From Council on Criminal Justice:
Introduction
The rapid advancement of artificial intelligence (AI) technologies has implications for every sector of society, including the criminal justice system. As AI tools for investigation, adjudication, prioritization, analysis, and decision-making proliferate and evolve, understanding their potential benefits and risks becomes increasingly important.
In June 2024, the Council on Criminal Justice (CCJ) convened a group of experts and stakeholders to discuss the implications of AI for the U.S. criminal justice system. The meeting brought together a diverse group of three dozen leading stakeholders from across ideologies, disciplines, and sectors of the system—policymakers, practitioners, researchers, technologists, and advocates—for two days of discussion and the examination of three use cases. The event was hosted by the Stanford Criminal Justice Center at the Stanford University School of Law.
Seven Areas of Inquiry
CCJ framed the convening around seven key areas of inquiry:
Effectiveness and Efficiency: How can AI enhance justice system operations while protecting rights? AI offers potential to improve efficiency and accuracy, but raises concerns about over-reliance and bias.
User Training: What knowledge, skills, and other preparation do practitioners need to use AI effectively and ethically? Proper training is crucial to maximize benefits and avoid misuse.
Benchmarks and Performance Standards: How should the performance of AI tools be evaluated? Establishing reliable standards is a critical and complex task given the difficulty of measuring AI performance.
Data Quality and Algorithmic Reliability: How can we ensure data integrity and algorithmic reliability in AI systems, especially given concerns around the accuracy, implicit bias, and comprehensiveness of criminal justice data?
Privacy, Bias, and Fairness: How can AI be used to reduce rather than amplify discrimination? AI has potential to identify and mitigate biases, but also risks reinforcing or creating new discriminatory practices.
Transparency, Explainability, and Accountability: How can AI systems be made more transparent and accountable? Balancing the need for explainable AI with proprietary interests and technical complexity is a key challenge.
Governance and Enforcement: What regulatory frameworks and oversight mechanisms are needed? Establishing robust governance structures is essential to ensure ethical use of AI in criminal justice.
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