skip to main content
research-article

Era of big data: danger of descrimination

Published: 05 January 2016 Publication History

Abstract

We live in a world of data collection where organizations and marketers know our income, our credit rating and history, our love life, race, ethnicity, religion, interests, travel history and plans, hobbies, health concerns, spending habits and millions of other data points about our private lives. This data, mined for our behaviors, habits, likes and dislikes, is referred to as the "creep factor" of big data [1]. It is estimated that data generated worldwide will be 1.3 zettabytes (ZB) by 2016. The rise of computational power plus cheaper and faster devices to capture, collect, store and process data, translates into the "datafication" of society [4]. This paper will examine a side effect of datafication: discrimination.

References

[1]
Buchta, Heather (2014) How Did Data Get to Be So Big? Inside Counsel. Breaking News. November 25, http://www.insidecounsel.com/2014/11/25/how-did-data-get-to-be-so-big Accessed 6/1/15.
[2]
MIT Technology Review (2013), http://www.technologyreview.com/view/519851/the-big-data-conundrum-how-to-define-it/, Accessed October, 3, 2013.
[3]
McGuire, T., Manyika, J. and Chui, M. (2012)"Why Big Data is the New Competitive Advantage". Ivey Business Journal, Jul/Aug, Vol. 76 Issue 4, pp. 1--4.
[4]
Mayer-Schonberger, V. and Cukier, K. Big Data: A revolution that will transform how we live, work, and think. (2013) Houghton Mifflin Harcourt, Boston, NY.
[5]
OECD Digital Economy Papers 222 (2013) Exploring Data-Driven Innovation as a New Sources of Growth: Mapping the Policy Issues Raised by Big Data, April.
[6]
Zwitter, Andrej (2014) Big Data Ethics. Big Data & Society July - December, 2014: 1--6.
[7]
Yu, Persis, McLaughlin, Jillian and Levy, Marina. (2014) Big Data: A big Disappointment for Scoring Consumer Credit Risk. National Consumer Law Center March 2014.
[8]
Pasquale, Frank and Citron, Danielee Keats (2014) Promoting Innovation While Preventing Discrimination: Policy Goals for the Scored Society. Washington Law Review 89:1413.
[9]
Murphy, Michael and Barton, Jophn. (2014) From a Sea of Data to Actionable Insights: Big Data and What it Means for Lawyers. Intellectual property & Technology Law Journal March, 26.3: 8--17.
[10]
Newman, Nathan. (2014) How Big Data Enables Economic Harm to Consumers, Especially Low Income and Other Vulnerable Sectors of the Population. Journal of Internet law December, 18.6: 11--23.
[11]
Curran, John (2014) FTC Chief Sounds Note of Caution on Development of Big Data. Cybersecurity Policy Report, September.
[12]
Dwoskin, Elizabeth (2014) White House Takes Aim at Big Data Discrimination; Report recommends More Privacy laws. Wall Street Journal (Online). May 1, 2014.
[13]
Helbing, Dirk. (2014) Big Data Society: Age of Reputation or Age of Discrimination? http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2501356 Accessed May 1, 2014.
[14]
Wireless News (2014) Consumer Watchdog Supports 6 Policy Recommendations in White House Big Data Report. May 7.
[15]
Baker, Pam. (2015) Data Divination: Big Data Strategies. Cengage Learning PTR, Boston, MA
[16]
Lohr, Steve (2015) Maintaining A Human Touch As the Algorithms Get to Work. New York Times, April 7, p. A3
[17]
Hammond, Brian. (2014) Industry Groups Stress Need to Protect Innovation in Big Data privacy Effort. Telecommunications Reports, Sept 1, 2014 80.17: 29--32
[18]
Kapoor, Bhushan.(2011), Impact of Globilization on Human Resource Management. Journal of International Management Studies 6.1 (Feb): 1--8.
[19]
Lohr, S. (2013). Big Data, Trying to Build Better Workers. New York Times, April 21, p. 5.
[20]
Zarsky, Tal Z. (2014) Understanding Discrimination in the Scored Society, Washington Law Review, 89:1375.
[21]
Kettleborough, Jonathan. (2014), Big Data. Training Journal. June. 14--19.
[22]
Grossman, K (2014) "System-integration drives talent acquisition". http://www.peoplefluent.com/blog/hr-system-integration-drives-talent-acquisition, Accessed June 12, 2015.
[23]
www.peoplefluent.com (2014) Make Your HR Data Actionable Now! Unlock the Value Trapped in Your Company's Data by using Role - Based Analytics. A Peoplefluent White Paper. Accessed May 14, 2014.
[24]
Sanger, D. and Lohr, S. (2014). Call for Limits on Web Data of Customers. NYT May 2, 2014. P. A1 and B6.
[25]
Rubinstein, Ira S. (2013) Big Data: The End of Privacy or a New Beginning? International Data privacy Law, Vol 3, No.2: 74--87
[26]
Lohr, Steve (2015) Dataism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else. HarperCollins, NY, NY.
[27]
Gurin, Joel (2015) Opening Business Innovation With Open Data. Business Horizon Quarterly. Issue 12, pp. 42--49.
[28]
Citron, Danielle Keats and Pasquale, Frank (2014) The Scored Society: Due Process for Automated Predictions. March, Washington Law Review. 1--33.

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM SIGCAS Computers and Society
ACM SIGCAS Computers and Society  Volume 45, Issue 3
Special Issue on Ethicomp
September 2015
446 pages
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 January 2016
Published in SIGCAS Volume 45, Issue 3

Check for updates

Author Tags

  1. big data
  2. discrimination
  3. human resources
  4. privacy

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)55
  • Downloads (Last 6 weeks)5
Reflects downloads up to 10 Feb 2025

Other Metrics

Citations

Cited By

View all

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media