Injusticia epistémica y reproducción de sesgos de género en la inteligencia artificial

  1. Perdomo Reyes, Inmaculada 1
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Journal:
CTS: Revista iberoamericana de ciencia, tecnología y sociedad

ISSN: 1668-0030,  1850-0013

Year of publication: 2024

Volume: 19

Issue: 56

Pages: 89-100

Type: Article

DOI: 10.52712/ISSN.1850-0013-555 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: CTS: Revista iberoamericana de ciencia, tecnología y sociedad

Abstract

Generative AIs reify and circulate existing gender gaps and biases, but give them a veneer of objectivity and neutrality despite the opacity of their processes and ability to reproduce and increase situations of inequality and exclusion. The situation is one of clear algorithmic and epistemic injustice that confronts us with major challenges in our modern democracies. With examples of specific cases and with the critical review of important texts that offer interpretative keys to understand the impact of the rapid development and implementation of these tools, we will outline some guidelines that will require more in-depth studies, but that aim to collect, from the perspective of science, technology and gender studies, new challenges for the development of the discipline and to envision the possibilities of a feminist AI.

Bibliographic References

  • Coeckelbergh, M. (2021). Ética de la Inteligencia Artificial. Madrid: Cátedra.
  • Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press.
  • Criado Pérez, C. (2019). Invisible Women: Exposing Data Bias in a World Designed for Men. Londres: Chatto & Windus.
  • Dastin, J. (2022). Amazon scraps secret AI recruiting tool that showed bias against women. En K. Martin (Comp.), Ethics of Data and Analytics. Concepts and cases (296-299). CRC Press Taylor & Francis Group.
  • Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police and Punish the Poor. Nueva York: St. Martin’s Press
  • Fricker, M. (2007). Epistemic Injustice. Power & the Ethics of Knowing. Oxford: Oxford University Press.
  • Gray, M. & Suri, S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Boston: Harcourt.
  • Han, B. C. (2022). Infocracia. Madrid: Taurus.
  • Hayles, N. K. (2023). Technosymbiosis: Figuring (Out) Our Relations to AI. En Feminism and AI. En J. Browne, S. Cave, E. Drage & K. McInerney (Eds.), Feminist AI: Critical Perspectives on Algorithms, Data, and Intelligent Machines (1-18). Oxford: Oxford University Press.
  • Jasanoff, S. (2004). States of Knowledge. The Co-production of Science and Social Order. Londres: Routledge.
  • Jasanoff, S. (2016). The Ethic of Invention. Technology and The Human Future. Nueva York: W.W. Norton & Company Ltd.
  • Larson, E. J. (2022). El mito de la Inteligencia Artificial. Por qué las máquinas no pueden pensar como nosotros lo hacemos. Barcelona: Shackleton Books.
  • O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Londres: Allen Lane.
  • Perdomo, I. (2024). Tecnociencia feminista. Una propuesta de demarcación. Revista Iberoamericana de Ciencia, Tecnología y Sociedad -CTS, 19(55), 127-143. DOI: https://doi.org/10.52712/issn.1850-0013-424.
  • Scavino, D. (2022). Máquinas filosóficas. Problemas de cibernética y desempleo. Barcelona: Anagrama.
  • Toupin, S. (2024). Shaping feminist artificial intelligence. New Media & Society, 26(1), 580-595. Sage Journals. DOI: https://doi.org/10.1177/14614448221150776.
  • Van Noorden, R. & Perkel, J. (2023). AI and Science: what 1600 researchers think. Nature, vol. 621, 28 de septiembre de 2023, 672-675. DOI: https://doi.org/10.1038/d41586-023-02980-0.
  • Wajcman, J. (2010). Feminist Theories of Technology. Cambridge Journal of Economics, 34(1), 144.
  • Wajcman, J. & Young, E. (2023). Feminism Confronts AI. En Feminism and AI. En J. Browne, S. Cave, E. Drage & K. McInerney (Eds.), Feminist AI: Critical Perspectives on Algorithms, Data, and Intelligent Machines (47-64). Oxford: Oxford University Press.
  • Young, E., Wajcman, J. & Sprejer, L. (2023). Mind the gender gap: inequalities in the emergent professions of artificial intelligence (AI) and data science. New Technology, Work and Employment, 1-24. DOI: https://doi.org/10.1111/ntwe.12278.
Data source: Dialnet