Anastassia Kornilova is the Director of Machine Learning at Trustible. Anastassia translates research into actionable insights and uses AI to accelerate compliance with regulations. Her notable projects have involved creating the Trustible Model Ratings and AI Policy Analyzer. Previously, she has worked at Snorkel AI developing large-scale machine learning systems, and at FiscalNote developing NLP […]
Updates include new models from OpenAI, Meta, Google, Anthropic, DeepSeek, and more; additional data evaluations; new regulatory and policy analysis; and more. Generative AI models are transforming industries, driving innovation, and reshaping how organizations operate. Despite their wide-scale adoption, these models introduce risks to organizations given the limited transparency into their data, training, and operations. […]
The speed at which new general purpose AI (GPAI) models are being developed is making it difficult for organizations to select which model to use for a given AI use case. While a model’s performance on task benchmarks, deployment model, and cost are primarily used, other factors, including the data sources, ethical design decisions, and regulatory risks of a model must be accounted for as well. These considerations cannot be inferred from a model’s performance on a benchmark, but are necessary to understand whether using a specific model is appropriate for a given task or legal to use within a jurisdiction.
While AI has been used in enterprise and consumer products for decades, only large tech organizations with sufficient resources were able to implement it at scale. In the past few years, advances in the quality and accessibility of ML systems have led to a rapid proliferation of AI tools in everyday life. The accessibility of these tools means there is a massive need for good AI Governance both by AI providers (e.g. OpenAI), as well as the organizations implementing and deploying AI systems into their own products.
