Bridging the Accountability Gap in AI Decision-Making: An Integrated Analysis of Legal Precedents and Scholarly Perspectives

Abstract

With the rapid popularization of artificial intelligence technology in various fields, AI systems increasingly assume an important role of autonomous decision-making, but its complex, opaque and constantly self-evolving characteristics make the traditional legal framework based on static and single subject responsibility face severe challenges. It is difficult for existing models to effectively trace decision errors and biases caused by multiple factors such as data processing, algorithm design and dynamic update. Based on representative judicial precedents such as State v. Loomis  and Google Spain, and combined with academic discourse, this paper explores in depth the legal and technical difficulties in AI decision-making accountability. By establishing mandatory transparency and detailed documentation mechanisms, building dynamic oversight and adaptive legal standards, promoting collaborative accountability among developers, data providers, and users, and redefining responsibilities for automated systems, a new regulatory framework can be built that ADAPTS to the dynamic evolution of AI technology. Research shows that only with the cooperation of law, technology and regulation can we effectively bridge the accountability gap, protect the public rights and interests, and provide solid legal support for the healthy development of artificial intelligence technology.