Skip to main content

Agency Laundering and Algorithmic Decision Systems

  • Conference paper
  • First Online:
Information in Contemporary Society (iConference 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11420))

Included in the following conference series:

Abstract

This paper has two aims. The first is to explain a type of wrong that arises when agents obscure responsibility for their actions. Call it “agency laundering.” The second is to use the concept of agency laundering to understand the underlying moral issues in a number of recent cases involving algorithmic decision systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    COMPAS stands for “Correctional Offender Management Profiling for Alternative Sanctions.” Northpointe is now a part of Equivant, Inc.

References

  1. Eubanks, V.: Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press, New York (2018)

    Google Scholar 

  2. O’Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown, New York (2016)

    MATH  Google Scholar 

  3. Noble, S.: Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press, New York (2018)

    Google Scholar 

  4. Barocas, S., Selbst, A.: Big data’s disparate impact. SSRN Scholarly Paper. Social Science Research Network, Rochester (2016). https://papers.ssrn.com/abstract=2477899

  5. Angwin, J., Larson, J.: Machine Bias. ProPublica, 23 May 2016. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

  6. Sweeney, L.: Discrimination in online ad delivery. ArXiv:1301.6822 [Cs], 28 January 2013. http://arxiv.org/abs/1301.6822

  7. Citron, D.: Technological due process. Washington Univ. Law Rev. 85, 1249–1314 (2008)

    Google Scholar 

  8. Pasquale, F.: The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press, Cambridge (2016)

    Google Scholar 

  9. Mittelstadt, B., Allo, P., Taddeo, M., Wachter, S., Floridi, L.: The ethics of algorithms: mapping the debate. Big Data Soc. 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

  10. Wisconsin v. Loomis. 2016 WI 68, 881 N.W.2d 749, 757, 760–64, cert. denied, 582 U.S. _ (U.S. June 26, 2017) (No. 16-6387) (2017)

    Google Scholar 

  11. Houston Federation of Teachers, Local 2415 v. Houston Independent School District. 251 F.Supp.3d 1168 (S.D. Tex. 2017)

    Google Scholar 

  12. U.S. Code § 1956 - Laundering of monetary instruments

    Google Scholar 

  13. Hill, T.: Autonomy and benevolent lies. J. Value Inq. 18(4), 251–267 (1984). https://doi.org/10.1007/BF00144766

    Article  Google Scholar 

  14. Angwin, J., Varner, M.: Facebook enabled advertisers to reach ‘Jew Haters.’ Text/html, 14 September 2017. https://www.propublica.org/article/facebook-enabled-advertisers-to-reach-jew-haters

  15. Sanberg, S.: Facebook post, 20 September 2017. https://www.facebook.com/sheryl/posts/10159255449515177. Accessed 30 Dec 2017

  16. Wagner v. Haslam, 112 F. Supp. 3d 673 (M.D. Tenn. 2015)

    Google Scholar 

  17. Walsh, E., Dotter, D.: Longitudinal analysis of the effectiveness of DCPS teachers. Mathematica Policy Research Reports. Mathematica Policy Research. https://ideas.repec.org/p/mpr/mprres/65770df94dde4573b331ce1cb33a9e07.html. Accessed 21 Apr 21 2018

  18. Isenberg, E., Hock, H.: Measuring School and Teacher Value Added in DC, 2011–2012 School Year. Mathematica Policy Research, Inc., 31 August 2012. https://eric.ed.gov/?id=ED565712

  19. American Statistical Association: ASA statement on using value added models for educational assessment, Alexandria, VA (2014)

    Google Scholar 

  20. http://static.battelleforkids.org/documents/HISD/EVAAS-Value-Added-FAQs-Final-2015-02-02.pdf

  21. Matthias, A.: The responsibility gap: ascribing responsibility for the actions of learning automata. Ethics Inf. Technol. 6(3), 175–183 (2004). https://doi.org/10.1007/s10676-004-3422-1

    Article  Google Scholar 

  22. Oremus, W., Carey, B.: Facebook’s offensive ad targeting options go far beyond ‘Jew Haters.’ Slate, 14 September 2017. http://www.slate.com/blogs/future_tense/2017/09/14/facebook_let_advertisers_target_jew_haters_it_doesn_t_end_there.html

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alan Rubel .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rubel, A., Pham, A., Castro, C. (2019). Agency Laundering and Algorithmic Decision Systems. In: Taylor, N., Christian-Lamb, C., Martin, M., Nardi, B. (eds) Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science(), vol 11420. Springer, Cham. https://doi.org/10.1007/978-3-030-15742-5_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-15742-5_56

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15741-8

  • Online ISBN: 978-3-030-15742-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics