Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/151549
Title: ¿Discriminadas sistemáticamente por los algoritmos? Un estudio preliminar del impacto del uso de herramientas de selección de talento alimentadas/potenciadas por IA (Inteligencia Artificial)
Other Titles: Systematically discriminated by algorithms? A preliminary study on the impact of AI-powered hiring tools
Author: Martinez Elmasri, Azahar
Tutor: Palmen, Rachel  
Abstract: Since the explosion of generative AI with the launch of ChatGPT, the economic activity that was already in the process of digitization, automation, and robotization has undergone another revolution. The expansion and implications of this new disruptive technology have been felt in various sectors, and People & Culture departments are not immune to these transformations. In this study, we question and inquire about the utility and potential of AI tools used in attraction and selection of individuals, employing qualitative methodology. Faced with the foreseeable exponential increase in the offer of services and products with AI that, under the pretext of streamlining and optimizing recruitment and increasing diversity while combating unconscious biases, that will tempt departments to acquire such tools, we propose a critical reflection, with a gender and intersectionality perspective, regarding the impact of these technologies. To this end, we analyze, from a techno-feminist and constructivist perspective, the emergence of AI, how discriminations penetrate designs and datasets, the existing regulatory framework, and its potential as a tool to promote diversity in organizations. We delve into these aspects with the input of key collaborators from both the world of AI design and data management, as well as the People & Culture, conducting a Focus Group and nine in-depth interviews. These dynamics help us reach conclusions and recommend a set of best practices, among which the use of Explainable AI or AIs generated within the framework of Design Thinking/Justice stand out, as well as paying special attention to and auditing the data processing that AI ingests.
Keywords: IA ética; sesgo género; DEI; selección; cualitativa; interseccionalidad
Document type: info:eu-repo/semantics/masterThesis
Issue Date: 25-Feb-2024
Publication license: http://creativecommons.org/licenses/by-nc-nd/3.0/es/  
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