Mining the networked self

Jerusalem Review of Legal Studies

Volumn 6, Issue 1, 2012, Pages 120-136

  Zarsky, Tal Z ^a  

^a UNIVERSITY OF HAIFA   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85015552386     PISSN: 22197117     EISSN: 22197125     Source Type: Journal    
DOI: 10.1093/jrls/jls015     Document Type: Article

References (23)

1. JULIE E. COHEN, CONFIGURING THE NETWORKED SELF: LAW, CODE AND THE PLAY OF EVERYDAY PRACTICE (Yale University Press 2012), at 4.
2. INSTAGRAM, www.instagram.com (last visited September 2012).
3. See Katz v. US, 389 U.S. 347, 361 (1967) (Harlen, J., Concurring), ("the expectation be one that society is prepared to recognize as 'reasonable'.").
4. For example, see Usama M. Fayyad et al., From Data Mining To Knowledge Discovery: An Overview, in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING (USAMA M. FAYYAD, GREGORY PIATETSKY-SHAPIRO, PADHRAIC SMYTH AND RAMASAMY UTHURUSAMY eds., (Westview Press 1996) at 6.
5. See Christopher Slobogin, Government Data Mining and the Fourth Amendment, 75 U. OF CHIC. L. REV. 317, 323 (2008).
6. Fred H. Cate, Government, Data Mining: The Need for a Legal Framework, 43 HARV. C.R.-C.L. L. REV. 436, 438 (2008).
7. See discussion in Tal Z. Zarsky, Governmental Data Mining and its Alternatives, 116 PENN ST. L. REV. 285 (2011), at 292.
8. For more on this process, see generally Tal Z. Zarsky, Mine Your Own Business: Making the Case for the Implications of the Data Mining of Personal Information in the Forum of Public Opinion, 5 YALE J.L. & TECH. (2002-2003), at 4.
9. David Martens & Foster Provost, Explaining Documents Classification 6 (N.Y.U. Stern School of Business, Working Paper No. CeDER-11-01, 2011), http://pages.stern.nyu.edu/fprovost/Papers/martens-CeDER-11-01.pdf (last accessed December 17, 2012).
10. I elaborate upon most of the ideas set forth in this section in an upcoming article: Tal Z. Zarsky, Transparent Predictions, U. OF ILL. L. REV. (forthcoming, 2013).
11. In a recent paper, Jennifer Shkabatur refers to similar elements as "performance transparency." For more as to how these requirements could be implemented, see Jennifer Shkabatur, Transparency With(out) Accountability: Open Government in the United States, 31 YALE L. & POL. REV. (forthcoming, 2013), at 50.
12. Mark Fenster, The Opacity of Transparency, 91 IOWA L. REV. 885, (2006), at 906.
13. For a discussion of this system and its flaws, see FRANK PASQUALE, THE BLACK BOX SOCIETY: PRIVATE TECHNOLOGY AND PUBLIC LIFE (Harvard University Press, forthcoming 2013).
14. Julie E. Cohen, Privacy, Visibility, Transparency, and Exposure, 75 U. OF CHIC. L. REV. (2008).
15. MICHEL FOUCAULT, DISCIPLINE AND PUNISH: THE BIRTH OF THE PRISON (Vintage Books 1995).
16. OSCAR GANDY, THE PANOPTIC SORT: A POLITICAL ECONOMY OF PERSONAL INFORMATION (AAAI Press 1993).
17. See, for instance, JOSEPH TUROW, NICHE ENVY: MARKETING DISCRIMINATION IN THE DIGITAL AGE (MIT Press 2006), at 133-35.
18. Id. at 309-12. See also Tal Z. Zarsky, Automated Prediction: Perception Law and Policy, 15 COMM. OF ACM 33 (2012).
19. This issue is explained in the recent work of Professor Jerry Kang. See JERRY KANG, http://jerrykang.net/research/ (last visited September 2012).
20. Samidh Chakrabarti & Aaron Strauss, Carnival Booth: An Algorithm for Defeating the Computer-Assisted Passenger Screening System, 7 FIRST MONDAY (2002).
21. For more on the IRS's use of predictive modeling for selecting returns for additional review, see FREDERICK SCHAUER, PROFILES, PROBABILITIES AND STEREOTYPING (Harvard University Press 2003), at 161-62.
22. The Financial Crisis Inquiry Report: Final Report of the National Commission on the Causes of the Financial and Economic Crisis in the United States, http://www.gpo.gov/fdsys/pkg/GPO-FCIC/pdf/GPO-FCIC.pdf (last accessed December 17, 2012).
23. See Danielle Keats Citron, Technological Due Process, 85 WASH. U. L. REV. 1249 (2008), at 1305-06.