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<p>This article examines the legal and ethical responsibilities of data controllers when using artificial intelligence (AI) to process personal data, with a focus on safeguarding vulnerable individuals. It develops a structured, risk-based responsibility framework grounded in European data protection law, particularly the General Data Protection Regulation (GDPR), the Law Enforcement Directive (LED), and the emerging obligations of the European Union’s (EU) AI Act. The framework seeks to reconcile technological innovation with the duty to uphold fundamental rights. Using a doctrinal legal research method, the study analyses legislation, case law, and regulatory guidance to identify three core areas of responsibility: risk assessment and mitigation, proportionality in protective measures, and demonstrable compliance. Procedural tools such as Data Protection Impact Assessments (DPIAs), Fundamental Rights Impact Assessments (FRIAs), data protection by design and default, and human oversight are integrated into this model. The framework’s practical relevance is illustrated through supervisory authority decisions and public debates in education, employment, credit scoring, and smart-city surveillance. The findings show that AI technologies—especially profiling, biometric recognition, and automated decision-making—create heightened risks for rights, including privacy, dignity, autonomy, and non-discrimination. Effective governance requires continuous risk evaluation, safeguards proportionate to the context, and, in highand persistent residual risk cases, consultation with supervisory authorities. The article concludes that protecting vulnerable individuals in AI contexts requires a proactive and adaptive governance model. While challenges and jurisdictional limits remain, the proposed framework offers a legally sound and ethically grounded basis for responsible AI deployment that prioritizes those most at risk.</p>

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Controller responsibilities in AI-driven processing of vulnerable data subjects: a legal framework for risk mitigation, proportionality, and compliance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月8日

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  • Kamrul Faisal

摘要

This article examines the legal and ethical responsibilities of data controllers when using artificial intelligence (AI) to process personal data, with a focus on safeguarding vulnerable individuals. It develops a structured, risk-based responsibility framework grounded in European data protection law, particularly the General Data Protection Regulation (GDPR), the Law Enforcement Directive (LED), and the emerging obligations of the European Union’s (EU) AI Act. The framework seeks to reconcile technological innovation with the duty to uphold fundamental rights. Using a doctrinal legal research method, the study analyses legislation, case law, and regulatory guidance to identify three core areas of responsibility: risk assessment and mitigation, proportionality in protective measures, and demonstrable compliance. Procedural tools such as Data Protection Impact Assessments (DPIAs), Fundamental Rights Impact Assessments (FRIAs), data protection by design and default, and human oversight are integrated into this model. The framework’s practical relevance is illustrated through supervisory authority decisions and public debates in education, employment, credit scoring, and smart-city surveillance. The findings show that AI technologies—especially profiling, biometric recognition, and automated decision-making—create heightened risks for rights, including privacy, dignity, autonomy, and non-discrimination. Effective governance requires continuous risk evaluation, safeguards proportionate to the context, and, in highand persistent residual risk cases, consultation with supervisory authorities. The article concludes that protecting vulnerable individuals in AI contexts requires a proactive and adaptive governance model. While challenges and jurisdictional limits remain, the proposed framework offers a legally sound and ethically grounded basis for responsible AI deployment that prioritizes those most at risk.