22. 22.
    Gudivada, V., Apon, A., Ding, J.: Data quality considerations for big data and machine learning: going beyond data cleaning and transformations. Int. J. Adv. Softw. 10(1), 1–20 (2017)
  23. 23.
    Gudivada, V.N., Ramaswamy, S., Srinivasan, S.: Data management issues in cyber-physical systems. In: Transportation Cyber-Physical Systems, pp. 173–200. Elsevier (2018)
  24. 24.
    Guerette, R.T., Bowers, K.J.: Assessing the extent of crime displacement and diffusion of benefits: a review of situational crime prevention evaluations. Criminology 47(4), 1331–1368 (2009)
  25. 25.
    Hamilton, M.: The biased algorithm: evidence of disparate impact on hispanics. Am. Crim. L. Rev. 56, 1553 (2019)
  26. 26.
    Hargittai, E.: Is bigger always better? potential biases of big data derived from social network sites. Ann. Am. Acad. Polit. Soc. Sci. 659(1), 63–76 (2015)
  27. 27.
    Hersch, J., Shinall, J.B.: Something to talk about: Information exchange under employment law. U. Pa. L. Rev. 165, 49 (2016)
  28. 28.
    Kleinberg, J.: Inherent trade-offs in algorithmic fairness. In: ACM SIGMETRICS Performance Evaluation Review, vol. 46, pp. 40–40. ACM (2018)
  29. 29.
    Kroll, J.A., Barocas, S., Felten, E.W., Reidenberg, J.R., Robinson, D.G., Yu, H.: Accountable algorithms. U. Pa. L. Rev. 165, 633 (2016)
  30. 30.
    Labrinidis, A., Jagadish, H.V.: Challenges and opportunities with big data. Proc. VLDB Endowment 5(12), 2032–2033 (2012)
  31. 31.
    Lazer, D., Pentland, A., Adamic, l., Aral, S., Barabasi, A.L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., Van Alstyne, M.: Computational social science. Science 323(5915), 721–723 (2009)
  32. 32.
    Levine, E., Tisch, J., Tasso, A., Joy, M.: The New York city police department’s domain awareness system. Interfaces 47(1), 70–84 (2017)
  33. 33.
    Lipton, Z.C.: The mythos of model interpretability. arXiv preprint (2016)
  34. 34.
    Madden, M., Gilman, M., Levy, K., Marwick, A.: Privacy, poverty, and big data: a matrix of vulnerabilities for poor americans. Wash. UL Rev. 95, 53 (2017)
  35. 35.
    Markham, A.N., Tiidenberg, K., Herman, A.: Ethics as methods: doing ethics in the era of big data research-introduction. Social Media Soc. 4(3), 2056305118784502 (2018).  
  36. 36.
    Meijer, A., Wessels, M.: Predictive policing: Review of benefits and drawbacks. Int. J. Pub. Adm., pp. 1–9 (2019)
  37. 37.
    Noble, S.: Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press, New York, NY (2018)
  38. 38.
    O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Fourth International AAAI Conference on Weblogs and Social Media (2010)
  39. 39.
    O’Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group, New York, NY (2016)
  40. 40.
    Oswald, M., Babuta, A.: Data Analytics and Algorithmic Bias in Policing (2019)
  41. 41.
    Pasquale, F.: The Black Box Society. Harvard University Press, Cambridge (2015)
  42. 42.
    Passi, S., Barocas, S.: Problem formulation and fairness. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 39–48. ACM (2019)
  43. 43.
    Pearsall, B.: Predictive policing: the future of law enforcement. Nat. Inst. Justice J. 266(1), 16–19 (2010)
  44. 44.
    Schlehahn, E., Wenning, R.: Gdpr transparency requirements and data privacy vocabularies. In: IFIP International Summer School on Privacy and Identity Management, pp. 95–113. Springer (2018)
  45. 45.
    Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Ramones, S.M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M.E., et al.: Personality, gender, and age in the language of social media: the open-vocabulary approach. PloS one 8(9), e73791 (2013)
  46. 46.
    Shahin, S., Zheng, P.: Big data and the illusion of choice: Comparing the evolution of India’s aadhaar and China’s social credit system as technosocial discourses. Soc. Sci. Comput. Rev., p. 0894439318789343 (2018)
  47. 47.
    Silva, S., Kenney, M.: Algorithms, platforms, and ethnic bias: an integrative essay. Phylon (1960-) 55(1 & 2), 9–37 (2018)
  48. 48.
    Stern, M.J., Bilgen, I., McClain, C., Hunscher, B.: Effective sampling from social media sites and search engines for web surveys: demographic and data quality differences in surveys of google and facebook users. Soc. Sci. Comput. Rev. 35(6), 713–732 (2017)
  49. 49.
    Strahilevitz, L.J.: Reputation nation: law in an era of ubiquitous personal information. Nw. UL Rev. 102, 1667 (2008)

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Baekkwan Park
    • 1
  • Dhana L. Rao
    • 2
  • Venkat N. Gudivada
    • 3
    Email author
  1. 1.Center for Survey ResearchEast Carolina UniversityGreenvilleUSA
  2. 2.Department of BiologyEast Carolina UniversityGreenvilleUSA
  3. 3.Department of Computer ScienceEast Carolina UniversityGreenvilleUSA

Personalised recommendations