Ensuring Transparency In AI Decision-Making

🛑AI Explainability Is Not Optional: How ISO42001 and ISO23053 Help Organizations Get It Right🛑 We see AI making more decisions that affect people’s lives: who gets hired, who qualifies for a loan, who gets access to healthcare. When those decisions can’t be explained, our trust erodes, and risk escalates. For your AI System(s), explainability isn’t a nice-to-have. It has become an operational and regulatory requirement. Organizations struggle with this because AI models, especially deep learning, operate in ways that aren’t always easy to interpret. Regardless, the business risks are real and regulators are starting to mandate transparency, and customers and stakeholders expect it. If an AI system denies a loan or approves one person over another for a job, there must be a way to explain why. ➡️ISO42001: Governance for AI Explainability #ISO42001 provides a structured approach for organizations to ensure AI decisions can be traced, explained, and reviewed. It embeds explainability into AI governance in several ways: 🔸AI Risk Assessments (Clause 6.1.2, #ISO23894) require organizations to evaluate whether an AI system’s decisions can be understood and audited. 🔸AI System Impact Assessments (Clause 6.1.4, #ISO42005) focus on how AI affects people, ensuring that decision-making processes are transparent where they need to be. 🔸Bias Mitigation & Explainability (Clause A.7.4) requires organizations to document how AI models arrive at decisions, test for bias, and ensure fairness. 🔸Human Oversight & Accountability (Clause A.9.2) mandates that explainability isn’t just a technical feature but a governance function, ensuring decisions are reviewable when they matter most. ➡️ISO23053: The Technical Side of Explainability #ISO23053 provides a framework for organizations using machine learning. It addresses explainability at different stages: 🔸Machine Learning Pipeline (Clause 8.8) defines structured processes for data collection, model training, validation, and deployment. 🔸Explainability Metrics (Clause 6.5.5) establishes evaluation methods like precision-recall analysis and decision traceability. 🔸Bias & Fairness Detection (Clause 6.5.3) ensures AI models are tested for unintended biases. 🔸Operational Monitoring (Clause 8.7) requires organizations to track AI behavior over time, flagging changes that could affect decision accuracy or fairness. ➡️Where AI Ethics and Governance Meet #ISO24368 outlines the ethical considerations of AI, including why explainability matters for fairness, trust, and accountability. ISO23053 provides technical guidance on how to ensure AI models are explainable. ISO42001 mandates governance structures that ensure explainability isn’t an afterthought but a REQUIREMENT for AI decision-making. A-LIGN #TheBusinessofCompliance #ComplianceAlignedtoYou

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

🩺 “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