Proactive Trustworthiness Assessment Tools

𝗟𝗟𝗠 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀? Google Cloud 𝘁𝗮𝗰𝗸𝗹𝗲𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗵𝗲𝗮𝗱 𝗼𝗻! I'm happy to share Gemini Hallcheck, a new open-source toolkit for evaluating model trustworthiness! A groundbreaking paper by Kalai et al. at OpenAI and Georgia Tech explains why hallucinations are a statistically inevitable result of pre-training. Our work provides the first open-source implementation of their core proposal to manage this reality. Existing benchmarks measure accuracy, but often reward models for confident guessing. This hides the real-world risk of hallucinations and makes it difficult to choose the truly most reliable model for high-stakes tasks. Building on the theoretical framework from the paper, we've created a practical evaluation suite that moves beyond simple accuracy to measure Behavioural Calibration. Here are the highlights: 🎯 Confidence-Targeted Prompting: A new evaluation method that tests if a model can follow risk/reward rules. ⚖️ Abstention-Aware Scoring: Implements the paper's novel penalty scheme to reward honest "I don't know" answers instead of penalizing them. 📈 Trustworthiness Curves: Generates a trade-off curve between a model's answer coverage and its conditional accuracy, revealing its true reliability. Our initial tests show that some models that look best on traditional accuracy benchmarks are not the most behaviourally calibrated. Choosing the right model for your enterprise use case just got a lot clearer 🤗 We're open-sourcing our work to help the community build and select more trustworthy AI. Feel free to explore the GitHub repo and run the evaluation on your own models, link to the code in the comments below!

🛠️ Not every AI risk assessment template is built equally. Some are too broad. Some are too theoretical. But every once in a while, you find one that’s actually usable straight out of the box. I just finished reviewing the AI Risk Assessment Template from TrustArc, and honestly, it’s one of the best starting points I’ve seen for organisations that want to move from principles to practice. 💡 Why it matters? Governance isn’t about creating walls of policy text no one uses. It’s about making structured, repeatable decisions—and this template gives you exactly that. 📋 What makes it stand out: – Covers the full AI lifecycle: from design, human oversight, and robustness to fairness, privacy, and transparency – Maps directly to leading frameworks like NIST AI RMF and EU AI Act obligations – Includes clear questions, tips, control effectiveness ratings, and risk scoring guidance – Provides actionable checkpoints—ideal for self-assessment, procurement reviews, internal audits, and early DPIAs It’s not rigid. It’s modular. You can scale it up for critical systems—or use a lighter version for internal tools. It’s the kind of high-quality template that’s “good enough” for most organizations—and smart enough to adapt when complexity grows. 🙏 A huge thank you to the team at TrustArc, and to everyone involved in creating and maintaining this template. You didn’t just write a document—you built a resource that will meaningfully improve AI governance practices around the world. You made the complicated simple, and the abstract actionable—and the field is stronger for it. If you’re setting up or improving your AI risk governance this year, this is exactly the kind of resource you want at your fingertips. #AIrisk #TrustworthyAI #AICompliance #RiskManagement #AIGovernance === Did you like this post? Connect or Follow 🎯 Jakub Szarmach, AIGP, CIPM Want to see all my posts? Ring that 🔔.