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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 57 • Internal AI Policies: Legal technologists should lead the effort to establish clear internal policies and procedures for AI development, deployment, and monitoring. These policies should cover data governance, bias mitigation, transparency, and accountability. • Data governance: Establish clear guidelines for data collection, storage, and use, ensuring compliance with privacy laws and ethical considerations. Include strategies for ensuring data quality and representativeness, such as data balancing (ensuring datasets accurately reflect the real-world population), data augmentation (creating new data from existing datasets), and data cleaning (identifying and removing errors). • Bias mitigation: Implement procedures for identifying and mitigating bias throughout the AI lifecycle, from data selection to algorithm • External Stakeholder Collaboration: Legal technologists should facilitate engagement with external stakeholders (ethicists, academics, and community representatives) through consultations, workshops, and partnerships. Interdisciplinary collaboration can provide valuable perspectives and assist in addressing broader societal concerns. IMPLEMENTING TECHNICAL SOLUTIONS FOR MITIGATING BIAS Technical solutions are essential for actively identifying and mitigating bias. Legal technologists occupy positions to implement and oversee these solutions. • Explainable AI (XAI): Legal technologists should be champions of using XAI solutions. These provide insights into how AI models make decisions, revealing influencing factors and promoting transparency. For example, design and model evaluation. • Transparency: Promote transparency by documenting AI development processes, data sources, and decision- making logic. • Accountability: Define clear lines of responsibility for AI outcomes and establish mechanisms for addressing unintended consequences. • AI Ethics Committees: Legal technologists should advocate for and participate in multidisciplinary AI ethics committees or review boards. These committees, composed of legal professionals, ethicists, and technologists, provide crucial oversight. Their responsibilities include reviewing AI projects, developing and updating ethical guidelines, providing recommendations to mitigate risks, and monitoring AI systems for compliance. in eDiscovery, XAI can help legal teams understand why an AI model flagged certain documents, allowing them to verify the accuracy and fairness. • Fairness-Aware Machine Learning: Implement fairness-aware machine learning techniques incorporating fairness constraints into algorithm design. The first step could be understanding the different fairness metrics available and choosing the most relevant ones to the specific legal context. These techniques can prevent AI systems from unfairly favoring or disadvantaging certain groups. In legal research, they can help ensure AI systems don't prioritize case law reflecting historical biases. • Bias Detection and Mitigation Tools: Utilize specialized tools to identify and address bias throughout the AI lifecycle. Tools like IBM's AI Fairness 360 toolkit and Microsoft's Fairlearn provide metrics and algorithms for bias detection and mitigation. As a legal technologist, you should become familiar with these tools and integrate them into your workflow. • Human-in-the-Loop (HITL) Systems: Advocate for and implement HITL systems, incorporating ISO, involves developing and using AI in a safe, trustworthy, legal and regulatory frameworks while also protecting ensuring data security.

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