The study explores the multifaceted impact of Artificial Intelligence (AI) on societal inequalities, focusing on how AI can perpetuate and exacerbate bias. It traces the evolution of AI foundational concepts to modern generative technologies. The discussion highlights the intrinsic link between AI, data, and bias, followed by in-depth case studies illustrating real world implications. An overview of international standards and regulations, including those by ILO, UNESCO, and the EU, is provided to contextualize efforts to mitigate bias. The study concludes with examples of successful initiatives promoting equity in AI applications, offering a hopeful outlook for addressing these challenges.
The study explores the multifaceted impact of Artificial Intelligence (AI) on societal inequalities, focusing on how AI can perpetuate and exacerbate bias. It traces the evolution of AI foundational concepts to modern generative technologies. The discussion highlights the intrinsic link between AI, data, and bias, followed by in-depth case studies illustrating real world implications. An overview of international standards and regulations, including those by ILO, UNESCO, and the EU, is provided to contextualize efforts to mitigate bias. The study concludes with examples of successful initiatives promoting equity in AI applications, offering a hopeful outlook for addressing these challenges.
ADRESSING BIAS IN ARTIFICIAL INTELLIGENCE (AI). How Artificial Intelligence increases the inequalities.
ONAT, KUBRA
2023/2024
Abstract
The study explores the multifaceted impact of Artificial Intelligence (AI) on societal inequalities, focusing on how AI can perpetuate and exacerbate bias. It traces the evolution of AI foundational concepts to modern generative technologies. The discussion highlights the intrinsic link between AI, data, and bias, followed by in-depth case studies illustrating real world implications. An overview of international standards and regulations, including those by ILO, UNESCO, and the EU, is provided to contextualize efforts to mitigate bias. The study concludes with examples of successful initiatives promoting equity in AI applications, offering a hopeful outlook for addressing these challenges.File | Dimensione | Formato | |
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OnatKubra_EGOS_2041203.pdf
embargo fino al 29/11/2025
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https://hdl.handle.net/20.500.12608/77561