How AI Models can Ensure Trustworthiness and Transparency

Why would your users distrust flawless systems? Recent data shows 40% of leaders identify explainability as a major GenAI adoption risk, yet only 17% are actually addressing it. This gap determines whether humans accept or override AI-driven insights. As founders building AI-powered solutions, we face a counterintuitive truth: technically superior models often deliver worse business outcomes because skeptical users simply ignore them. The most successful implementations reveal that interpretability isn't about exposing mathematical gradients—it's about delivering stakeholder-specific narratives that build confidence. Three practical strategies separate winning AI products from those gathering dust: 1️⃣ Progressive disclosure layers Different stakeholders need different explanations. Your dashboard should let users drill from plain-language assessments to increasingly technical evidence. 2️⃣ Simulatability tests Can your users predict what your system will do next in familiar scenarios? When users can anticipate AI behavior with >80% accuracy, trust metrics improve dramatically. Run regular "prediction exercises" with early users to identify where your system's logic feels alien. 3️⃣ Auditable memory systems Every autonomous step should log its chain-of-thought in domain language. These records serve multiple purposes: incident investigation, training data, and regulatory compliance. They become invaluable when problems occur, providing immediate visibility into decision paths. For early-stage companies, these trust-building mechanisms are more than luxuries. They accelerate adoption. When selling to enterprises or regulated industries, they're table stakes. The fastest-growing AI companies don't just build better algorithms - they build better trust interfaces. While resources may be constrained, embedding these principles early costs far less than retrofitting them after hitting an adoption ceiling. Small teams can implement "minimum viable trust" versions of these strategies with focused effort. Building AI products is fundamentally about creating trust interfaces, not just algorithmic performance. #startups #founders #growth #ai

Generative AI is transforming industries, but as adoption grows, so does the need for trust and reliability. Evaluation frameworks ensure that generative AI models perform as intended—not just in controlled environments, but in the real world. Key Insights from GCP Blog : Scalable Evaluation - new batch evaluation API allows you to assess large datasets efficiently, making it easier to validate model performance at scale. Customizable Autoraters - Benchmark automated raters against human judgments to build confidence in your evaluation process and highlight areas for improvement. Agentic Workflow Assessment - For AI agents, evaluate not just the final output, but also the reasoning process, tool usage, and decision trajectory. Continuous Monitoring - Implement ongoing evaluation to detect performance drift and ensure models remain reliable as data and user needs evolve. - Key Security Considerations: - Data Privacy: Ensure models do not leak sensitive information and comply with data protection regulations - Bias and Fairness: Regularly test for unintended bias and implement mitigation strategies[3]. - Access Controls:Restrict model access and implement audit trails to track usage and changes. - Adversarial Testing:Simulate attacks to identify vulnerabilities and strengthen model robustness **My Perspective: ** I see robust evaluation and security as the twin pillars of trustworthy AI. #Agent Evaluation is Evolving : Modern AI agent evaluation goes beyond simple output checks. It now includes programmatic assertions, embedding-based similarity scoring, and grading the reasoning path—ensuring agents not only answer correctly but also think logically and adapt to edge cases. Automated evaluation frameworks, augmented by human-in-the-loop reviewers, bring both scale and nuance to the process. - Security is a Lifecycle Concern: Leading frameworks like OWASP Top 10 for LLMs, Google’s Secure AI Framework (SAIF), and NIST’s AI Risk Management Framework emphasize security by design—from initial development through deployment and ongoing monitoring. Customizing AI architecture, hardening models against adversarial attacks, and prioritizing input sanitization are now standard best practices. - Continuous Improvement: The best teams integrate evaluation and security into every stage of the AI lifecycle, using continuous monitoring, anomaly detection, and regular threat modeling to stay ahead of risks and maintain high performance. - Benchmarking and Transparency: Standardized benchmarks and clear evaluation criteria not only drive innovation but also foster transparency and reproducibility—key factors for building trust with users and stakeholders. Check GCP blog post here: [How to Evaluate Your Gen AI at Every Stage](https://lnkd.in/gDkfzBs8) How are you ensuring your AI solutions are both reliable and secure?

This new white paper "Introduction to AI assurance" by the UK Department for Science, Innovation, and Technology from Feb 12, 2024, provides an EXCELLENT overview of assurance methods and international technical standards that can be utilized to create and implement ethical AI systems. The new guidance is based on the UK AI governance framework, laid out in the 2023 white paper "A pro-innovation approach to AI regulation". This white paper defined 5 universal principles applicable across various sectors to guide and shape the responsible development and utilization of AI technologies throughout the economy: - Safety, Security, and Robustness - Appropriate Transparency and Explainability - Fairness - Accountability and Governance - Contestability and Redress The 2023 white paper also introduced a suite of tools designed to aid organizations in understanding "how" these outcomes can be achieved in practice, emphasizing tools for trustworthy AI, including assurance mechanisms and global technical standards. See: https://lnkd.in/gydvi9Tt The new publication, "Introduction to AI assurance," is a deep dive into these assurance mechanisms and standards. AI assurance encompasses a spectrum of techniques for evaluating AI systems throughout their lifecycle. These range from qualitative assessments for evaluating potential risks and societal impacts to quantitative assessments for measuring performance and legal compliance. Key techniques include: - Risk Assessment: Identifies potential risks like bias, privacy, misuse of technology, and reputational damage. - Impact Assessment: Anticipates broader effects on the environment, human rights, and data protection. - Bias Audit: Examines data and outcomes for unfair biases. - Compliance Audit: Reviews adherence to policies, regulations, and legal requirements. - Conformity Assessment: Verifies if a system meets required standards, often through performance testing. - Formal Verification: Uses mathematical methods to confirm if a system satisfies specific criteria. The white paper also explains how organizations in the UK can ensure their AI systems are responsibly governed, risk-assessed, and compliant with regulations: 1.) For demonstrating good internal governance processes around AI, a conformity assessment against standards like ISO/IEC 42001 (AI Management System) is recommended. 2.) To understand the potential risks of AI systems being acquired, an algorithmic impact assessment by a accredited conformity assessment body is advised. This involves (self) assessment against a proprietary framework or responsible AI toolkit. 3.) Ensuring AI systems adhere to existing data protection regulations involves a compliance audit by a third-party assurance provider. This white paper also has exceptional infographics! Pls, check it out, and TY Victoria Beckman for posting and providing us with great updates as always!

✳ Bridging Ethics and Operations in AI Systems✳ Governance for AI systems needs to balance operational goals with ethical considerations. #ISO5339 and #ISO24368 provide practical tools for embedding ethics into the development and management of AI systems. ➡Connecting ISO5339 to Ethical Operations  ISO5339 offers detailed guidance for integrating ethical principles into AI workflows. It focuses on creating systems that are responsive to the people and communities they affect. 1. Engaging Stakeholders  Stakeholders impacted by AI systems often bring perspectives that developers may overlook. ISO5339 emphasizes working with users, affected communities, and industry partners to uncover potential risks and ensure systems are designed with real-world impact in mind. 2. Ensuring Transparency  AI systems must be explainable to maintain trust. ISO5339 recommends designing systems that can communicate how decisions are made in a way that non-technical users can understand. This is especially critical in areas where decisions directly affect lives, such as healthcare or hiring. 3. Evaluating Bias  Bias in AI systems often arises from incomplete data or unintended algorithmic behaviors. ISO5339 supports ongoing evaluations to identify and address these issues during development and deployment, reducing the likelihood of harm. ➡Expanding on Ethics with ISO24368  ISO24368 provides a broader view of the societal and ethical challenges of AI, offering additional guidance for long-term accountability and fairness. ✅Fairness: AI systems can unintentionally reinforce existing inequalities. ISO24368 emphasizes assessing decisions to prevent discriminatory impacts and to align outcomes with social expectations.  ✅Transparency: Systems that operate without clarity risk losing user trust. ISO24368 highlights the importance of creating processes where decision-making paths are fully traceable and understandable.  ✅Human Accountability: Decisions made by AI should remain subject to human review. ISO24368 stresses the need for mechanisms that allow organizations to take responsibility for outcomes and override decisions when necessary. ➡Applying These Standards in Practice  Ethical considerations cannot be separated from operational processes. ISO24368 encourages organizations to incorporate ethical reviews and risk assessments at each stage of the AI lifecycle. ISO5339 focuses on embedding these principles during system design, ensuring that ethics is part of both the foundation and the long-term management of AI systems. ➡Lessons from #EthicalMachines  In "Ethical Machines", Reid Blackman, Ph.D. highlights the importance of making ethics practical. He argues for actionable frameworks that ensure AI systems are designed to meet societal expectations and business goals. Blackman’s focus on stakeholder input, decision transparency, and accountability closely aligns with the goals of ISO5339 and ISO24368, providing a clear way forward for organizations.

𝐃𝐢𝐝 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐋𝐋𝐌 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧𝐬 𝐜𝐚𝐧 𝐛𝐞 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐝 𝐢𝐧 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞? In a recent post, I talked about why hallucinations happen in LLMs and how they affect different AI applications. While creative fields may welcome hallucinations as a way to spark out-of-the-box thinking, business use cases don’t have that flexibility. In industries like healthcare, finance, or customer support, hallucinations can’t be overlooked. Accuracy is non-negotiable, and catching unreliable LLM outputs in real-time becomes essential. So, here’s the big question: 𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐦𝐨𝐧𝐢𝐭𝐨𝐫 𝐟𝐨𝐫 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐚𝐬 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐚𝐬 𝐡𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧𝐬? That’s where the 𝐓𝐫𝐮𝐬𝐭𝐰𝐨𝐫𝐭𝐡𝐲 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 (𝐓𝐋𝐌) steps in. TLM helps you detect LLM errors/hallucinations by scoring the trustworthiness of every response generated by 𝐚𝐧𝐲 LLM.  This comprehensive trustworthiness score combines factors like data-related and model-related uncertainties, giving you an automated system to ensure reliable AI applications. 🏁 The benchmarks are impressive. TLM reduces the rate of incorrect answers from OpenAI’s o1-preview model by up to 20%. For GPT-4o, that reduction goes up to 27%. On Claude 3.5 Sonnet, TLM achieves a similar 20% improvement. Here’s how TLM changes the game for LLM reliability: 1️⃣ For Chat, Q&A, and RAG applications: displaying trustworthiness scores helps your users identify which responses are unreliable, so they don’t lose faith in the AI. 2️⃣ For data processing applications (extraction, annotation, …): trustworthiness scores help your team identify and review edge-cases that the LLM may have processed incorrectly. 3️⃣ The TLM system can also select the most trustworthy response from multiple generated candidates, automatically improving the accuracy of responses from any LLM. With tools like TLM, companies can finally productionize AI systems for customer service, HR, finance, insurance, legal, medicine, and other high-stakes use cases.  Kudos to the Cleanlab team for their pioneering research to advance the reliability of AI. I am sure you want to learn more and use it yourself, so I will add reading materials in the comments!

FDA Calls for Greater Transparency and Bias Mitigation in AI Medical Devices: ⚖️The recently issued US FDA draft guidance emphasizes transparency in AI device approvals, recommending detailed disclosures on data sources, demographics, blind spots, and biases ⚖️ Device makers should outline validation data, methods, and postmarket performance monitoring plans to ensure ongoing accuracy and reliability ⚖️ The guidance highlights the need for data diversity to minimize bias and ensure generalizability across populations and clinical settings ⚖️ Recommendations include using “model cards” to provide clear, concise information about AI models and their updates ⚖️ The FDA proposes manufacturers submit plans for updating and maintaining AI models without requiring new submissions, using pre-determined change control plans (PCCP) ⚖️ Concerns about retrospective-only testing and site-specific biases in existing AI devices highlight the need for broader validation methods ⚖️ The guidance is currently advisory but aims to set a higher standard for AI device approvals while addressing public trust in AI technologies 👇Link to articles and draft guidance in comments #digitalhealth #FDA #AI

🩺 “The scan looks normal,” the AI system says. The doctor hesitates. Will the clinician trust the algorithm? And perhaps most importantly—should they? We are entering an era where artificial intelligence will be woven into the fabric of healthcare decisions, from triaging patients to predicting disease progression. The potential is breathtaking: earlier diagnoses, more efficient care, personalized treatment plans. But so are the risks: opaque decision-making, inequitable outcomes, and the erosion of the sacred trust between patient and provider. The challenge is no longer just about building better AI. It’s about building better ways to decide if—and how—we should use it. That’s where the FAIR-AI framework comes in. Developed through literature reviews, stakeholder interviews, and expert workshops, it offers healthcare systems a practical, repeatable, and transparent process to: 👍 Assess risk before implementation, distinguishing low, moderate, and high-stakes tools. 👍 Engage diverse voices, including patients, to evaluate equity, ethics, and usefulness. 👍 Monitor continuously, ensuring tools stay aligned with their intended use and don’t drift into harm. 👍 Foster transparency, with plain-language “AI labels” that demystify how tools work. FAIR-AI treats governance not as a barrier to innovation, but as the foundation for trust—recognizing that in medicine, the measure of success isn’t how quickly we adopt technology, but how wisely we do it. Because at the end of the day, healthcare isn’t about technology. It’s about people. And people deserve both the best we can build—and the safeguards to use it well. #ResponsibleAI #HealthcareInnovation #DigitalHealth #PatientSafety #TrustInAI #HealthEquity #EthicsInAI #FAIRAI #AIGovernance #HealthTech

Transparency has become essential across AI legislation, risk management frameworks, standardization methods, and voluntary commitments alike. How to ensure that AI models adhere to ethical principles like fairness, accountability, and responsibility when much of their reasoning is hidden in a “black box”? This is where Explainable AI (XAI) comes in. The field of XAI is relatively new but crucial as it confirms that AI explainability enhances end-users’ trust (especially in highly-regulated sectors such as healthcare and finance). Important note: transparency is not the same as explainability or interpretability. The paper explores top studies on XAI and highlights visualization (of the data and process that goes behind it) as one of the most effective methods when it comes to AI transparency. Additionally, the paper highlights 5 levels of explanation for XAI (each suited for a person’s level of understanding): 1.      Zero-order (basic level): immediate responses of an AI system to specific inputs 2.      First-order (deeper level): insights into reasoning behind AI system’s decisions 3.      Second-order (social context): how interactions with other agents and humans influence AI system’s behaviour 4.      Nth order (cultural context): how cultural context influences the interpretation of situations and the AI agent's responses 5.      Meta (reflective level): insights into the explanation generation process itself

AI Governance: Map, Measure and Manage 1. Governance Framework:   - Contextualization: Implement policies and practices to foster risk management in development cycles.   - Policies and Principles: Ensure generative applications comply with responsible AI, security, privacy, and data protection policies, updating them based on regulatory changes and stakeholder feedback.   - Pre-Trained Models: Review model information, capabilities, limitations, and manage risks.   - Stakeholder Coordination: Involve diverse internal and external stakeholders in policy and practice development.   - Documentation: Provide transparency materials to explain application capabilities, limitations, and responsible usage guidelines.   - Pre-Deployment Reviews: Conduct risk assessments pre-deployment and throughout the development cycle, with additional reviews for high-impact uses. 🎯Map 2. Risk Mapping:   - Critical Initial Step: Inform decisions on planning, mitigations, and application appropriateness.   - Impact Assessments: Identify potential risks and mitigations as per the Responsible AI Standard.   - Privacy and Security Reviews: Analyze privacy and security risks to inform risk mitigations.   - Red Teaming: Conduct in-depth risk analysis and identification of unknown risks. 🎯Measure 3. Risk Measurement:   - Metrics for Risks: Establish metrics to measure identified risks.   - Mitigation Performance Testing: Assess effectiveness of risk mitigations. 🎯Manage 4. Risk Management:   - Risk Mitigation: Manage risks at platform and application levels, with mechanisms for incident response and application rollback.   - Controlled Release: Deploy applications to limited users initially, followed by phased releases to ensure intended behavior.   - User Agency: Design applications to promote user agency, encouraging users to edit and verify AI outputs.   - Transparency: Disclose AI roles and label AI-generated content.   - Human Oversight: Enable users to review AI outputs and verify information.   - Content Risk Management: Incorporate content filters and processes to address problematic prompts.   - Ongoing Monitoring: Monitor performance and collect feedback to address issues.   - Defense in Depth: Implement controls at every layer, from platform to application level. Source: https://lnkd.in/eZ6HiUH8

The California AG issues a useful legal advisory notice on complying with existing and new laws in the state when developing and using AI systems. Here are my thoughts. 👇 📢 𝐅𝐚𝐯𝐨𝐫𝐢𝐭𝐞 𝐐𝐮𝐨𝐭𝐞 ---- “Consumers must have visibility into when and how AI systems are used to impact their lives and whether and how their information is being used to develop and train systems. Developers and entities that use AI, including businesses, nonprofits, and government, must ensure that AI systems are tested and validated, and that they are audited as appropriate to ensure that their use is safe, ethical, and lawful, and reduces, rather than replicates or exaggerates, human error and biases.” There are a lot of great details in this, but here are my takeaways regarding what developers of AI systems in California should do: ⬜ 𝐄𝐧𝐡𝐚𝐧𝐜𝐞 𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲: Clearly disclose when AI is involved in decisions affecting consumers and explain how data is used, especially for training models. ⬜ 𝐓𝐞𝐬𝐭 & 𝐀𝐮𝐝𝐢𝐭 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬: Regularly validate AI for fairness, accuracy, and compliance with civil rights, consumer protection, and privacy laws. ⬜ 𝐀𝐝𝐝𝐫𝐞𝐬𝐬 𝐁𝐢𝐚𝐬 𝐑𝐢𝐬𝐤𝐬: Implement thorough bias testing to ensure AI does not perpetuate discrimination in areas like hiring, lending, and housing. ⬜ 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Establish policies and oversight frameworks to mitigate risks and document compliance with California’s regulatory requirements. ⬜ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐇𝐢𝐠𝐡-𝐑𝐢𝐬𝐤 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: Pay special attention to AI used in employment, healthcare, credit scoring, education, and advertising to minimize legal exposure and harm. 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐦𝐞𝐞𝐭𝐢𝐧𝐠 𝐥𝐞𝐠𝐚𝐥 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬—it’s about building trust in AI systems. California’s proactive stance on AI regulation underscores the need for robust assurance practices to align AI systems with ethical and legal standards... at least this is my take as an AI assurance practitioner :) #ai #aiaudit #compliance Khoa Lam, Borhane Blili-Hamelin, PhD, Jeffery Recker, Bryan Ilg, Navrina Singh, Patrick Sullivan, Dr. Cari Miller