Understanding AI Model Reliability

In the realm of artificial intelligence, discerning truth from falsehood is more than a philosophical question—it’s a practical challenge that impacts business decisions and consumer trust daily. We are designing our new systems inspired by the classic dilemma of the Village of Truth and Lies, that can reliably manage the accuracy of their outputs. Here are some practical approaches that we are finding useful. 1. Multiple Agents: Use different AI models to answer the same question to cross-verify responses. 2. Consistency Checks: Follow-up with related questions to check the consistency of AI responses. 3. Confidence Estimation: Measure how confident an AI is in its answers, using this as a heuristic for reliability. 4. External Validation: Integrate verified databases to confirm AI responses wherever possible. 5. Feedback Loops: Incorporate user feedback to refine AI accuracy over time. 6. Adversarial Testing: Regularly challenge the system with tough scenarios to strengthen its discernment. 7. Ethical Responses: Design AIs to admit uncertainty and avoid making up answers. 8. Audit Trails: Keep logs for accountability and continuous improvement. I am also looking at game theoretic approach to estimating AI confidence. If you are interested in learning more, please feel free to connect for a discussion. Managing accuracy and trust is critical factor. By crafting smarter, self-aware AI systems, we pave the way for more reliable, transparent interactions—essential in today’s data-driven landscape. Please share your thoughts in the comments. #ArtificialIntelligence #MachineLearning #DataIntegrity #BusinessEthics #Innovation

We keep talking about model accuracy. But the real currency in AI systems is trust. Not just “do I trust the model output?” But: • Do I trust the data pipeline that fed it? • Do I trust the agent’s behavior across edge cases? • Do I trust the humans who labeled the training data? • Do I trust the update cycle not to break downstream dependencies? • Do I trust the org to intervene when things go wrong? In the enterprise, trust isn’t a feeling. It’s a systems property. It lives in audit logs, versioning protocols, human-in-the-loop workflows, escalation playbooks, and update governance. But here’s the challenge: Most AI systems today don’t earn trust. They borrow it. They inherit it from the badge of a brand, the gloss of a UI, the silence of users who don’t know how to question a prediction. Until trust fails. • When the AI outputs toxic content. • When an autonomous agent nukes an inbox or ignores a critical SLA. • When a board discovers that explainability was just a PowerPoint slide. Then you realize: Trust wasn’t designed into the system. It was implied. Assumed. Deferred. Good AI engineering isn’t just about “shipping the model.” It’s about engineering trust boundaries that don’t collapse under pressure. And that means: → Failover, not just fine-tuning. → Safeguards, not just sandboxing. → Explainability that holds up in court, not just demos. → Escalation paths designed like critical infrastructure, not Jira tickets. We don’t need to fear AI. We need to design for trust like we’re designing for failure. Because we are. Where are you seeing trust gaps in your AI stack today? Let’s move the conversation beyond prompts and toward architecture.

Sanjeev Mohan dives into why the success of AI in enterprise applications hinges on the quality of data and the robustness of data modeling. Accuracy Matters: Accurate, clean data ensures AI algorithms make correct predictions and decisions. Consistency is Key: Consistent data formats allow for smoother integration and processing, enhancing AI efficiency. Timeliness: Current, up-to-date data keeps AI-driven insights relevant, supporting timely business decisions. Just as a building needs a blueprint, AI systems require robust data models to guide their learning and output. Data modeling is crucial because: Structures Data for Understanding: It organizes data in a way that machines can interpret and learn from efficiently. Tailors AI to Business Needs: Customized data models align AI outputs with specific enterprise objectives. Enables Scalability: Well-designed models adapt to increasing data volumes and evolving business requirements. As businesses continue to invest in AI, integrating high standards for data quality and strategic data modeling is non-negotiable.

Opening the floodgates to more data isn't a surefire recipe for success in AI projects. In some cases, access to data and models at scale, only makes it easier to amplify harmful biases. This recent MIT study examines the consequences of not having the right data. Here's why it matters: 🚨 Quantity doesn't equal quality. Imagine having data from 1,000 patients, but only 10 are women over 70. This imbalance skews a model's reliability across different demographics. 🔍 The study highlights 'subpopulation shifts,' where machine learning models perform inconsistently for different demographic groups. In simpler terms, the same model could be accurate for one group but faulty for another. ⚖️ It's not only about how accurate a model is overall, but also how it performs within these subpopulations. The disparity can be life-altering, particularly in sectors like healthcare where the stakes are high. 💡 The illusion of data availability can be misleading. The focus should be on having accurate, verifiable, and representative samples, especially when lives are on the line. #AI #Healthcare #DataQuality #Equity 📊🌐

The UK Department for Science, Innovation and Technology published the guide "Introduction to AI assurance," to provide an overview of assurance mechanisms and global technical standards for industry and #regulators to build and deploy responsible #AISystems. #Artificialintelligence assurance processes can help to build confidence  in #AI systems by measuring and evaluating reliable, standardized, and accessible evidence about their capabilities. It measures whether such systems will work as intended, hold limitations, or pose potential risks; as well as how those #risks are being mitigated to ensure that ethical considerations are built-in throughout the AI development #lifecycle. The guide outlines different AI assurance mechanisms, including: - Risk assessments - Algorithmic impact assessment - Bias and compliance audits - Conformity assessment - Formal verification It also provides some recommendations for organizations interested in developing their understanding of AI assurance: 1. Consider existing regulations relevant for AI systems (#privacylaws, employment laws, etc) 2. Develop necessary internal skills to understand AI assurance and anticipate future requirements. 3. Review internal governance and #riskmanagement practices and ensure effective decision-making at appropriate levels.  4. Keep abreast of sector-specific guidance on how to operationalize and implement proposed principles in each regulatory domain.  5. Consider engaging with global standards development organizations to ensure the development of robust and universally accepted standard protocols. https://lnkd.in/eiwRZRXz

Trustworthy AI in production demands a fundamentally different approach to classical software. Unlike deterministic systems, AI applications – especially those built on LLMs and RAG – face constantly shifting data inputs, probabilistic outputs, and complex pipelines that span data, systems, code, and models. My colleague Shane Murray recently spoke on this same topic at the University of Arizona for IEEE International Congress on Intelligent and Service-Oriented Systems Engineering (CISOSE) alongside Vrushali C. (Dir Eng, Data & AI at Okta) Sharoon Srivastava (Principal Product Manager AI at Microsoft) Stephanie Kirmer (Senior MLE at DataGrail) Anusha Dwivedula (Director of PM at Morningstar) Vibe-coding a new AI tool might seem easy enough, but making it reliable is anything but. As Shane states in his position, to ensure reliability and trust, organizations must continuously observe every layer of their data + AI stack—not only in a secure testing environment, but live in production—by combining automated, scalable monitoring with human-in-the-loop oversight and a repeatable operational practice to rapidly root-cause and resolve issues. Only by pairing these approaches can we detect failures, mitigate risks, and sustain trust as AI systems evolve in the real world. You can see the full abstract from the session in the doc below. And if you want more from Shane, you can read his full thoughts in his latest article - or check out his feature in this week’s Alt Data Weekly (shout-out to John Farrall). Reliability isn’t a new challenge. But in the milieu of AI-everything, we need to define a different approach. The wheels are turning. Are you on board? Resources: https://lnkd.in/gZ_Nta3H https://lnkd.in/g8g2U3qs