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We're thrilled to share our latest 2-minute video that articulates the vision for how Trustible supports customers operationalizing their AI governance strategy. Much like seatbelts and brakes enabled cars to go faster, so can AI governance accelerate responsible AI development & deployment. The Trustible Responsible AI Governance platform helps customers: 🤝 Demonstrate trustworthy practices and governance controls to your customers or regulators 🛡️ Manage and measure the benefits and risks of your AI systems ⚖️ Ensure compliance with AI regulations and international standards 💡Leverage best-in-class recommendations, insights, and best practices on AI governance to make sure you’re managing both the speed of change of the technology and the regulatory environment Whether you're looking for an out-of-the-box solution or a customizable platform to meet your unique needs, Trustible is your partner in navigating the complexities of AI with confidence. YouTube link 👉 https://lnkd.in/dd4Q77g8 #AI #ResponsibleAI #AIGovernance

Trustible is proud to contribute to the White House Office of Science and Technology Policy's Request for Information (RFI) on shaping the U.S. AI Action Plan. As a leader in AI governance, Trustible’s focus has been and will continue to be in fostering trust in AI systems. We believe the single most important thing that will positively contribute to responsible AI adoption is ensuring AI can be trusted. In our comments on the RFI, we highlight the importance of scalable, industry-driven AI standards that foster trust and innovation.  ✅ 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬 𝐚𝐫𝐞 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐟𝐨𝐫 𝐭𝐫𝐮𝐬𝐭 𝐚𝐧𝐝 𝐦𝐚𝐫𝐤𝐞𝐭 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬 – both AI providers and those adopting AI benefit from understanding what “good” means.  ✅ 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐜𝐲𝐛𝐞𝐫𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐞𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐩𝐫𝐚𝐠𝐦𝐚𝐭𝐢𝐜 𝐀𝐈 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬 – cybersecurity is good business practice. AI governance standards are as well.  ✅ 𝐔.𝐒. 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐢𝐧 𝐀𝐈 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬 𝐞𝐧𝐬𝐮𝐫𝐞𝐬 𝐠𝐥𝐨𝐛𝐚𝐥 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞𝐧𝐞𝐬𝐬 𝐚𝐧𝐝 𝐚𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐰𝐢𝐭𝐡 𝐀𝐦𝐞𝐫𝐢𝐜𝐚𝐧 𝐯𝐚𝐥𝐮𝐞𝐬 – if we don’t develop the standards, someone else will. 

AI Governance accelerates AI adoption. Trustible recently wrote for CIOReview highlighting how CIOs (and CTOs and CDOs etc) are leapfrogging the next wave of enterprise transformation with AI at the center. Yet, many organizations find themselves hamstrung by a lack of clarity around risks and the absence of a standardized, systematic approach to evaluating AI use cases, models, and vendors. 𝐓𝐡𝐞𝐬𝐞 𝐠𝐚𝐩𝐬 𝐨𝐟𝐭𝐞𝐧 𝐜𝐚𝐮𝐬𝐞 𝐩𝐫𝐨𝐦𝐢𝐬𝐢𝐧𝐠 𝐀𝐈 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐭𝐨 𝐬𝐭𝐚𝐥𝐥 𝐢𝐧 𝐚𝐩𝐩𝐫𝐨𝐯𝐚𝐥 𝐥𝐢𝐦𝐛𝐨—𝐨𝐫, 𝐰𝐨𝐫𝐬𝐞, 𝐭𝐨 𝐛𝐞 𝐝𝐞𝐩𝐥𝐨𝐲𝐞𝐝 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐬𝐮𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐨𝐯𝐞𝐫𝐬𝐢𝐠𝐡𝐭. The result is not just slowed innovation, but also heightened exposure to financial, legal, and reputational consequences. 𝐖𝐡𝐞𝐧 𝐝𝐨𝐧𝐞 𝐫𝐢𝐠𝐡𝐭, 𝐀𝐈 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐬 𝐚 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫 𝐟𝐨𝐫 𝐀𝐈 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐫𝐚𝐭𝐡𝐞𝐫 𝐭𝐡𝐚𝐧 𝐚 𝐫𝐨𝐚𝐝𝐛𝐥𝐨𝐜𝐤. A defined, cross-functional governance process enables CIOs and their peers to confidently deploy AI faster, ensuring that each AI initiative aligns with corporate objectives, meets regulatory requirements, and maintains stakeholder trust. But how does an organization actually operationalize AI Governance? We highlight eight ways that the Trustible Platform supports enterprises to accelerate AI adoption. Read the full link in comments.

Our Director of Policy & Product Counsel, John (Heflin) Hopkins-Gillispie, joined the 𝐃𝐂 𝐃𝐞𝐜𝐨𝐝𝐞𝐝: 𝐀 𝐆𝐞𝐧𝐀𝐈 𝐏𝐨𝐝𝐜𝐚𝐬𝐭 to discuss the key definitions of AI and AI governance, an overview of Trustible, and the evolving space of AI regulations in the US and across the world. Link to the podcast here 👉 https://lnkd.in/eqEAyAxH

Big shout out to our Director of Machine Learning on a quick turn around analyzing Claude 3.7's system card transparency!

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. Many organizations assume that if they rely primarily on third-party AI models, they may be exempt from extensive data governance responsibilities. However, even model deployers often must comply with data-related obligations—especially as AI regulations around transparency, privacy, and intellectual property continue to evolve. In our latest blog post, we explore six key data categories that are relevant to AI systems: ◾ Pre-Training Data ◾ Fine-Tuning & RLHF Data ◾ RAG/Agent-Integrated Datasets ◾ Model Inputs ◾ Model Outputs ◾ User Feedback We outline the top governance concerns and key governance activities for each of these categories. Finally, we conclude with how Trustible’s Responsible AI Governance Platform can support your organization in managing these challenges. Link to post https://lnkd.in/edEKGr_i

In today's Trustible Newsletter: 𝐓𝐡𝐞 “𝐄𝐔 𝐅𝐢𝐫𝐬𝐭” 𝐓𝐞𝐜𝐡 𝐀𝐠𝐞𝐧𝐝𝐚 Will the EU really become a tech leader? After criticism of its AI Act, the EU is now pushing hard with huge investments and a focus on innovation. Will they catch up to the US and China? 𝐄𝐔 𝐀𝐈 𝐀𝐜𝐭 𝐇𝐢𝐭𝐬 𝐅𝐢𝐫𝐬𝐭 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐌𝐢𝐥𝐞𝐬𝐭𝐨𝐧𝐞 The EU AI Act is live, and the first rules are here. What does this mean for businesses right now? With guidelines out and more deadlines coming, what is in effect for businesses today and what's coming down the line? 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐢𝐧 𝐀𝐈 Strong data governance is critical for AI governance and involves managing various data categories like pre-training, fine-tuning, input/output, and user feedback. Even those using third-party AI must meet data obligations, especially for transparency, privacy, and IP. 𝐔𝐒 𝐂𝐨𝐩𝐲𝐫𝐢𝐠𝐡𝐭 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞 𝐨𝐧 𝐀𝐈 The US Copyright Office has new guidance on AI-generated content copyright. Can AI art be copyrighted? The US Copyright Office has rules, but they're complex. What's "human modification" vs. pure AI? This will change creative industries. Will it boost innovation or lead to legal fights? 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐃𝐢𝐬𝐭𝐢𝐥𝐥𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝟐𝟎𝟐𝟓 Knowledge distillation, training smaller models with larger ones, is growing. This method, seen in models like DeepSeek-R1 and Gemini-1.5 Flash, creates smaller, efficient models. Restrictions on using model outputs for training create uncertainty about the technique's widespread use. Read more here https://lnkd.in/eksxkKTz

The first requirements of the EU AI Act are now in effect! Here is what you need the know: ✅ 𝐓𝐇𝐄 𝐖𝐇𝐀𝐓: Articles 1-5, which includes AI Literacy requirements and Prohibited Use Cases (e.g., emotional inference systems and social scoring) ✅ 𝐓𝐇𝐄 𝐖𝐇𝐎: While the EU AI Act applies to EU Member States, companies outside the EU will have to comply with these requirements if they do business in the EU. ✅ 𝐓𝐇𝐄 𝐇𝐎𝐖: Orgs need to implement trainings for employees to have a basic knowledge about AI (e.g., definitions, risks, and laws). Orgs should also create and review AI inventories for systems that may be prohibited in the EU. 𝐇𝐨𝐰 Trustible 𝐇𝐞𝐥𝐩𝐬 🗂 𝐀𝐈 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 – gives organizations complete visibility into where and how AI is being used, enabling them to centralize AI use cases, models, data, and vendor information while streamlining risk assessments and compliance https://lnkd.in/gWQccPRr 🎓 𝐀𝐈 𝐋𝐢𝐭𝐞𝐫𝐚𝐜𝐲 & 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 – upskill your staff through ready-to-implement e-training modules focused on the fundamentals of AI, potential risks, and general compliance obligations https://lnkd.in/gS-ZGwVJ

The Massive Data Institute at Georgetown University McCourt School of Public Policy has announced that Trustible's Co-Founder & CTO, Andrew Gamino-Cheong, has just been welcomed to their inaugural cohort of non-resident affiliates. Andrew, alongside Dr Eleni Antoniadou Dimokidis, will share their expertise to enhance the Institute’s mission to advance ethical and impactful data use in policy and research. Read the announcement here 👉 https://lnkd.in/e9YuiFrj

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