Embedded Biases and Digital Islamophobia: The Socio-Technical Perspectives of AI Literacy

Artificial intelligence literacy refers to the foundational competence of individuals to understand the core principles, architectural structures, and operational dynamics of AI systems. In this context, AI-literate individuals are familiar with essential concepts from subfields such as machine learning, deep learning, and natural language processing. They are also able to distinguish methodological and functional differences across various AI platforms, applications, and model types. Recognizing ethical and security issues—such as data bias, algorithmic unfairness, limited model transparency, and explainability gaps—is an essential part of AI literacy. Moreover, it constitutes a comprehensive socio-technical critical awareness that allows for the analysis of broader societal impacts, including systemic discrimination and hate. From identifying exclusionary patterns in model outputs to critically evaluating results that violate the principles of algorithmic neutrality, AI literacy plays a vital role in developing a critical technological perspective grounded in values such as digital justice, explainability, and representational fairness. In addressing the digital manifestations of structural biases such as Islamophobia, AI literacy equips individuals with both technical expertise and ethical reasoning. It enables them to critically engage with algorithmic systems, resist discriminatory outcomes in informed ways, and actively contribute to the design of more just, transparent, and inclusive digital environments.