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As machine learning (ML) becomes more central to many decision-making processes, including in high-stakes contexts such as criminal justice and banking, the companies deploying such automated decision-making systems face increased pressure for transparency on how these decisions are made. Annotation and Benchmarking on Understanding and Transparency of Machine learning Lifecycles (ABOUT ML) is a multi-year, iterative multi-stakeholder project of the Partnership on AI (PAI) that will work towards establishing evidence-based ML transparency best practices throughout the ML system lifecycle from design to deployment, starting with synthesizing existing published research and practice into recommendations on documentation practice.
The impact of artificial intelligence on the economy, labor, and society has long been a topic of debate — particularly in the last decade — amongst policymakers, business leaders, and the broader public. To help elucidate these various areas of uncertainty, the Partnership on AI’s Working Group on “AI, Labor, and the Economy” conducted a series of case studies across three geographies and industries, using interviews with management as an entry point to investigate the productivity impacts and labor implications of AI implementation.
Gathering the views of the Partnership on AI's multidisciplinary artificial intelligence and machine learning research and ethics community, this report documents the serious shortcomings of algorithmic risk assessment tools in the U.S. criminal justice system. Though advocates of such tools suggest that these data-driven AI predictions will produce a reduction in unnecessary detention and provide fairer and less punitive decisions than existing processes, an overwhelming majority of the Partnership’s consulted experts agree that current risk assessment tools are not ready for decisions to incarcerate human beings. This report calls for ten largely unfulfilled requirements that jurisdictions should weigh heavily prior to the use of these tools.