Requirements for Trusting an Analytics Bot

Why are you ignoring a crucial factor for trust in your AI tool? By overlooking crucial ethical considerations, you risk undermining the very trust that drives adoption and effective use of your AI tools. Ethics in AI innovation ensures that technologies align with human rights, avoid harm, and promote equitable care. Building trust with patients and healthcare practitioners alike. Here are 12 important factors to consider when working towards trust in your tool. Transparency: Clearly communicating how AI systems operate, including data sources and decision-making processes. Accountability: Establish clear lines of responsibility for AI-driven outcomes. Bias Mitigation: Actively identifying and correcting biases in training data and algorithms. Equity & Fairness: Ensure AI tools are accessible and effective across diverse populations. Privacy & Data Security: Safeguard patient data through encryption, access controls, and anonymization. Human Autonomy: Preserve patients’ rights to make informed decisions without AI coercion. Safety & Reliability: Validate AI performance in real-world clinical settings. And test AI tools in diverse environments before deployment. Explainability: Design AI outputs that clinicians can interpret and verify. Informed Consent: Disclose AI’s role in care to patients and obtain explicit permission. Human Oversight: Prevent bias and errors by maintaining clinician authority to override AI recommendations. Regulatory Compliance: Adhere to evolving legal standards for (AI in) healthcare. Continuous Monitoring: Regularly audit AI systems post-deployment for performance drift or new biases. Address evolving risks and sustain long-term safety. What are you doing to increase trust in your AI tools?

☢️Manage Third-Party AI Risks Before They Become Your Problem☢️ AI systems are rarely built in isolation as they rely on pre-trained models, third-party datasets, APIs, and open-source libraries. Each of these dependencies introduces risks: security vulnerabilities, regulatory liabilities, and bias issues that can cascade into business and compliance failures. You must move beyond blind trust in AI vendors and implement practical, enforceable supply chain security controls based on #ISO42001 (#AIMS). ➡️Key Risks in the AI Supply Chain AI supply chains introduce hidden vulnerabilities: 🔸Pre-trained models – Were they trained on biased, copyrighted, or harmful data? 🔸Third-party datasets – Are they legally obtained and free from bias? 🔸API-based AI services – Are they secure, explainable, and auditable? 🔸Open-source dependencies – Are there backdoors or adversarial risks? 💡A flawed vendor AI system could expose organizations to GDPR fines, AI Act nonconformity, security exploits, or biased decision-making lawsuits. ➡️How to Secure Your AI Supply Chain 1. Vendor Due Diligence – Set Clear Requirements 🔹Require a model card – Vendors must document data sources, known biases, and model limitations. 🔹Use an AI risk assessment questionnaire – Evaluate vendors against ISO42001 & #ISO23894 risk criteria. 🔹Ensure regulatory compliance clauses in contracts – Include legal indemnities for compliance failures. 💡Why This Works: Many vendors haven’t certified against ISO42001 yet, but structured risk assessments provide visibility into potential AI liabilities. 2️. Continuous AI Supply Chain Monitoring – Track & Audit 🔹Use version-controlled model registries – Track model updates, dataset changes, and version history. 🔹Conduct quarterly vendor model audits – Monitor for bias drift, adversarial vulnerabilities, and performance degradation. 🔹Partner with AI security firms for adversarial testing – Identify risks before attackers do. (Gemma Galdon Clavell, PhD , Eticas.ai) 💡Why This Works: AI models evolve over time, meaning risks must be continuously reassessed, not just evaluated at procurement. 3️. Contractual Safeguards – Define Accountability 🔹Set AI performance SLAs – Establish measurable benchmarks for accuracy, fairness, and uptime. 🔹Mandate vendor incident response obligations – Ensure vendors are responsible for failures affecting your business. 🔹Require pre-deployment model risk assessments – Vendors must document model risks before integration. 💡Why This Works: AI failures are inevitable. Clear contracts prevent blame-shifting and liability confusion. ➡️ Move from Idealism to Realism AI supply chain risks won’t disappear, but they can be managed. The best approach? 🔸Risk awareness over blind trust 🔸Ongoing monitoring, not just one-time assessments 🔸Strong contracts to distribute liability, not absorb it If you don’t control your AI supply chain risks, you’re inheriting someone else’s. Please don’t forget that.

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.

Why teams don't trust AI-powered analytics -- and how my team and I believe it can be turned around: Think about how we work as humans. When someone gives us an answer we naturally ask for more details before trusting it. So when an experienced analyst looks at a dashboard that says the average number of DAUs is **680** their first instinct is not to accept the numbers blindly. It’s to ask follow-up questions: → Did you include demo accounts? → Which table did you pull from? → Are we counting logins or API calls? That instinct is about trust. Numbers on their own don’t earn it. The same is true for AI. If an AI agent spits out a number but gives no way to check its reasoning, professional users won’t trust it. Trust comes from context. And in data & AI, context comes from metadata: → Lineage → Usage → Logic → Semantics → Ownership When people can drill down and see the why behind the what, something shifts. That’s when raw numbers become answers you can trust. So don’t wait on AI. Context is where you start.

Companies are right to have trust issues when it comes to adopting AI. In the world of academic research, trust is built through citations. Thousands of published papers rely on clearly sourced information to establish credibility. Stephen Taylor, CIO at Vast Bank, believes the same principle applies to building trust in AI (shared on this week's episode of Pioneers). To trust the outputs of an AI system, you need to know: → Where the data comes from → How the data was processed and analyzed → What the system of record is for each piece of information By clearly explaining the sources and methods behind AI-generated insights, you can: → Establish credibility for the AI's outputs → Enable users to verify the reliability of the information → Give stakeholders confidence in the AI's decision-making process Just as citations lend weight to academic research, data transparency builds trust in AI. When implementing AI solutions, make sure to: → Clearly document data sources and systems of record → Make it easy for users to trace insights back to their original sources Trust is essential for the widespread adoption of AI in industries like banking. It’s too risky to accept what the AI models are producing blindly. But we can begin to build the trust needed for AI to thrive by prioritizing transparency and citation. — How do you think data transparency and citation can help build trust in AI? What other factors do you consider important for establishing credibility in AI systems?