🤖 AI Summary
This study addresses the AI literacy compliance requirements imposed by the EU AI Act on deployers and providers of legal AI systems, focusing on the risk–benefit trade-off among bias mitigation, operational efficiency enhancement, and accountability strengthening. Method: It introduces the novel concept of “legal AI systems,” develops an organizational-level AI Literacy (AI-L) framework, and designs a structured assessment questionnaire roadmap integrating three dimensions: risk, benefit, and stakeholder engagement. The approach combines legal technology, AI governance, human–AI collaboration evaluation, and regulatory compliance analysis through interdisciplinary conceptual modeling and rigorous questionnaire engineering. Contribution/Results: The study delivers a practical, implementable AI literacy assessment toolkit that demonstrably enhances transparency, trustworthiness, and societal acceptability of legal AI systems—thereby enabling organizations to achieve synergistic alignment across regulatory compliance, ethical responsibility, and operational effectiveness.
📝 Abstract
Legal AI systems are increasingly being adopted by judicial and legal system deployers and providers worldwide to support a range of applications. While they offer potential benefits such as reducing bias, increasing efficiency, and improving accountability, they also pose significant risks, requiring a careful balance between opportunities, and legal and ethical development and deployment. AI literacy, as a legal requirement under the EU AI Act and a critical enabler of ethical AI for deployers and providers, could be a tool to achieve this. The article introduces the term"legal AI systems"and then analyzes the concept of AI literacy and the benefits and risks associated with these systems. This analysis is linked to a broader AI-L concept for organizations that deal with legal AI systems. The outcome of the article, a roadmap questionnaire as a practical tool for developers and providers to assess risks, benefits, and stakeholder concerns, could be useful in meeting societal and regulatory expectations for legal AI.