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40 P E E R T O P E E R : I L T A ' S Q U A R T E R L Y M A G A Z I N E | W I N T E R 2 0 2 4 A strong governance framework also includes rules for version control and mechanisms for auditing AI decisions to maintain transparency and accountability across legal practices. Access control and role management are critical. Defining who has access to AI tools and training data helps prevent unauthorized use and ensures proper oversight. Human-in-the-loop oversight should ensure that human experts regularly review AI outputs, particularly for critical legal tasks, helping mitigate the risks of autonomous AI decision-making. An effective policy must also include an incident response plan detailing procedures for managing AI-related incidents like data breaches or algorithmic errors. This plan should outline how incidents are reported, resolved, and documented to ensure accountability and continuous improvement. Compliance mechanisms are needed to keep up with evolving AI regulations, with a designated team or governance officer responsible for tracking regulatory changes and integrating necessary updates. Your team should conduct regular ethical impact assessments to evaluate the broader implications of AI systems, focusing on client rights, potential social impacts, and the alignment with the ethical obligations of the legal profession. Data minimization and retention protocols are also essential, as they set rules for collecting only the necessary data and establishing retention timelines to minimize privacy risks and comply with data protection laws. ELEMENTS OF AN EFFECTIVE AI POLICY An effective AI governance policy for law firms addresses key operational, ethical, and compliance issues associated with AI usage. Typically, such policies define what data is used for AI training, which often means setting clear boundaries on client information, ensuring that any data used is either anonymized or explicitly approved for AI purposes through special agreements with clients. Policies also cover procedures for addressing bias or unexpected AI hallucinations—instances where the system generates inaccurate or misleading content—outlining a formal process for assessment, mitigation, and continuous monitoring. Additionally, the policy should establish a method for purging any erroneous or inappropriate content from the AI's knowledge base, ensuring the system constantly evolves to meet firm standards. MORE AI RESOURCES ONLINE Browse the Spring Issue for more AI resources. ó

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