About this document
Queering AI: Addressing Gender Bias by resourcesssh is a document available to read on EtoBox.
This study examines the implications of gender bias in AI-driven systems through queer theory, emphasizing the marginalization of non-binary and gender-diverse identities. It advocates for inclusive datasets, ethical AI design, and interdisciplinary collaboration to mitigate biases and promote equitable technological landscapes. The research aims to inform AI governance and contribute to the discourse on AI ethics by integrating diverse perspectives and fostering responsible AI practices.
- Author
- resourcesssh
- Language
- EN