Demographic gender bias in Artificial Intelligence
Authorship
L.A.B.L.
Master in Philosophy: Knowledge and Citizenship
L.A.B.L.
Master in Philosophy: Knowledge and Citizenship
Defense date
02.13.2025 10:00
02.13.2025 10:00
Summary
The study analyses the problem of gender bias in the artificial intelligence environment using theoretical knowledge to solve its complexity. Initially, it classifies the categories of bias by responding to the group contexts in implicit psychological tests (IATs). The results are processed into semantic and syntactic representations using natural language conceptualization. Word associations (WEAT) are processed using optimization vectors in coding and translation circuits. Next, some methods of elimination, statistical summaries, and contextualizations of occupational and sectoral contexts are contrasted. In this way, it is possible to arrive at objective relationships of the environments, and profiles of the characters by certain socio-demographic criteria, and gender indices. Finally, the ethical and cultural impact is analysed by observing the existing rules guides reflecting on existing knowledge.
The study analyses the problem of gender bias in the artificial intelligence environment using theoretical knowledge to solve its complexity. Initially, it classifies the categories of bias by responding to the group contexts in implicit psychological tests (IATs). The results are processed into semantic and syntactic representations using natural language conceptualization. Word associations (WEAT) are processed using optimization vectors in coding and translation circuits. Next, some methods of elimination, statistical summaries, and contextualizations of occupational and sectoral contexts are contrasted. In this way, it is possible to arrive at objective relationships of the environments, and profiles of the characters by certain socio-demographic criteria, and gender indices. Finally, the ethical and cultural impact is analysed by observing the existing rules guides reflecting on existing knowledge.
Direction
LATORRE RUIZ, ENRIQUE (Tutorships)
LATORRE RUIZ, ENRIQUE (Tutorships)
Court
DOLDAN GARCIA, XOAN RAMON (Chairman)
CONDE SOTO, FRANCISCO (Secretary)
Donato Rodríguez, Javier de (Member)
DOLDAN GARCIA, XOAN RAMON (Chairman)
CONDE SOTO, FRANCISCO (Secretary)
Donato Rodríguez, Javier de (Member)