Advertisement

Customer Needs and Solutions

, Volume 6, Issue 3–4, pp 84–91 | Cite as

Technological Workforce and Its Impact on Algorithmic Justice in Politics

  • Jerome D. Williams
  • David Lopez
  • Patrick Shafto
  • Kyungwon LeeEmail author
Research Article
  • 4 Downloads

Abstract

The use of algorithms can be highly beneficial and efficient to make statistical decisions in settings where data are voluminous. However, there are on-going concerns about the potential long-term negative consequences of the use of algorithms due to inherent biases against certain subgroups of the population which tend to be under-represented in the society. To address this issue, we propose that it is critical to develop ways to bring the technological capabilities that underlie these advances to the broadest group of people by focusing on the nature of workforce in the tech industry. Particularly, we propose that having a diverse workforce in the tech industry and inter-disciplinary education, including principles of ethical coding, can be a starting point to resolve this issue. Politicians, regulators, and educational institutions must be prepared to address these issues in order to set a system that works equally for all people in a democratic society.

Keywords

Algorithmic justice Big data Tech industry Diverse workforce Political impact 

References

  1. 1.
    Ajunwa I (2018) Algorithms at work: productivity monitoring platforms and wearable technology as the new data-centric research agenda for employment and labor law. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3247286. Accessed 3 Mar 2019
  2. 2.
    Angwin J, Larson J, Mattu S, Kirchner L (2016) Machine bias risk assessments in criminal sentencing. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Accessed 3 March 2019
  3. 3.
    Angwin J, Scheiber N, Tobin A (2017) Facebook job ads raise concerns about age discrimination, New York Times. https://www.nytimes.com/2017/12/20/business/facebook-job-ads.html. Accessed 4 March 2019
  4. 4.
    Apple. Inclusion and diversity. https://www.apple.com/diversity/. Accessed 5 August 2019
  5. 5.
    Assistant Secretary for Fair Housing and Equal Opportunity (2018) Housing discrimination complaint. https://www.hud.gov/sites/dfiles/PIH/documents/HUD_01-18-0323_Complaint.pdf. Accessed 4 March 2019
  6. 6.
    Barocas S, Selbst A (2016) Big data's disparate impact, Calif. L. Rev 104. 607.https://ssrn.com/abstract=2477899. Accessed 3 Mar 2019
  7. 7.
    Berlatsky N (2018) Google search algorithms are not impartial, NBC News. https://www.nbcnews.com/think/opinion/google-search-algorithms-are-not-impartial-they-are-biased-just-ncna849886. Accessed 3 March 2019
  8. 8.
    Bertrand M, Mullainathan S (2004) Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. Am Econ Rev 94(4):991–1013CrossRefGoogle Scholar
  9. 9.
    Bodle R (2014) Predictive algorithms and personalization services on social network sites: implications for users and society. In: Bechmann A, Lombarg S (eds) The ubiquitous internet: User and industry perspectives, 1st edn, Routledge, pp 130–144Google Scholar
  10. 10.
    Booker B (2019) Housing department slaps Facebook with discrimination charge. NPR. https://www.npr.org/2019/03/28/707614254/hud-slaps-facebook-with-housing-discrimination-charge. Accessed 5 May 2019
  11. 11.
    Brauneis R, Goodman E (2018) Algorithmic transparency for the smart city. The Yale Journal of Law & Technology 20:103–176Google Scholar
  12. 12.
    Buolamwini J, Gebru T (2018) Gender shades: intersectional accuracy disparities in commercial gender classification. Conference on Fairness, Accountability and Transparency. 77–91Google Scholar
  13. 13.
    Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook. https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm. Accessed 3 March 2019
  14. 14.
    Chin C (2018) AI is the future—but where are the women?. Wired. https://www.wired.com/story/artificial-intelligence-researchers-gender-imbalance/. Accessed 3 March 2019
  15. 15.
    Chouldechova A (2017) Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2):153–163CrossRefGoogle Scholar
  16. 16.
    EEOC, Special Reports, Diversity in High Tech. https://www.eeoc.gov/eeoc/statistics/reports/hightech/. Accessed 3 March 2019
  17. 17.
    Elberfeld J (2019) The economic imperative of diversity in tech. Forbes. https://www.forbes.com/sites/forbestechcouncil/2019/04/25/the-economic-imperative-of-diversity-in-tech/#5bb9700910d2. Accessed 5 August 2019
  18. 18.
    Fong-Jones L (2019) Google workers lost a leader, but the fight will continue. Medium. https://medium.com/s/story/google-workers-lost-a-leader-but-the-fight-will-continue-c487aa5fd2ba. Accessed 4 March 2019
  19. 19.
    Godsil RD (2016) Why race matters in physics class. UCLA Law Review Discourse 64:40–63Google Scholar
  20. 20.
    Godsil RD, Tropp LR, Goff PA, Powell JA (2014) Addressing implicit bias, racial anxiety, and stereotype threat in education and health care. The Science of Equality 1. http://perception.org/wp-content/uploads/2014/11/Science-of-Equality.pdf. Accessed 3 Mar 2019
  21. 21.
  22. 22.
    Guynn J (2017) Russians used Facebook the way other advertisers do: by tapping into its data-mining machine, USA Today. https://www.usatoday.com/story/tech/news/2017/11/01/russians-used-facebook-way-other-advertisers-do-tapping-into-its-data-mining-machine/817826001/. Accessed 3 March 2019
  23. 23.
    Guynn J (2018) Facebook limits ad targeting after Cambridge Analytica data leak, USA Today. https://www.usatoday.com/story/tech/news/2018/03/28/facebook-limits-ad-targeting-after-cambridge-analytica-data-leak/468470002/. Accessed 29 March 2018
  24. 24.
    Halpern S (2018) How campaigns are using marketing, manipulation, and “psychographic targeting” to win elections-and weakens demogracy. The New Republica. https://newrepublic.com/article/151548/political-campaigns-big-data-manipulate-elections-weaken-democracy. Accessed 31 May 2019
  25. 25.
    Hoffman S (2018) Big data analytics: what can go wrong. Indiana Health Law Review 15:227–246CrossRefGoogle Scholar
  26. 26.
    Hunt V, Layton D, Prince S (2015) Why diversity matters. McKinsey & Company, 1:15–29. https://www.mckinsey.com/~/media/mckinsey/business%20functions/organization/our%20insights/why%20diversity%20matters/diversity%20matters.ashx. Accessed 5 March 2019
  27. 27.
    Hyde A (2003) Working in Silicon Valley: economic and legal analysis of a high-velocity labor market. Routledge, New YorkCrossRefGoogle Scholar
  28. 28.
    Jackie S (2018) Google photos still has a problem with gorillas. MIT Technology Review. https://www.technologyreview.com/f/609959/google-photos-still-has-a-problem-with-gorillas. Accessed 3 Mar 2019
  29. 29.
    Kirchner L (2015) When big data becomes bad data. ProPublica. https://www.propublica.org/article/when-big-data-becomes-bad-data. Accessed 3 March 2019
  30. 30.
    Moritz H, Price E, Srebro N (2016) Equality of opportunity in supervised learning. Adv Neural Inf Proces Syst 29:3315–3323Google Scholar
  31. 31.
    Nasraoui O, Shafto P (2016) Human-algorithm interaction biases in the big data cycle: a Markov chain iterated learning framework. https://arxiv.org/pdf/1608.07895.pdf. Accessed 4 Mar 2019
  32. 32.
    Newcombe C (2013) When bad data happens to good companies. SAS Best Practices. https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/bad-data-good-companies-106465.pdf. Accessed 3 Mar 2019
  33. 33.
    Newman R, Grewal E (2016) Beginning with ourselves. Medium. https://medium.com/airbnb-engineering/beginning-with-ourselves-48c5ed46a703. Accessed 25 August 2019
  34. 34.
    O'neil C (2016) Weapons of math destruction: how big data increases inequality and threatens democracy. Broadway Books, New YorkGoogle Scholar
  35. 35.
    Pinterest (2015) Our plan for a more diverse Pinterest. https://newsroom.pinterest.com/en/post/our-plan-for-a-more-diverse-pinterest. Accessed 25 August 2019
  36. 36.
    Rand K (2018) How big data has changed politics. An inside look into how big data is revolutionizing politics, for better or worse. CIO. https://www.cio.com/article/3285710/how-big-data-has-changed-politics.html. Accessed 5 August 2019
  37. 37.
    Rangarajan S (2018) Here’s the clearest picture of Silicon Valley’s diversity yet: its’ bad. But some companies are doing less bad. https://www.revealnews.org/article/heres-the-clearest-picture-of-silicon-valleys-diversity-yet/. Accessed 25 August 2019
  38. 38.
    Russell SJ, Norvig P (2006) Artificial intelligence: a modern approach. Pearson Education Limited, LondonGoogle Scholar
  39. 39.
    SAS. Machine Learning. https://www.sas.com/en_us/insights/analytics/machine-learning.html. Acesssed 15 August 2019
  40. 40.
    Silberg J, Manyika J (2019) Notes from the AI frontiers: tackling bias in AI (and in humans). McKinsey Global Institute. https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Artificial%20Intelligence/Tackling%20bias%20in%20artificial%20intelligence%20and%20in%20humans/MGI-Tackling-bias-in-AI-June-2019.ashx. Accessed 15 August 2019
  41. 41.
    Stephens-Davidwitz S (2017) Everybody lies: big data, new data and what the internet can tell us about who we really are. HarperCollins, New YorkGoogle Scholar
  42. 42.
    StatCounter. Search engine market share worldwide. https://gs.statcounter.com/search-engine-market-share. Accessed 1 Sept 2019
  43. 43.
    Wakabayashi D, Benner K (2018) How Google protected Andy Rubin, the ‘father of Android’. The New York Times. https://www.nytimes.com/2018/10/25/technology/google-sexual-harassment-andy-rubin.html. Accessed 4 March 2019
  44. 44.
    WhiteHouse (2014) Big data: Seizing opportunities, preserving values. Executive Office of the President. https://obamawhitehouse.archives.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf. Accessed 5 March 2019

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Rutgers Business SchoolRutgers-The State University of New JerseyNewarkUSA
  2. 2.Rutgers Law SchoolRutgers-The State University of New JerseyNewarkUSA
  3. 3.Department of Mathematics and Computer ScienceRutgers-The State University of New JerseyNewarkUSA
  4. 4.College of BusinessUniversity of Michigan-DearbornDearbornUSA

Personalised recommendations