Analysis of Islamophobic Bias and Religious Discrimination in AI Datasets
An academic study written by Bakht Munir examines Islamophobic biases in artificial intelligence models and the role of datasets.
A comprehensive academic analysis published in the Law Library Journal addresses Islamophobic biases emerging in artificial intelligence systems and large language models, the underlying dataset issues, and potential solutions.
Religious Biases in Artificial Intelligence
As artificial intelligence technologies rapidly integrate into many areas of society, they continue to face ethical concerns and particularly problems with bias. The outputs of generative artificial intelligence models rely directly on training data.
Islamophobia and Model Results
Islamophobic bias manifests when artificial intelligence models produce negative outcomes due to biased datasets or affected model designs, associating Muslims with violence more frequently compared to other groups.
Solutions and Mitigation Techniques
Although completely eliminating inherent religious biases in historical datasets is difficult, diverse design teams, high-quality data, and active end-user engagement are among the proposed solutions.