Evaluating Trustworthiness in Operational Contexts

Chain-of-Trust: A Progressive Trust Evaluation Framework Enabled by Generative AI 👉 Why Traditional Trust Evaluation Falls Short Modern collaborative systems—from smart factories to distributed AI—rely on diverse devices working together. But how do we ensure these collaborators are trustworthy when: - Device capabilities update asynchronously - Network delays create incomplete data snapshots - Task requirements vary dramatically Traditional "all-at-once" trust assessments struggle with these dynamics, often leading to over-resourcing or security gaps. 👉 What Makes Chain-of-Trust Different Researchers from Western University, University of Glasgow, and University of Waterloo propose a staged evaluation framework: 1. Task decomposition: Break complex tasks into sequential requirements (e.g., "3D mapping" needs service availability → secure transmission → sufficient compute → reliable delivery) 2. Progressive filtering: Evaluate collaborators stage-by-stage using only relevant attributes 3. Generative AI integration: Use LLMs' contextual reasoning to:   - Interpret evolving task requirements   - Analyze partial attribute data   - Adapt evaluations dynamically through few-shot learning 👉 How It Works in Practice For a 3D mapping task: 1. Stage 1: Filter devices offering 3D mapping services 2. Stage 2: Verify communication security/bandwidth 3. Stage 3: Assess computing power/isolation 4. Stage 4: Confirm result delivery reliability At each stage, GPT-4 analyzes only the needed attributes, progressively narrowing trusted candidates. Key Results: - 92% accuracy vs. 64% in single-stage evaluations (GPT-4) - 40% resource reduction vs. full-attribute collection - No model retraining required for new tasks Implications: This approach addresses three critical gaps: 1. Handling asynchronous device updates 2. Preventing resource waste on irrelevant attributes 3. Maintaining context-aware evaluations Paper Authors: Botao Zhu, Xianbin Wang (Western University) Lei Zhang (University of Glasgow) Xuemin (Sherman) Shen (University of Waterloo) For those working on distributed systems or AI collaboration frameworks, this paper offers a practical blueprint for trustworthy resource allocation in dynamic environments.

✈️ 🇪🇺 « Trustworthy AI in Defence »: The European Way 🗞️The European Defence Agency’s White Paper is out! At a time when global powers are racing to develop & deploy AI-enabled defence capabilities,the European way =tech innovation + ethical responsibility, operational effectiveness + legal compliance, strategic autonomy + respect for human dignity & democratic values. 🔹AI in defence as legally compliant, ethically sound, technically robust, societally acceptable. 1 🤝🏻Principles of Trustworthiness 🔹foundational principles for trustworthy AI in defence: accountability, reliability, transparency, explainability, fairness, privacy, human oversight. Not optional but integral to the legitimacy of AI systems used by European armed forces. 2. Ethical and Legal Compliance 🔹 Europe’s commitment is to effective military capabilities but also to a rules-based international order. The EU explicitly rejects the idea that technological advancement justifies the erosion of ethical norms. 🔹 importance of ethical review mechanisms, institutional safeguards, alignment with #EU legal frameworks=a legal-ethical backbone ensuring trustworthiness is a practical requirement embedded into every phase of AI development/deployment. 3. Risk Assessment & Mitigation 🔹 EU’s precautionary principle=>rigorous & ongoing risk assessments of AI systems, incl. risks related to technical failures, misuse, bias, and unintended escalation in operational contexts. To anticipate harm before it materializes and equip systems with built-in safeguards 🔹Risk mitigation not only a technical task but an ethical &strategic imperative in high-stakes domains (targeting, threat detection, autonomous mobility). 4. 👁️Human Oversight & Control 🔹The EU rejects fully autonomous weapon systems operating without human intervention in critical functions like the use of force. The Paper calls for clear human-in-the-loop models, where operators retain oversight, intervention capability, and accountability. = safeguards democratic accountability & operational reliability, ensuring no algorithm makes life-and-death decisions. 5. Transparency and Explainability 🔹transparent #AI systems, not black-box models : decision-making processes understandable by users & traceable by designers. Key for after-action reviews, audits, & compliance. Strong stance on explainability 6. European Cooperation &Standardization 🔹Enhanced cooperation and harmonization in defence AI : shared definitions, frameworks to ensure interoperability, avoid duplication, promote a common culture of responsibility. 🔹 joint work on certification processes, training, testing environments 7. Continuous Monitoring and Evaluation 🔹ongoing monitoring, validation, recalibration of AI tools throughout their deployment. «trustworthiness must be maintained, not assumed » =The European way: lead not by imitating others’ race toward automation at any cost, but by demonstrating security, innovation, and values can go hand in hand

Why Trust in AI Will Make or Break Insurance Innovation. In Europe, we’re all watching the AI Act unfold... That's why I sat down with Lutz Goldmann, our VP Data Science, to shed some light on the implementation side. ❓ 1. What makes an AI system trustworthy — and why does that matter now? Because trust is still one of the biggest blockers to adoption. And rightly so. AI systems that aren’t properly developed or tested can cause real harm. That’s why we need to move beyond pure performance metrics and start addressing the fundamentals: reliability, fairness, autonomy, control. We’re in the middle of a global policy wave. In Europe, the AI Act is setting the legislative foundation — but in practice, it’s still more of a framework than a standard. There’s little operational guidance yet. Which means the burden is on us, as builders, to define what good looks like. ❓2. How is your system classified under the AI Act & what does compliance look like in practice? At omni:us, our claims automation system — which interacts directly with humans to settle property and motor claims — was recently classified as “limited risk” by an independent auditor. We meet all transparency obligations and clearly position the product as AI-based. Our teams, from data scientists to automation consultants, are trained in AI literacy. And even though our solution doesn’t fall under the high-risk category, we’ve already adopted several of the high-risk requirements. ❓ 3. What frameworks or guidelines do you follow to ensure trustworthiness today? The AI Act is not yet practical enough to be used as a standard in day-to-day development. That’s why we actively work with more established guidance — such as Mission KI, Fraunhofer’s AI Catalog, AIC4, and Oxford capAI. These provide clearer operational criteria for areas like transparency, security, and control — all of which we consider non-negotiable. ❓4. Q: And what about bias: How do you ensure fairness in claims decisions? Bias in training data can skew outcomes — especially in insurance. That’s why we reduce bias early on by relying primarily on non-personal data. The goal is to avoid unfair treatment across customer groups, regardless of background. Of course, we also recognize: humans aren’t free of bias either. That makes it even more important to define fairness clearly and build it into the system from the start.