AI governance, regulation, strategy, and practice developments from monitored sources.
A 2023 systematic review of 175 articles identifies nine categories of AI social impact, led by bias and discrimination.
MIT AI Risk Repository spotlights a 2016 literature review categorising AI ethical risks along three axes.
NIST and NHTSA researchers met to share approaches to automated vehicle testing and assessment methodologies.
NIST held its second stakeholder workshop on Digital Twins standards and infrastructure in January 2025.
MIT AI Risk Repository summarises a four-class framework covering misuse, accident, structural, and agential AI risks.
MIT AI Risk Repository summarises a UN-focused ethical AI framework identifying 13 AI risk categories.
A scoping review identifies 378 normative issues across 19 topic areas in generative AI ethics literature.
A 2023 academic framework clusters AI ethics and responsibility issues into six groups via systematic literature review.
A module-oriented LLM risk taxonomy covering 12 risks and 44 sub-categories across input, model, toolchain, and output layers.
TASRA classifies AI risks into six types based on accountability: who acts, whether unified, and whether deliberate.
AI Now Institute draws on FDA pharmaceutical regulation as a model for ex ante AI regulatory design.
AI Now Institute argues military AI systems like Lavender and Gospel lack safety assurance, oversight, and accountability.