Ethics and Artificial Intelligence: Data Privacy and Algorithmic Transparency in Social Sciences. - Arğın, Emrah (Electronic Turkish Studies)
This article comprehensively examines the ethical, methodological, and societal challenges emerging from the increasing use of artificial intelligence (AI) in the social sciences and aims to develop a human-centered ethical framework. While acknowledging the methodological advantages offered by AI—such as large-scale data analysis, natural language processing, automated coding, and predictive modeling—the study emphasizes that this transformation simultaneously generates serious ethical paradoxes related to data privacy, algorithmic transparency, and accountability. The article focuses particularly on three core tensions: the conflict between data privacy and analytical depth, the dilemma between algorithmic efficiency and ethical transparency, and the balance between social benefit and individual autonomy. Within this context, the study analyzes AI applications in social sciences through four main dimensions: privacy-preserving techniques aimed at reducing re-identification risks, explainability tools that make decision-making processes more transparent, mechanisms designed to mitigate algorithmic bias, and participatory evaluation models that strengthen public trust. The research adopts a sequential mixedmethod approach combining systematic literature review, case studies, technical validation tests, and a Delphibased expert consensus process. The findings reveal that technical solutions alone are insufficient for ethical AI implementation; instead, legal regulations, societal participation, and interdisciplinary oversight mechanisms must operate together. For instance, federated learning significantly reduces privacy risks but increases computational costs, while bias mitigation techniques may reduce certain inequalities yet unintentionally create new disparities for other social groups. The study further argues that algorithmic decision-making cannot be detached from cultural contexts and that ethnographic oversight is essential for ensuring ethical AI practices in social research. In conclusion, the article asserts that the ethical use of AI in social sciences is possible only through transparent, accountable, and participatory models grounded in human rights principles. Within the Turkish context, data protection certifications, independent oversight mechanisms, and locally developed language models are highlighted as key implementation strategies. Ultimately, the study advocates for a shift away from technological determinism toward a human-centered approach and provides an applicable ethical roadmap for academia, policymakers, and technology developers.