Liability Issues in the Context of Artificial Intelligence: Legal Challenges and Solutions for AI-Supported Decisions
Résumé
Artificial intelligence (AI), which enhances efficiency, production, and decision-making, has rapidly become a crucial component in sectors such as healthcare, banking, education, and transportation. However, as AI systems increasingly integrate into critical aspects of daily life, significant legal challenges related to liability, transparency, and accountability arise. The issue is that it can be challenging to assign blame for judgments made by AI, particularly when self-learning systems are involved and go beyond initial programming. In addition to algorithmic bias, opaque decision-making procedures, and third-party involvement, there are ambiguities in the assignment of accountability among developers, operators, and users. The purpose of this study is to discuss these legal issues and offer workable answers to guarantee fairness and responsibility in AI-assisted decision-making. In order to streamline compensation by emphasizing causality rather than culpability, key findings recommend the implementation of strict responsibility for high-risk AI applications. Accountability and traceability can be increased by increasing transparency through required paperwork and explainable AI systems. Uncertainty can be decreased by using explicit contractual frameworks to clearly define roles for developers, operators, and users. Furthermore, the creation of specialist liability insurance can promote the appropriate use of AI while providing financial protection for stakeholders. Building public trust and making sure AI advances society without endangering it needs striking a balance between innovation and moral and legal obligations. Cross-border AI applications require international harmonization of legal norms, such as the GDPR and the EU's AI Act, in order to establish a uniform regulatory framework. To ensure justice, fairness, and the well-being of society, these extensive legal reforms are required to close the gap between accountability and technological innovation
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