Interactive Web Column: Machine Learning Algorithms, In and Out of Libraries

Abstract

These days, algorithms make a growing number of consequential decisions for and about us, whether it’s the news we get, the kinds of ads we view, or what route we take to work. As libraries move toward adopting algorithms for information retrieval and discovery, it is important that we educate our consumers and ourselves about their pitfalls and promise.

Keywords:

• Algorithms
• machine learning
• bias
• collection development
• discovery

Notes

Notes

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9 Fernandez, “Through the Looking Glass,” 5–8.

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11 Griffey, “Introduction”, 5–9

12 A. Howard, and J. Borenstein, “The Ugly Truth about Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity,” Science and Engineering Ethics 5, no. 24 (2018): 1521–1536.

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

14 R. Tatman, “Gender and Dialect Bias in YouTube Automatic Captions” (Paper presented at the First Workshop on Ethics in Natural Language Processing, Valencia, Spain, April 4, 2017).

15 R. Courtland, “The Bias Detectives,” Nature 558, no. 7710 (2018): 357–360.

16 Noble, "Algorithms of Oppression"

17 R. Caplan, L. Hanson, J. Donovan, and J. Matthews, “Algorithmic Accountability: A Primer,” in Tech Algorithm Briefing: How Algorithms Perpetuate Racial Bias and Inequality (Data and Society, 2018).

18 Reidsma, “Algorithmic Bias”