The Importance of Transparency in AI Governance

🛑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

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 Belgium Data Protection Agency (DPA) published a report explaining the intersection between the GDPR and the AI Act and how organizations can align AI systems with data protection principles. The report emphasizes transparency, accountability, and fairness in AI, particularly for high-risk AI systems. The report also outlines how human oversight and technical measures can ensure compliant and ethical AI use. AI systems are defined based on the AI Act as machine-based systems that can operate autonomously and adapt based on data input. Examples in the report: spam filters, streaming service recommendation engines, and AI-powered medical imaging. GDPR & AI Act Requirements: The report explains how both frameworks complement each other: 1) GDPR focuses on lawful processing, fairness, and transparency. GDPR principles like purpose limitation and data minimization apply to AI systems which collect and process personal data. The report stresses that AI systems must use accurate, up-to-date data to prevent discrimination or unfair decision-making, aligning with GDPR’s emphasis on data accuracy. 2) AI Act adds prohibitions for high-risk systems, like social scoring and facial recognition. It also stresses bias mitigation in AI decisions and emphasizes transparency. * * * Specific comparisons: Automated Decision-Making: While the GDPR allows individuals to challenge fully automated decisions, the AI Act ensures meaningful human oversight for high-risk AI systems in particular cases. This includes regular review of the system’s decisions and data. Security: - The GDPR requires technical and organizational measures to secure personal data. - The AI Act builds on this by demanding continuous testing for potential security risks and biases, especially in high-risk AI systems. Data Subject Rights: - The GDPR grants individuals rights such as access, rectification, and erasure of personal data. - The AI Act reinforces this by ensuring transparency and accountability in how AI systems process data, allowing data subjects to exercise these rights effectively. Accountability: Organizations must demonstrate compliance with both GDPR and the AI Act through documented processes, risk assessments, and clear policies. The AI Act also mandates risk assessments and human oversight in critical AI decisions. See: https://lnkd.in/giaRwBpA Thanks so much Luis Alberto Montezuma for posting this report! #DPA #GDPR #AIAct

The Imperative of #Transparency in #AI: Insights from Dr. Jesse Ehrenfeld and the Boeing 737 Max Tragedy Jesse Ehrenfeld MD MPH President of the #AmericanMedicalAssociation, recently highlighted the critical need for transparency in AI deployments at the RAISE Health Symposium 2024. He referenced the tragic Boeing 737 Max crashes, where a lack of transparency in AI systems led to devastating consequences, underscoring the importance of clear communication and human oversight in AI applications. Key Lessons: 1. **Transparency is Non-Negotiable**: Dr. Ehrenfeld stressed that users must be fully informed about AI functionalities and limitations, using the Boeing 737 Max as a cautionary tale where undisclosed AI led to fatal outcomes. 2. **Expectation of Awareness**: Dr. Ehrenfeld provided a relatable example from healthcare, stating he would expect to know if a ventilator he was using in surgery was being adjusted by AI. This level of awareness is essential to ensure safety and effectiveness in high-stakes environments. 3. **Human Oversight is Essential**: The incidents highlight the need for human intervention and oversight, ensuring that AI complements but does not replace critical human decision-making. 4. **Building Trust in Technology**: Prioritizing transparency, safety, and ethics in AI is crucial for building trust and preventing avoidable disasters. As AI continues to permeate various sectors, it is imperative to learn from past mistakes and ensure transparency, thereby fostering a future where technology enhances human capabilities responsibly. **Join the Conversation**: Let's discuss how we can further integrate transparency in AI deployments across all sectors. Share your thoughts and experiences below. #AIethics #TransparencyInAI #HealthcareInnovation #DigitalHealth #DrGPT

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

👉 📢 Latest findings from the Responsible AI panel, where I contribute as a member, featured in MIT Sloan Management Review (with Boston Consulting Group (BCG): 💻 https://lnkd.in/gwpqv9ta ➡️ The new survey highlights the role of AI disclosures in fostering customer trust. With 84% of global experts in favor of mandatory AI transparency, it's another indication that responsible AI practices, including clear and ethical disclosures, are key to building confidence in AI-powered products and services. 🤔 Quotes of some of my contributions: GovLab’s Stefaan Verhulst agrees that “disclosures should be user-friendly and visually accessible to ensure comprehension.”...Verhulst remarks, “As a best practice, companies should not only disclose the use of AI in their operations but also detail how they will manage and protect the data generated and collected by these AI applications.” #AI #responsibleai #data #transparency #artificialintelligence #datastewardship

Ethical AI: Beyond Buzzwords Post Day 3#💥 AI makes a terrible call. Who takes the fall? A résumé gets silently rejected. A patient’s symptoms are dismissed by a diagnostic tool. An algorithm recommends a harsher sentence. A face recognition system flags the wrong person. And what do we hear? “That’s just what the AI said.” “The system flagged it. Not us.” 🚨 Nope. Let’s be really clear: AI doesn’t get to be the scapegoat. AI didn’t choose the data. It didn’t greenlight deployment. It didn’t write the documentation—or decide to skip it. Humans did that. So let’s stop hiding behind the black box. Because if the system is making life-changing decisions, someone has to be responsible. Here’s the tough truth: AI isn’t always wrong. But when it is—and it will be—the damage can be deep, fast, and hard to reverse. So who’s accountable? 🧠 Human-in-the-loop: Someone actively makes or approves decisions. 👁️ Human-on-the-loop: You’re monitoring, but not always in real time. 💣 No human in sight: Fully automated decision-making with no fallback. As we move further into automation, we need to get serious about AI governance: ✅ Clear audit trails (not “we think it made the decision because…”) ✅ Role ownership (who is the decision steward?) ✅ Testing not just for accuracy—but fairness, bias, and context ✅ Risk logs, escalation plans, real oversight And thankfully, regulators are waking up. The EU AI Act is the start—not the finish—of holding systems and creators accountable. 🔁 Here’s what I believe: If your AI product has power— To approve, to deny, to diagnose, to decide— Then you owe people transparency. Oversight. Redress. You don’t just need a model. You need a map of who’s in charge when things go wrong. This isn’t about fear. It’s about responsibility. 💬 So let me ask you: What role should you play when AI makes the call? Builder? Auditor? Human safety net? 👇 Drop your thoughts—especially if you’ve seen it go wrong, or helped get it right. #AIAccountability #EthicalAI #AIgovernance #MicrosoftTeamsAIChallenge #Sweepstakes

Ever been fooled by a chatbot thinking it was a real person? It happened to me! As AI continues to evolve, particularly in the realm of chatbots, transparency is more important than ever. In many interactions, it’s not always clear if you’re talking to a human or an AI—an issue that can affect trust and accountability. AI-powered tools can enhance convenience and efficiency, but they should never blur the lines of communication. People deserve to know when they’re interacting with AI, especially when it comes to critical areas like healthcare, customer service, and financial decisions. Transparency isn’t just ethical—it fosters trust, allows users to make informed decisions, and helps prevent misinformation or misunderstandings. As we integrate AI more deeply into our daily lives, let’s ensure clarity is a top priority. Transparency should be built into every interaction, making it clear when AI is at the wheel. That’s how we build responsible, reliable, and user-friendly AI systems. GDS Group #AI #Transparency #EthicsInAI #TrustInTechnology

One of the things that's important about implementing AI is to ensure people know when they are interacting with AI, whether that be in a live interaction or via AI-produced content. Brands that fail to be transparent can risk doing damage to customer relationships and reputation. By offering AI transparency and options, people can decide if they wish to engage with the AI or prefer an alternative. But if you offer AI interactions or content without transparency, it can leave people feeling deceived and manipulated. Arena Group, which owns Sports Illustrated, fired its CEO. The announcement only mentions "operational efficiency and revenue," but it comes weeks after an AI scandal hit the sports magazine. A tech publication discovered articles on SI that appeared to be from real humans were, in fact, created by AI. Even the headshots and biographies of the "authors" were AI-created. At the time, Arena Group blamed a third-party ad and content provider and severed its relationship with the firm. #GenAI can provide some remarkable benefits, but leaders must recognize the variety of risks that AI can bring. Being transparent about when customers are interacting with AI is one of the ways to mitigate the risks. Make it clear and conspicuous when you provide a #CustomerExperience facilitated by AI so that customers have the information and control they desire. https://lnkd.in/gnC2fE57

🤖 AI is evolving, but so are the questions we must ask! While AI, and particularly large language models (LLMs) like ChatGPT, are progressing rapidly, it’s important to remember that we’re still in the nascent stages of this technology. 🌱 Every month brings new advancements, but it also introduces skepticism—especially when it comes to trust and transparency. Let’s consider some critical questions: 1) Why are certain recommendations made? Whether you’re using AI to shortlist candidates in recruiting or identify top deals in your CRM, understanding why the AI makes those suggestions is crucial. 2) How do we balance excitement with caution? AI’s strength lies in tasks like summarization, but when it comes to business recommendations, users need clear insights into the reasoning behind its choices. Trust comes from transparency. As AI continues to progress, it’s important to keep an eye on its why and how to ensure we're getting not just powerful tools but reliable, explainable solutions. Its great to see Aravind Srinivas at Perplexity is bringing transparency to the results. We need more explainability and transparency in the AI. 💬 How are you incorporating AI while ensuring transparency in your use cases? #AI #Transparency #SalesAI #TrustInTech #FutureOfWork #llm