This effort includes: ● Optimizing JAG Data Management: Employing AI/ML for automated document classification, metadata extraction, and intelligent eDiscovery to streamline evidence review processes. ● Enhancing Decision-Making: Utilizing predictive analytics for case forecasting, resource allocation, and bias detection to ensure equitable and efficient legal rulings. ● Improving Military Justice Outcomes: Implementing generative AI for rapid advisory responses, anomaly detection in compliance pipelines, and reinforcement learning for workflow improvements. The aim is to develop capabilities that bolster data integrity, mitigate risks in critical areas such as military justice proceedings, and support the AFDW JA's mission to provide timely, accurate legal counsel. These efforts will align with Department of War ethical AI principles, ensuring secure data handling on IL5-compliant platforms, and adhere to the guidelines set forth in Air Force Doctrine Note 25-1 for responsible AI integration. The legal and judicial data management processes within the Air Force District of Washington Judge Advocate (AFDW JA) are currently labor-intensive and prone to delays due to the reliance on manual reviews and updates. These challenges hinder the timely delivery of accurate and independent counsel required for contingency responses, ceremonial honors, and global operational support. This Phase I Small Business Innovation Research (SBIR) topic aims to address these issues by introducing innovative AI/ML solutions that automate and expedite AFDW JA's workflows, thereby enhancing mission effectiveness and efficiency. Key areas of focus include: ● Military Justice under the UCMJ: Automating the intake and processing of judicial and legal case data through advanced AI techniques such as natural language processing for automatic document classification, metadata extraction, and anomaly detection. ● Contracting Oversight: Leveraging predictive modeling techniques to improve data quality, accuracy, and mitigate bias in decision-making processes. ● Interagency Civil Support: Utilizing generative AI for rapid query responses and reinforcement learning to optimize workflow efficiencies, along with advanced data visualization tools to provide actionable insights and facilitate stakeholder briefings. The desired outcome of this project is to transform the unstructured data overload into actionable intelligence, thereby reducing processing delays by 50-70% and significantly enhancing overall legal advisory excellence. The proposed AI/ML solutions must comply with Air Force standards and Department of War ethical AI principles, ensuring secure and explainable outputs that are compatible with existing Air Force data platforms. This will support seamless integration and scalability across the National Capital Region and worldwide missions. The initial Technology Readiness Level (TRL) at the project start is TRL 3, with the aim to achieve TRL 6 by the end of Phase II. The efforts required to achieve these objectives include: ● Phase I: Conducting feasibility studies, developing preliminary AI/ML models, and validating the core functionalities through prototype testing. Minimum deliverables include a detailed feasibility report, initial prototype/wireframe demonstration, and a comprehensive plan for Phase II development. ● Phase II: Refining and expanding the AI/ML models based on Phase I results, conducting extensive testing in operational environments, and finalizing the integration with existing AFDW JA systems. Minimum deliverables include a fully functional AI/ML solution, detailed testing and evaluation reports, and documentation for system integration and user training. By implementing these advanced AI/ML solutions, the AFDW JA will enhance its capability to provide timely and accurate legal counsel, aligning with the strategic objectives of the Air Force District of Washington and ensuring operational readiness and effectiveness.
No documents are available for this record yet.