ADVANTAGES AND RISKS OF ARTIFICIAL INTELLIGENCE AND AUTOMATED DECISION-MAKING IN ADMINISTRATIVE LAW
States are rapidly digitalizing public administration, using digital public services to reduce the administrative burden on businesses and citizens by making public services and interactions more efficient, cheaper, transparent and faster. The digitalization of public administration and the increasing use of artificial intelligence (AI) are profoundly transforming the functioning of modern states and the relationship between public authorities and citizens. This increased transformation raises significant questions regarding the compatibility of algorithmic governance with the rule of law, fundamental rights, and core principles of administrative law.
This paper examines the legal implications of integrating digital technologies and AI-based systems into public administration, with a particular focus on fundamental principles of administrative law. As automated decision-making by public authorities in their dealings with individuals is increasing, traditional legal principles such as transparency, accountability, proportionality and privacy, face new challenges. The analysis considers relevant EU and CoE instruments and standards, with particular attention to the principles of legality, transparency, non-discrimination, accountability, personal data protection and the right to good administration in the context of AI-based systems in public administration and automated administrative decision-making.
Governments differ significantly in their readiness to implement AI in the delivery of public services, reflecting varying levels of digital transformation and preparedness for the use of AI systems. In this respect, countries in the Western Balkan region continue to demonstrate limited institutional capacity and uneven levels of digital maturity, despite ongoing efforts undertaken within the framework of the European integration process.
Keywords: administrative law, public administration, artificial intelligence, automated decision-making.
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