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 […]
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.
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.
When many technical personas hear the term monitoring, they often think of internal monitoring of the AI system.
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
On July 11, 2024, the New York Department of Financial Services (NY DFS) released its final circular letter on the use of external consumer data and information sources (ECDIS), AI systems, and other predictive models in underwriting and pricing insurance policies and annuity contracts. A circular letter is not a regulation per se, but rather a formalized interpretation of existing laws and regulations by the NY DFS. The finalized guidance comes after the NY DFS sought input on its proposed circular letter, which was published in January 2024.
In our final blog post of this AI Policy series (see Comprehensive AI Policy and AI Use Policy guidance posts here), we want to explore what organizations should make available to the public about their use of AI. According to recent research by Pew, 52 percent of Americans feel more concerned than excited by AI. This data demonstrates that, while organizations may understand or realize the value of AI, their users and customers may harbor some skepticism. Policymakers and large AI companies have sought to address public concerns, albeit in their own ways.
In this series’ first blog post, we broke down AI policies into 3 categories: 1) a comprehensive organizational AI policy that includes organizational principles, roles and processes, 2) an AI use policy that outlines what kinds of tools and use cases are allowed, as well as what precautions employees must take when using them, and 3) a public facing AI policy that outlines core ethical principles the organization adopts, as well as their stance on key AI policy stances. In this second blog post on AI policies, we want to explore critical decisions and factors that organizations should consider as they draft their AI use policy.
As organizations increase their adoption of AI, governance leaders are looking to put in place policies that ensure their AI deployment aligns with their organization’s principles, complies with regulatory standards, and mitigates potential risks. But where to start in developing your policies can oftentimes be overwhelming.
AI Governance is hard as it involves collaboration across multiple teams and an understanding of a highly complex technology and its supply chains. It’s about to get even harder. The complexity of AI governance is growing along 2 different dimensions at the same time – both of them are poised to accelerate in the coming […]
