Auditing for transparency in content personalization systems

International Journal of Communication

Volumn 10, Issue , 2016, Pages 4991-5002

  Mittelstadt, Brent ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Algorithms; Automation; Bots; Ethics; Information ethics; Personalization; Politics; Recommendation systems; Transparency

Indexed keywords

EID: 85019344821     PISSN: None     EISSN: 19328036     Source Type: Journal    
DOI: None     Document Type: Article

References (42)

1. Allen, C., Wallach, W., & Smit, I. (2006). Why machine ethics? Intelligent Systems, IEEE, 21(4). Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1667947
2. Ananny, M. (2016). Toward an ethics of algorithms convening, observation, probability, and timeliness. Science, Technology & Human Values, 41(1), 93-117. http://doi.org/10.1177/0162243915606523
3. Barocas, S., & Selbst, A. D. (2015). Big data's disparate impact. Social Science Research Network. Retrieved from http://papers.ssrn.com/abstract=2477899
4. Bozdag, E. (2013). Bias in algorithmic filtering and personalization. Ethics and Information Technology, 15(3), 209-227. http://doi.org/10.1007/s10676-013-9321-6
5. Burrell, J. (2016). How the machine "thinks": Understanding opacity in machine learning algorithms. Big Data & Society, (3)1. Retrieved from http://bds.sagepub.com/content/3/1/2053951715622512
6. Cohen, I. G., Amarasingham, R., Shah, A., Xie, B., & Lo, B. (2014). The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Affairs, 33(7), 1139-1147. http://doi.org/10.1377/hlthaff.2014.0048
7. Danna, A., & Gandy, O. H., Jr. (2002). All that glitters is not gold: Digging beneath the surface of data mining. Journal of Business Ethics, 40(4), 373-386
8. European Commission. (2012). Regulation of the European parliament and of the council on the protection of individuals with regard to the processing of personal data and on the free movement of such data: General data protection regulation (No. COM[2012] 11). Retrieved from http://ec.europa.eu/justice/data-protection/document/review2012/com_2012_11_en.pdf
9. Floridi, L. (2010). Information: A very short introduction. Oxford, UK: Oxford University Press
10. Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems, 14(3), 330-347
11. Glenn, T., & Monteith, S. (2014). New measures of mental state and behavior based on data collected from sensors, smartphones, and the Internet. Current Psychiatry Reports, 16(12), 1-10. http://doi.org/10.1007/s11920-014-0523-3
12. Goldman, E. (2006). Search engine bias and the demise of search engine utopianism. Yale Journal of Law & Technology, 2005-2006, 6-8
13. Habermas, J. (1984). The theory of communicative action, vol. 1: Reason and the rationalization of society. Boston, MA: Beacon Press
14. Habermas, J. (1985). The theory of communicative action, vol. 2: Lifeworld and system: A critique of functionalist reason. Boston, MA: Beacon Press
15. Habermas, J. (1990). Moral consciousness and communicative action. Cambridge, MA: The MIT Press
16. Habermas, J. (1997). Between facts and norms: Contributions to a discourse theory of law and democracy. London, UK: Polity Press
17. Hajian, S., Domingo-Ferrer, J., & Martinez-Balleste, A. (2011). Discrimination prevention in data mining for intrusion and crime detection. In IEEE Symposium on Computational Intelligence in Cyber Security (pp. 47-54). Paris, France: IEEE. http://doi.org/10.1109/CICYBS.2011.5949405
18. Hildebrandt, M. (2008). Defining profiling: A new type of knowledge? In M. Hildebrandt & S. Gutwirth (Eds.), Profiling the European citizen (pp. 17-45). Rotterdam, Netherlands: Springer
19. Hinman, L. M. (2008). Searching ethics: The role of search engines in the construction and distribution of knowledge. Berlin, Germany: Springer
20. Howard, P. N., & Kollanyi, B. (2016). Bots, #strongerin, and #brexit: Computational propaganda during the UK-EU referendum. Social Science Research Network. Retrieved from http://papers.ssrn.com/abstract=2798311
21. Johnson, J. A. (2006). Technology and pragmatism: From value neutrality to value criticality. Social Science Research Network. Retrieved from http://papers.ssrn.com/abstract=2154654
22. Kraemer, F., van Overveld, K., & Peterson, M. (2011). Is there an ethics of algorithms? Ethics and Information Technology, 13(3), 251-260. http://doi.org/10.1007/s10676-010-9233-7
23. Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2016). Accountable algorithms. Social Science Research Network. Retrieved from http://papers.ssrn.com/abstract=2765268
24. Leese, M. (2014). The new profiling: Algorithms, black boxes, and the failure of anti-discriminatory safeguards in the European Union. Security Dialogue, 45(5), 494-511. http://doi.org/10.1177/0967010614544204
25. Lisboa, P. J. (2013). Interpretability in machine learning: Principles and practice. In 10th Annual Conference on Fuzzy Logic and Applications (pp. 15-21). Genoa, Italy: Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-319-03200-9_2
26. Macnish, K. (2012). Unblinking eyes: The ethics of automating surveillance. Ethics and Information Technology, 14(2), 151-167. http://doi.org/10.1007/s10676-012-9291-0
27. Matthias, A. (2004). The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics and Information Technology, 6(3), 175-183. http://doi.org/10.1007/s10676-004-3422-1
28. Mittelstadt, B. D., & Floridi, L. (2016). The ethics of big data: Current and foreseeable issues in biomedical contexts. Science and Engineering Ethics, 22(2), 303-341. http://doi.org/10.1007/s11948-015-9652-2
29. Naik, G., & Bhide, S. S. (2014). Will the future of knowledge work automation transform personalized medicine? Applied & Translational Genomics, 3(3), 50-53. http://doi.org/10.1016/j.atg.2014.05.003
30. Newell, S., & Marabelli, M. (2015). Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of datification. The Journal of Strategic Information Systems, 24(1), 3-14. http://doi.org/10.1016/j.jsis.2015.02.001
31. Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. London, UK: Viking
32. Romei, A., & Ruggieri, S. (2014). A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review, 29(5), 582-638. http://doi.org/10.1017/S0269888913000039
33. Rubel, A., & Jones, K. M. L. (2014). Student privacy in learning analytics: An information ethics perspective. Social Science Research Network. Retrieved from http://papers.ssrn.com/abstract=2533704
34. Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014). Auditing algorithms: Research methods for detecting discrimination on Internet platforms. Paper presented at the 2014 International Communication Association Preconference on Data and Discrimination: Converting Critical Concerns into Productive Inquiry, Seattle, WA. Retrieved from http://social.cs.uiuc.edu/papers/pdfs/ICA2014-Sandvig.pdf
35. Schermer, B. W. (2011). The limits of privacy in automated profiling and data mining. Computer Law & Security Review, 27(1), 45-52. http://doi.org/10.1016/j.clsr.2010.11.009
36. Stark, M., & Fins, J. J. (2013). Engineering medical decisions. Cambridge Quarterly of Healthcare Ethics, 22(4), 373-381. http://doi.org/10.1017/S0963180113000224
37. Tene, O., & Polonetsky, J. (2013). Big data for all: Privacy and user control in the age of analytics. Northwestern Journal of Technology and Intellectual Property, 239. Retrieved from http://heinonlinebackup.com/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/nwteintp11§ion=20
38. Turilli, M. (2007). Ethical protocols design. Ethics and Information Technology, 9(1), 49-62. http://doi.org/10.1007/s10676-006-9128-9
39. Turilli, M., & Floridi, L. (2009). The ethics of information transparency. Ethics and Information Technology, 11(2), 105-112. http://doi.org/10.1007/s10676-009-9187-9
40. Tutt, A. (2016). An FDA for algorithms. Social Science Research Network. Retrieved from http://papers.ssrn.com/abstract=2747994
41. Van Otterlo, M. (2013). A machine learning view on profiling. In M. Hildebrant & K. de Vries (Eds.), Privacy, due process and the computational turn: Philosophers of law meet philosophers of technology (pp. 46-64). London, UK: Routledge
42. Woolley, S. (2016). Automating power: Social bot interference in global politics. First Monday, 21(4). http://doi.org/10.5210/fm.v21i4.6161