Resisting Dehumanization in the Age of “AI”

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Abstract

The production and promotion of “AI” technology involves dehumanization on many fronts. I explore these processes of dehumanization and the role that cognitive science can play by bringing a richer picture of human cognition to the discourse.

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Most cited references 25

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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

Emily Bender, Timnit Gebru, Angelina McMillan-Major … (2021)

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Energy and Policy Considerations for Deep Learning in NLP

Emma Strubell, Ananya Ganesh, Andrew McCallum (2019)

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Word embeddings quantify 100 years of gender and ethnic stereotypes

Dan Jurafsky, Nikhil Garg, James Zou … (2018)

Word embeddings are a popular machine-learning method that represents each English word by a vector, such that the geometry between these vectors captures semantic relations between the corresponding words. We demonstrate that word embeddings can be used as a powerful tool to quantify historical trends and social change. As specific applications, we develop metrics based on word embeddings to characterize how gender stereotypes and attitudes toward ethnic minorities in the United States evolved during the 20th and 21st centuries starting from 1910. Our framework opens up a fruitful intersection between machine learning and quantitative social science. Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts—e.g., the women’s movement in the 1960s and Asian immigration into the United States—and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.

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Author and article information

Contributors

Emily M. Bender: (View ORCID Profile)

Journal

Title: Current Directions in Psychological Science

Abbreviated Title: Curr Dir Psychol Sci

Publisher: SAGE Publications

ISSN (Print): 0963-7214

ISSN (Electronic): 1467-8721

Publication date (Electronic): February 02 2024

Affiliations

[1 ]Department of Linguistics, University of Washington

Article

DOI: 10.1177/09637214231217286

SO-VID: 2dbe454d-2ef3-4db6-928f-8b8a36249d39

License:

https://journals.sagepub.com/page/policies/text-and-data-mining-license

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