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research-article

Managing Bias in AI

Authors:

Jeanna Matthews,

Nisha TalagalaAuthors Info & Claims

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Pages 539 - 544

https://doi.org/10.1145/3308560.3317590

Published: 13 May 2019 Publication History

Abstract

Recent awareness of the impacts of bias in AI algorithms raises the risk for companies to deploy such algorithms, especially because the algorithms may not be explainable in the same way that non-AI algorithms are. Even with careful review of the algorithms and data sets, it may not be possible to delete all unwanted bias, particularly because AI systems learn from historical data, which encodes historical biases. In this paper, we propose a set of processes that companies can use to mitigate and manage three general classes of bias: those related to mapping the business intent into the AI implementation, those that arise due to the distribution of samples used for training, and those that are present in individual input samples. While there may be no simple or complete solution to this issue, best practices can be used to reduce the effects of bias on algorithmic outcomes.

References

[1]

Witten, H., Frank, E., and Hall, M.A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kaufmann Publishers Inc., San Francisco, CA.

Digital Library

[2]

McKinsey Global Institute (2017). “Artificial Intelligence: The Next Digital Frontier?”

[3]

Goodman, B. and Flaxman, S. (2016). “European Union regulations on algorithmic decision-making and a 'right to explanation'”. ICML Workshop on Human Interpretability in Machine Learning, New York, NY.

[4]

Kahn, J. (2018). “Artificial Intelligence Has Some Explaining to Do”. Bloomberg Businessweek, 12 Dec 2018.

[5]

Bolukbasi. T., Chang, K., Zou, J., Saligrama, A., and Kalai, A. (2016). “Man is to computer programmer as woman is to homemaker? Debiasing word embeddings”. Proceedings of the 30th International Conference on Neural Information Processing Systems.Barcelona, Spain.

Digital Library

[6]

Angwin, J., Larson, J, Mattu, S., and Kirchner, L. (2016). “Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks”. ProPublica, 23 May 2016.

[7]

Ensign, D., Friedler, S.A., Neville, S., Scheidegger, C. and Venkatasubramanian, S. (2018). “Runaway Feedback Loops in Predictive Policing”. Proceedings of Machine Learning Research.

[8]

Hao, K. (2019). “This is How AI Bias Really Happens -— and Why It's so Hard to Fix”. MIT Technology Review, 4 Feb 2019.

[9]

Dastin, J. (2018). “Amazon scraps secret AI recruiting tool that showed bias against women”. Reuters Business News, 10 Oct 2018.

[10]

Del Balso, M. and Hermann, J. (2017). “Meet Michelangelo: Uber's Machine Learning Platform”. Uber Engineering, 5 Sep 2017.

[11]

Baylor. D. (2017). “TFX: A TensorFlow-Based Production-Scale Machine Learning Platform”. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Nova Scotia, Canada.

Digital Library

[12]

Vincent, J. (2016). “Twitter taught Microsoft's AI chatbot to be a racist asshole in less than a day”. The Verge, 24 Mar 2016.

[13]

Bursztein, E. (2018). “Attacks against machine learning - an overview”. https://elie.net/blog/ai/attacks-against-machine-learning-an-overview/.

[14]

Buolamwini, J. (2019). “Aritificial Intelligence Has a Problem with Gender and Racial Bias”. TIME, 7 Feb 2019.

[15]

Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). “’Why Should I Trust You?’: Explaining the Predictions of Any Classifier”. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, CA.

Digital Library

[16]

Google. "TensorFlow Hub," https://www.tensorflow.org/hub.

[17]

H2O. “Open Source Leader in AI and ML”. h2o, https://www.h2o.ai/.

[18]

European Commission. “Rights for Citizens - European Commission”. https://ec.europa.eu/info/law/law-topic/data-protection/reform/rights-citizens_en.

[19]

Board of Governors of the Federal Reserve System (2011). “The Fed - Supervisory Letter SR 11-7 on Guidance on Model Risk Management”. https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm.

[20]

Suciu, O., Marginean, R., Kaya, Y., Daume. H. III, and Dumitras, T. (2018). “When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks”.27th USENIX Security Symposium, Baltimore, MD.

Digital Library

[21]

IBM. “AI Fairness 360”. http://aif360.mybluemix.net/.

[22]

H2O. “Interpreting a Model — Using Driverless AI”. https://www.h2o.ai/wp-content/uploads/2017/09/driverlessai/interpreting.html.

[23]

Ghanta, S. and Talagala N. (2018). “MLOps Health: Taking the Pulse of ML in Production”. ITOpsTimes, 10 July 2018.

[24]

Chen, I.Y., Johansson, F.D., and Sontag, D. (2018). “Why Is My Classifier Discriminatory?”.32nd Conference on Neural Information Processing Systems, Montreal, Canada.

Digital Library

[25]

Kroll, J., Huey, J., Barocas, S, Felten, E., Reidenberg, J., Robinson, D., and Yu, H. (2017). “Accountable Algorithms”. University of Pennsylvania Law Review, vol. 165, pp. 633-705.

[26]

Cramer, H., Garcia-Gathright, J., Springer, A, and Reddy, S. (2018). “Assessing and addressing algorithmic bias in practice”. ACM Interactions, vol. XXV, no. 6, pp. 58-63.

Digital Library

[27]

Bier, D. (2017). “E-Verify Has Delayed or Cost Half a Million Jobs for Legal Workers”. CATO Institute, 16 May 2017. https://www.cato.org/blog/e-verify-has-held-or-cost-jobs-half-million-legal-workers.

[28]

Suhartono, H., Levin, A., and Johnsson, J. (2018). “Why Did Lion Air Flight 610 Crash? New Report Details Struggle”. Bloomberg, 27 Nov 2018. https://www.bloomberg.com/news/articles/2018-11-27/lion-air-pilots-struggle-detailed-in-preliminary-crash-report.

[29]

0'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown. New York, NY.

Digital Library

[30]

Buolamwini, J. and Gebru, T. (2018). “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification”. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, New York, NY.

[31]

Buranyi, S. (2017). “Rise of the racist robots - how AI is learning all our worse impulses”. The Guardian, 8 Aug 2017. https://www.theguardian.com/inequality/2017/aug/08/rise-of-the-racist-robots-how-ai-is-learning-all-our-worst-impulses.

[32]

Nicholas Diakopoulos and Sorelle Friedler (2016). How to Hold Algorithms Accountable, MIT Technology Review, November 17, 2016. https://www.technologyreview.com/s/602933/how-to-hold-algorithms-accountable/

[33]

Garfinkel, S., Matthews, J., Shapiro, S., and Smith, J. (2017). Toward Algorithmic Transparency and Accountability. Communications of the ACM. Vol. 60, No. 9, Page 5, Sept. 2017.

Digital Library

[34]

ACM US Public Policy Council (USACM) (2017). Statement on Algorithmic Transparency and Accountability. January 12, 2017. https://www.acm.org/binaries/content/assets/public-policy/2017_usacm_statement_algorithms.pdf

[35]

World Wide Web Foundation (2017). Algorithmic accountability report, 2017. https://webfoundation.org/docs/2017/07/Algorithms_Report_WF.pdf

[36]

Reisman, D., Schultz, J., Crawford, K., and Whittaker, M. (2018). Algorithmic impact assessments: A practical framework for public agency accountability. AI Now institute. Apr. 2018. https://ainowinstitute.org/aiareport2018.pdf

[37]

Caplan, R., Donovan, J., Hanson, L., and Matthews, J. (2018). Algorithmic accountability primer. Data & Society. Apr. 2018. https://datasociety.net/wp-content/uploads/2018/04/Data_Society_Algorithmic_Accountability_Primer_FINAL-4.pdf

[38]

Friedman, B. and Nissenbaum, H. (1996). Bias in computer systems. ACM Trans. Inf. Syst. 14, 3, 330–347.

Digital Library

[39]

Olteanu, A., Castillo, C, Diaz, F., and Kiciman, E. (2016). Social data: Biases, methodological pitfalls, and ethical boundaries.

[40]

Baeza-Yates, R. (2016). Data and algorithmic bias in the web. Proceedings of the 8th ACM Conference on Web Science. ACM. New York.

Digital Library

Cited By

Patil PKulkarni KSharma P(2024)Algorithmic Issues, Challenges, and Theoretical Concerns of ChatGPTApplications, Challenges, and the Future of ChatGPT10.4018/979-8-3693-6824-4.ch003(56-74)Online publication date: 28-May-2024
https://doi.org/10.4018/979-8-3693-6824-4.ch003
Bhavana SJayashree KRao T(2024)Navigating AI Biases in EducationAI Applications and Strategies in Teacher Education10.4018/979-8-3693-5443-8.ch005(135-160)Online publication date: 25-Oct-2024
https://doi.org/10.4018/979-8-3693-5443-8.ch005
Monica MPatel SRamanaiah GManoharan SGhilan T(2024)Promoting Fairness and Ethical Practices in AI-Based Performance Management SystemsAdvancements in Intelligent Process Automation10.4018/979-8-3693-5380-6.ch007(155-178)Online publication date: 20-Sep-2024
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Index Terms

Managing Bias in AI
1. Computing methodologies
2. Social and professional topics
  1. Professional topics

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Other conferences

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

May 2019

1331 pages

ISBN:9781450366755

DOI:10.1145/3308560

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 May 2019

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WWW '19: The Web Conference

May 13 - 17, 2019

San Francisco, USA

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Cited By

Patil PKulkarni KSharma P(2024)Algorithmic Issues, Challenges, and Theoretical Concerns of ChatGPTApplications, Challenges, and the Future of ChatGPT10.4018/979-8-3693-6824-4.ch003(56-74)Online publication date: 28-May-2024
https://doi.org/10.4018/979-8-3693-6824-4.ch003
Bhavana SJayashree KRao T(2024)Navigating AI Biases in EducationAI Applications and Strategies in Teacher Education10.4018/979-8-3693-5443-8.ch005(135-160)Online publication date: 25-Oct-2024
https://doi.org/10.4018/979-8-3693-5443-8.ch005
Monica MPatel SRamanaiah GManoharan SGhilan T(2024)Promoting Fairness and Ethical Practices in AI-Based Performance Management SystemsAdvancements in Intelligent Process Automation10.4018/979-8-3693-5380-6.ch007(155-178)Online publication date: 20-Sep-2024
https://doi.org/10.4018/979-8-3693-5380-6.ch007
Nadi AAhshan KRahman SSofin M(2024)The Role of Artificial Intelligence in Business ManagementUtilizing AI and Smart Technology to Improve Sustainability in Entrepreneurship10.4018/979-8-3693-1842-3.ch008(117-133)Online publication date: 18-Mar-2024
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Mengesha GBelay EAdams R(2024)Technical Considerations for Designing, Developing, and Implementing AI Systems in AfricaExamining the Rapid Advance of Digital Technology in Africa10.4018/978-1-6684-9962-7.ch005(86-104)Online publication date: 23-Feb-2024
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Liu Z(2024)Cultural Bias in Large Language Models: A Comprehensive Analysis and Mitigation StrategiesJournal of Transcultural Communication10.1515/jtc-2023-0019Online publication date: 16-Sep-2024
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