How the machine ‘thinks’: Understanding opacity in machine learning algorithms

Big Data and Society

Volumn 3, Issue 1, 2016, Pages

  Burrell, Jenna ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

classification; discrimination; inequality; machine learning; Opacity; spam filtering

Indexed keywords

EID: 85074198691     PISSN: None     EISSN: 20539517     Source Type: Journal    
DOI: 10.1177/2053951715622512     Document Type: Article

References (31)

1. Barocas S (2014a) Data mining and the discourse on discrimination. In: Proceedings of the Data Ethics Workshop, Conference on Knowledge Discovery and Data Mining (KDD), 24--27 August, New York City.
2. Barocas S (2014b) Panic Inducing: Data Mining, Fairness, and Privacy, PhD Thesis, New York University, USA.
3. Barocas S, Selbst A, (forthcoming) Big Data’s disparate impact. California Law Review.
4. Both G (2014) What drives research in self-driving cars? (Part 2: Surprisingly not machine learning). Available at: http://blog.castac.org/2014/04/what-drives-research-in-self-driving-cars-part-2-surprisingly-not-machine-learning/.
5. Bowker GC, Star SL, (1999) Sorting Things Out: Classification and Its Consequences, Cambridge, MA: The MIT Press.
6. Brunton F, (2013) Spam, Cambridge, MA: The MIT Press.
7. Burrell J (2012) Invisible Users: Youth in the Internet Cafes of Urban Ghana. Cambridge, MA: The MIT Press.
8. Datta A, Tschantz MC and Datta A (2015) Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination. In: Proceedings on Privacy Enhancing Technologies, 30 June--2 July, Philadelphia, PA.
9. Diakopoulos N (2013) Algorithmic Accountability Reporting: On the Investigation of Black Boxes. Report, Tow Center for Digital Journalism, Columbia University.
10. Domingos P, (2012) A few useful things to know about machine learning. Communications of the ACM 55(10): 78.
11. Dwork C, Hardt M, Pitassi T, et al. (2012) Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, 8--10 January, Cambridge, MA, pp. 214–226.
12. Ensmenger NL, (2003) Letting the “computer boys” take over: Technology and the politics of organizational transformation. International Review of Social History 48(S11): 153–180.
13. Eubanks V, (2012) Digital Dead End: Fighting for Social Justice in the Information Age, Cambridge, MA: The MIT Press.
14. Fourcade M, Healy K, (2013) Accounting, organizations and society classification situations: Life-chances in the neoliberal era. Accounting, Organizations and Society 38(8): 559–572.
15. Gandy OH, (2010) Engaging rational discrimination: Exploring reasons for placing regulatory constraints on decision support systems. Ethics and Information Technology 12(1): 29–42.
16. Gillespie T, (2012) The relevance of algorithms. In: Gillespie T, Boczkowski P, Foot K, (eds) Media Technologies: Essays on Communication, Materiality, and Society, Cambridge, MA: The MIT Press.
17. Grudin J, (2006) Turing maturing: The separation of artificial intelligence and human–computer interaction. Interactions 13(5): 54–57.
18. Hargittai E, (2008) The digital reproduction of inequality. In: Gursky D, (ed.) Social Stratification, Boulder, CO: Westview Press, pp. 936–944.
19. Khosla (2012) Will we need teachers or algorithms? In: TechCrunch. Available at: http://techcrunch.com/2012/01/15/teachers-or-algorithms/ (accessed 11 December 2015).
20. Lee I, Martin F, Denner J, (2011) Computational thinking for youth in practice. ACM Inroads 2(1): 32–37.
21. Lipton Z (2015) The myth of model interpretability. Available at: http://www.kdnuggets.com/2015/04/model-interpretability-neural-networks-deep-learning.html (accessed 11 December 2015).
22. Mateas M and Montfort N (2005) A box, darkly: Obfuscation, weird languages, and code aesthetics. In: Proceedings of the 6th Annual Digital Arts and Culture Conference, 1--3 December, Copenhagen, Denmark.
23. Olazaran M, (1996) A sociological study of the official history of the perceptrons controversy. Social Studies of Science 26(3): 611–659.
24. Pasquale F, (2015) The Black Box Society: The Secret Algorithms that Control Money and Information, Cambridge, MA: Harvard University Press.
25. Sandvig C, (2014) Seeing the sort: The aesthetic and industrial defense of “the algorithm”. Journal of the New Media Caucus 10(3): 1–21.
26. Sandvig C, Hamilton K, Karahalios K, et al. (2014) Auditing algorithms: Research methods for detecting discrimination on internet platforms. In: Annual Meeting of the International Communication Association, Seattle, WA, pp. 1–23.
27. Seaver N (2014) Knowing algorithms. Presented at Media in Transition 8, Cambridge, MA.
28. Straka JW, (2000) A shift in the mortgage landscape: The 1990s move to automated credit evaluations. Journal of Housing Research 11(2): 207–232.
29. Tufekci Z (2014) The year we get creeped out by the algorithms. Available at: http://www.niemanlab.org/2014/12/the-year-we-get-creeped-out-by-algorithms/ (accessed 17 June 2015).
30. Wing JM, (2006) Computational thinking. Communications of the ACM 49(3): 33–35.
31. Winograd T, (2006) Shifting viewpoints: Artificial intelligence and human–computer interaction. Artificial Intelligence 170(18): 1256–1258.