Trust and Value Exchange in Autonomous Systems

Metaphors can be powerful and useful. "What Human-Horse Interactions may Teach us About Effective Human-AI Interactions" offers a wealth of insights for how we can - and probably should - relate to and create value with AI. This marvellous new paper by Mohammad Hossein Jarrahi and Stan Ahalt explores what turns out to be a valuable and highly relevant analogy. 💡 AI-Horse Partnership as a Metaphor for Collaboration. Using human-horse interactions as a metaphor, the paper emphasizes AI as a complement rather than a replacement for human capabilities. Just as horses enhance human tasks with their unique strengths, AI can augment human decision-making, enabling a symbiotic relationship that values trust, communication, and adaptability over imitation of human behavior. 🔄 Trust and Transparency as the Foundation. Trust in human-horse and human-AI relationships is built on predictability, mutual understanding, and transparent communication. For AI, this requires clear design and consistent performance, particularly in high-stakes scenarios. Focusing on domain-specific models can enhance reliability, mirroring the trust dynamics seen in successful equine partnerships. 🌟 Training and Ethical Integration of AI. Taming and habituating AI parallels horse training, emphasizing gradual integration and ethical management. Adapting AI to real-world complexities through methods like adversarial training and transfer learning can ensure safety and reliability, allowing systems to perform effectively outside controlled environments. 🌀 Continuous Feedback for Adaptability. Mutual adaptability in partnerships relies on feedback loops. Like riders adjusting based on a horse's responses, AI systems must learn from user interactions to refine outputs. Similarly, users must understand AI's capabilities and limitations, fostering a reciprocal learning process. 🤝 Shared Decision-Making for Synergy. Collaborative decision-making between humans and AI echoes the dynamics of horse-rider relationships, where autonomy is balanced with guidance. AI systems should be active participants, offering insights and alternatives while humans maintain oversight to ensure ethical and strategic alignment. ⚖️ Asymmetry in Responsibility Requires Oversight. Humans bear the ultimate responsibility in both horse and AI partnerships, ensuring safety and ethical adherence. Clear guidelines, akin to reins in horse training, maintain this asymmetry while allowing AI to contribute meaningfully within defined boundaries. ⏳ Long-Term Commitment and Mutual Growth. Effective partnerships require sustained interaction, training, and updates. AI systems should evolve alongside human users, learning from ongoing engagement to improve performance and responsiveness, much like the deepening bond in horse-human relationships. Link to paper in comments.

This white paper covers the concept of value alignment, including its definition and practical application and the processes involved in embedding values into artificial intelligence (AI) systems. Human values such as justice, privacy and agency are contrasted with such operational attributes as robustness and transparency, highlighting the importance of balancing ethical implications with technical mechanisms. Exploring the entire life cycle of AI systems, the paper’s analysis emphasizes the need for explicit and auditable processes to translate values into norms and verify their adherence. Active stakeholder participation and continuous monitoring are crucial to maintain alignment with societal values and ethical standards. A comprehensive approach to AI value alignment also includes a detailed examination of frameworks, guidelines, human engagement, organizational change and auditing processes. These enablers help to ensure that AI systems are not only innovative but also ethical and trustworthy, thereby promoting trust and transparency among users and other stakeholders. Finally, value alignment is linked to the concept of AI ethics red lines – the non-negotiable boundaries that AI systems must not cross. By embedding core human values and maintaining rigorous oversight, the value alignment process makes sure AI systems operate within established moral and legal frameworks, safeguarding against unethical behaviour and maintaining societal trust.

Trust in AI isn't a PR problem. It's an engineering one. Public trust in AI is falling fast. In the UK, 87% of people want stronger regulation on AI and a majority believe current safeguards aren't enough. We can't rebuild that trust with ethics statements, glossy videos, or "trust centers" that nobody reads. We need to engineer trust into AI systems from day one. That means: Designing for transparency and explainability (not just performance) Piloting high-benefit, low-risk use cases that prove value (and safety) Embedding value-alignment into system architecture using standards like ISO/IEEE 24748-7000 Engineers can no longer afford to be left out of the trust conversation. They are the trust conversation. Here’s how: 🔧 1. Value-Based Engineering (VBE): Turning Ethics into System Design Most companies talk about AI ethics. Few can prove it. Value-Based Engineering (VBE), guided by ISO/IEEE 24748-7000, helps translate public values into system requirements. It’s a 3-step loop: Elicit values: fairness, accountability, autonomy Translate into constraints: e.g., <5% error rate disparity across groups Implement & track across dev lifecycle This turns “fairness” from aspiration to implementation. The UK’s AI Safety Institute can play a pivotal role in defining and enforcing these engineering benchmarks. 🔍 2. Transparency Isn’t a Buzzword. It’s a Stack Explainability has layers: Global: what the system is designed to do Local: why this output, for this user, right now? Post hoc: full logs and traceability The UK’s proposed AI white paper encourages responsible innovation but it’s time to back guidance with technical implementation standards. The gold standard? If something goes wrong, you can trace it and fix it with evidence. ✅ 3. Trust Is Verifiable, Not Assumed Brundage et al. offer the blueprint: External audits and third-party certifications Red-team exercises simulating adversarial misuse Bug bounty-style trust challenges Compute transparency: what was trained, how, and with what data? UK regulators should incentivise these practices with procurement preferences and public reporting frameworks. This isn’t compliance theater. It’s engineering maturity. 🚦 4. Pilot High-Impact, Low-Risk Deployments Don’t go straight to AI in criminal justice or benefits allocation. Start where you can: Improve NHS triage queues Explainable fraud detection in HMRC Local council AI copilots with human-in-the-loop override Use these early deployments to build evidence and public trust. 📐 5. Build Policy-Ready Engineering Systems Public trust is shaped not just by what we build but how we prove it works. That means: Engineering for auditability Pre-wiring systems for regulatory inspection Documenting assumptions and risk mitigation Let’s equip Ofcom, ICO, and the AI Safety Institute with the tools they need and ensure engineering teams are ready to deliver. The public is asking: Can we trust this? The best answer isn’t a promise. It’s a protocol.

AI without trust is like a supercar without brakes. Powerful but dangerous. Originally posted on Trust3 AI Consider this split: Without Trust Layer: → Black box decisions → Unknown biases → Hidden agendas → Unchecked power With Trust Layer: → Transparent processes → Verified outcomes → Ethical guardrails → Human oversight The difference matters because: - AI touches everything - Decisions affect millions - Stakes keep rising - Trust determines adoption What we need: → Clear audit trails → Explainable outputs → Value alignment → Democratic control Remember: Power without accountability? That's not innovation. That's danger. The future needs both: → AI advancement → Trust infrastructure Which side are you building for?

We’re leaving “assistants in apps” behind and entering the era of autonomous systems that perceive, reason, act, and learn. The research from University of Oxford is clear: UX must shift from guiding users through procedures to systems that take a goal, execute safely, and return a verified result with an audit trail. Agents can plan, coordinate tools/APIs, adapt from feedback, and carry memory forward. Our job moves from arranging screens to specifying goals, guardrails, and governance. ↳What to design next? ➤ Delegation over steps: Users set objectives and constraints; agents handle multi-step execution. ➤ Receipts for autonomy: Preview the plan, explain actions, expose confidence/provenance. ➤ Reversibility: Approve/modify before execution; one-tap rollback after. ➤ Safety as telemetry: Adversarial tests, shadow runs, and thresholds treated like uptime SLOs. ➤ Inspectable memory: Show what was learned; let users review, edit, or forget it. ↳Forward-thinking questions: ➤ Where can users delegate an outcome instead of a procedure? ➤ How do we surface confidence fast enough for oversight? ➤ What’s the rollback path for every action? ➤ How is memory exposed and controlled? Design the contract well, and autonomy becomes usable, trustworthy, and shippable. Trust is currency. 💰 ---- Inspired by: Michael Negnevitsky (author)

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬: 𝗧𝗿𝘂𝘀𝘁 𝗮𝘀 𝘁𝗵𝗲 𝗖𝗼𝗿𝗻𝗲𝗿𝘀𝘁𝗼𝗻𝗲 𝗼𝗳 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 As Industry 5.0 takes shape, trust becomes the defining factor in securing the future of industrial ecosystems. With the convergence of AI, digital twins, IoT, and decentralized networks, organizations must adopt a structured trust architecture to ensure reliability, resilience, and security. 𝗪𝗵𝘆 𝗶𝘀 𝘁𝗿𝘂𝘀𝘁 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗶𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬? With the rise of AI-driven decision-making, digital twins, and decentralized networks, industrial ecosystems need a robust trust architecture to ensure reliability, security, and transparency. 𝗧𝗵𝗲 𝗧𝗿𝘂𝘀𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬 J. Mehnen from the University of Strathclyde defines six progressive trust layers : 𝗦𝗺𝗮𝗿𝘁 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆 – The foundation of Industry 5.0 trust. This layer ensures secure IoT networks, smart sensors, and seamless machine-to-machine communication for industrial automation. 𝗗𝗮𝘁𝗮-𝘁𝗼-𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 – Moving beyond raw data, this layer integrates AI-driven analytics, real-time insights, and multi-dimensional data correlation to enhance decision-making. 𝗖𝘆𝗯𝗲𝗿 𝗟𝗲𝘃𝗲𝗹 – The backbone of digital security, incorporating digital twins, simulation models, and cyber-trust frameworks to improve system predictability and integrity. 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 𝗟𝗲𝘃𝗲𝗹 – AI-powered diagnostics, decision-making, and remote visualization ensure predictive maintenance and self-learning systems that minimize operational disruptions. 𝗦𝗲𝗹𝗳-𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 – AI-driven systems that self-optimize, self-configure, self-repair, and self-organize, reducing dependency on human intervention. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 – The highest level of trust, where decentralized computing, autonomous decision-making, and blockchain-based governance eliminate single points of failure and ensure system-wide resilience. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗔𝗜: 𝗧𝗵𝗲 𝗖𝗼𝗿𝗲 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 To achieve a trusted Industry 5.0 ecosystem, organizations must embrace a structured framework : 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 – Ensuring ethical AI, traceable decision-making, and accountable automation. 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 – Withstanding cyberattacks and operational disruptions. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 – Protecting data, IoT devices, and industrial networks from cyber threats. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 – Ensuring system performance across various conditions. 𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Enabling auditability, transparency, and regulatory compliance in automation. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻 – Implementing policy-driven AI and decentralized oversight mechanisms.  𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 As industries embrace AI, smart factories, and autonomous supply chains, trust becomes the new currency of industrial success. Ref :https://lnkd.in/dz998J_6

🚀 Autonomous AI workflows: trust and adoption dynamics During my recent presentations at Brightlands AI Academy sessions or during company visits, I show my fully autonomous AI workflows that I build to support my own activities. And lots of companies then come back to me: "Roy, I also want this. Can you also help us implement such stuff?". But then when we detail the scope further, one recurring theme stands out: the trust gap in fully autonomous AI workflows. Large enterprises often struggle to go “all-in.” They prefer human-in-the-middle setups — a safeguard where people validate AI decisions before execution. The reasons are clear: risk management, compliance, reputation. In heavily regulated or high-stakes industries, even a minor error can cascade into major consequences. By contrast, start-ups move differently. With less legacy, fewer compliance hurdles, and higher risk tolerance, they are much more willing to deploy 100% autonomous workflows. For them, speed and efficiency often outweigh the potential downside of mistakes — and that agility can become a competitive advantage. The question isn’t whether autonomous workflows will be trusted at scale, but when. The pace of adoption will likely follow the familiar curve: experimentation at the edges (start-ups), gradual acceptance in mid-sized firms, and finally cautious integration into enterprise cores. 👉 Where do you stand on this? Would you trust an AI workflow to run fully autonomously in your organization — or do you still want a human checkpoint in the loop? #autonomous #aiworkflows #n8n #genzai

The Critical Role of Know Your Agent (KYA) in Today's Multi-Agent Ecosystem In the rapidly evolving AI landscape, organizations are moving beyond single-model interactions toward sophisticated multi-agent systems. As these autonomous AI agents increasingly perform critical tasks, establishing trust becomes paramount, and this is where KYA (Know Your Agent) emerges as an essential infrastructure. The Authentication Gap in Today's Protocols While Anthropic's Model Context Protocol (MCP) provides a robust foundation for agent-tool interactions, it still lacks comprehensive Authentication, Authorization, and Accounting (AAA) mechanisms crucial for enterprise adoption. This creates significant security challenges as organizations deploy increasingly autonomous systems. Google's Agent-to-Agent (A2A) protocol currently offers the most relevant intermediate solution in the market. A2A enables agents to communicate directly, exchange information securely, and coordinate actions across diverse environments. Its architecture addresses explicitly the authentication challenges through a structured framework that includes: * Identity verification - for both AI agents and delegating users * Delegation management - controlling what actions agents can perform * Access control - mechanisms for restricting resource access * Comprehensive audit trails - for monitoring agent activities In this multi-agent ecosystem, implementing robust KYA frameworks provides several critical advantages: 1. Enhanced Security - Properly authenticated agents prevent unauthorized access to sensitive systems and data 2. Trust Infrastructure - Creates verification pathways essential for agents acting on behalf of users 3. Reduced Risk - Minimizes vulnerabilities in increasingly complex agent interactions 4. Regulatory Readiness - Positions organizations ahead of inevitable compliance requirements BairesDev experts specialize in architecting intelligent multi-agent AI systems that address these authentication challenges head-on. Our expertise spans: * Custom Agent Development - Building secure, verifiable AI agents with built-in authentication mechanisms * Multi-Agent System Design - Creating architectures where multiple specialized agents collaborate while maintaining security * A2A Implementation - Deploying Google's A2A protocol for secure agent communication * Hybrid Agent Architecture - Combining reactive and deliberative agents for maximum efficiency[5] We help organizations move beyond basic implementations toward sophisticated agent ecosystems with enterprise-grade security controls. By partnering with BairesDev, you can leapfrog common implementation challenges and deploy trusted, authenticated agent networks that drive measurable business value. How is your organization addressing authentication in your AI agent strategy? Share your thoughts below. #KnowYourAgent #AIAuthentication #MultiAgentSystems #A2A #AIGovernance #BairesDev, Nacho De Marco