FAccT Finding: AI Takeaways from ACM FAccT 2025
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 […]
Trustible Becomes Official Implementation Partner for the Databricks AI Governance Framework (DAGF)
Despite the explosive growth of AI, most enterprises remain unprepared to manage the very real risks that come with its adoption. While the opportunities are vast—from smarter products to more efficient operations—the path to realizing AI’s full potential is fraught with challenges around performance, cybersecurity, privacy, ethics, and legal compliance. Without a strong AI governance […]
Trustible’s Perspective: The AI Moratorium would have been bad for AI adoption
In the early hours of July 1, 2025, the Senate overwhelmingly voted to strip the proposed federal moratorium on state and local AI laws from the Republican’s reconciliation bill. The moratorium went through several re-writes in an attempt to salvage it, though ultimately 99 Senators supported removing it from the final legislative package. While the political […]
Trustible Releases Updates and Improvements to Model Transparency Ratings
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. […]
AI Governance Triggers: When to Act and Why It Matters
The rapid evolution of artificial intelligence—with continuous advancements in models, policies, and regulations—presents a growing challenge for AI governance teams. Organizations often struggle to determine when governance intervention is necessary in order to balance risk oversight without imposing excessive compliance burdens. This eBook introduces the concept of “AI Governance Triggers” to provide clarity on the specific AI model events that should prompt governance activities.
Understanding the Data in AI
Data governance is a key component of responsible AI governance, and it features prominently in every emerging AI regulations and standards. However, “data” is not a monolithic concept within AI systems. From the massive datasets collected for training large language models (LLMs), to user feedback loops that refine and improve outputs, multiple “data streams” flow through any modern AI application.
Navigating AI Vendor Risk: 10 Questions for your Vendor Due Diligence Process
AI is everywhere in the vendor ecosystem. The race to embed AI into products has also embedded unknown risks into your supply chain. Knowing what AI your suppliers use is difficult enough. Knowing whether their due diligence actually addresses the risks that AI introduces is another challenge entirely. Customers and regulators are increasingly probing how […]
What is AI Monitoring?
When many technical personas hear the term monitoring, they often think of internal monitoring of the AI system.
Understanding AI Stakeholders with Trustible’s AI Stakeholder Taxonomy
Trustible developed an AI Stakeholder Taxonomy that can help organizations easily identify stakeholders as part of the impact assessment process for their high-risk use cases