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We recognize AI governance can be overwhelming - we’re here to help. Contact us today to discuss how we can help you solve your challenges and Get AI Governance Done.

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Blog, Corporate News, Insights

Introducing the Trustible AI Governance Insights Center

November 17, 2025 trustible

At Trustible, we believe AI can be a powerful force for good, but it must be governed effectively to align with public benefit. Introducing the Trustible AI Governance Insights Center, a public, open-source library designed to equip enterprises, policymakers, and consumers with essential knowledge and tools to navigate AI’s risks and benefits. Our comprehensive taxonomies cover AI Risks, Mitigations, Benefits, and Model Ratings, providing actionable insights that empower organizations to implement robust governance practices. Join us in transforming the conversation around trusted AI into tangible, measurable outcomes. Explore the Insights Center today!

Blog, Corporate News

Trustible Announces New Model Transparency Ratings to Enhance AI Model Risk Evaluation

April 23, 2024 trustible

Organizational leaders are looking to better understand what AI models may be best fit for a given use case. However, limited public transparency on these systems makes this evaluation difficult. 

In response to the rapid development and deployment of general-purpose AI (GPAI) models, Trustible is proud to introduce its research on Model Transparency Ratings – offering a comprehensive assessment of transparency disclosures of the top 21 Large Language Models (LLMs).

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Inside Trustible’s Methodology for Model Transparency Ratings

April 22, 2024 trustible

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.

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