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P E E R T O P E E R M A G A Z I N E · S P R I N G 2 0 2 5 59 that represent diverse perspectives. • Avoid relying solely on historical data that may reflect outdated or biased practices. A practical approach is supplementing historical data with more recent and representative data. • Regularly update datasets to reflect current trends and realities. Set up a schedule for data review and updates. ADVOCATING FOR TRANSPARENCY Legal technologists should be champions of transparency within their organizations. • Push vendors for XAI solutions that clarify how AI outputs are generated. Ask for explanations, not just results. • Vendors must disclose details about their algorithms' training processes and fairness measures. Make transparency a key criterion in vendor selection. • Document internal processes for evaluating and deploying AI tools transparently. Create clear, accessible documentation for all stakeholders. Address vendor transparency issues by requiring transparency during procurement and prioritizing vendors' sharing of fairness documentation. MONITORING AND MAINTAINING SYSTEMS POST-DEPLOYMENT AI systems require ongoing monitoring, and legal technologists should establish and manage this process. • Establish regular intervals for conducting algorithmic audits after deployment. Consider a schedule of quarterly or bi- annual audits, depending on the criticality of the AI system. • Retrain models as needed to incorporate new data or address identified biases. Develop a process to determine when retraining is necessary. • Monitor performance metrics over time to ensure consistent alignment with organizational goals and ethical standards. Establish key metrics and track them regularly. COLLABORATING ACROSS TEAMS Building responsible AI requires collaboration. Legal technologists should act as bridges between different departments. • Work with lawyers to define fairness goals aligned with ethical standards. Facilitate discussions to ensure everyone understands the ethical implications of AI. • Partner with data scientists to identify technical solutions for mitigating bias. Provide the

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