Artificial Intelligence

Algorithmic Bias: Looking Beyond Data Bias to Ensure Algorithmic Accountability and Equity

Savannah Thais3 Hannah Shumway , and Austin Iglesias Saragih

Savannah Thais*, Hannah Shumway, and Austin Iglesias Saragih

Edited by Audrey Bertin and Sarah Radway

Review | Aug. 31 2023

*Email: st3565@columbia.edu

DOI: 10.38105/spr.5lwvw66ssy

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Highlights

  • A large portion of academic literature as well as proposed and enacted US policies commonly focus on quantitative notions of fairness and data bias when they assess algorithmic bias. This has limited practical utility.
  • Few algorithmic bias-focused policies have actually been enacted in the United States, but those that have center on temporary bans, transparency, and post-hoc bias audits.
  • A holistic and collective approach is likely the most promising for meaningfully addressing issues of algorithmic bias.

Article Summary

As algorithms increasingly aid public sector decision making in the United States, it becomes important to understand how to effectively tackle algorithmic bias in systems that local, state, and federal government entities use and procure, including what kinds of policies are currently in place or proposed. There is a prevalent belief that algorithmic bias arises primarily from statistical biases present in the data used to train or develop the algorithm, but bias may also arise during data collection, problem specification, how and where algorithms are deployed, and within the broader societal contextualization of algorithms. So far, enacted policies in in the US center on temporary bans of particular types of algorithms, transparency, and post-hoc bias audits, as well as more wide-ranging (but non-binding) policy frameworks; all largely focus on quantitative notions of fairness when they assess bias, leaving room for more comprehensive legislation to meaningfully address this issue going forward.

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This MIT Science Policy Review article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/ by/4.0/.

Savannah Thais

Data Science Institute, Columbia University, New York, NY

Hannah Shumway

Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA

Austin Iglesias Saragih

Center for Transportation and Logistics, Massachusetts Institute of Technology, Cambridge, MA