Managing Ethical Risks In AI Startups

🧭Governing AI Ethics with ISO42001🧭 Many organizations treat AI ethics as a branding exercise, a list of principles with no operational enforcement. As Reid Blackman, Ph.D. argues in "Ethical Machines", without governance structures, ethical commitments are empty promises. For those who prefer to create something different, #ISO42001 provides a practical framework to ensure AI ethics is embedded in real-world decision-making. ➡️Building Ethical AI with ISO42001 1. Define AI Ethics as a Business Priority ISO42001 requires organizations to formalize AI governance (Clause 5.2). This means: 🔸Establishing an AI policy linked to business strategy and compliance. 🔸Assigning clear leadership roles for AI oversight (Clause A.3.2). 🔸Aligning AI governance with existing security and risk frameworks (Clause A.2.3). 👉Without defined governance structures, AI ethics remains a concept, not a practice. 2. Conduct AI Risk & Impact Assessments Ethical failures often stem from hidden risks: bias in training data, misaligned incentives, unintended consequences. ISO42001 mandates: 🔸AI Risk Assessments (#ISO23894, Clause 6.1.2): Identifying bias, drift, and security vulnerabilities. 🔸AI Impact Assessments (#ISO42005, Clause 6.1.4): Evaluating AI’s societal impact before deployment. 👉Ignoring these assessments leaves your organization reacting to ethical failures instead of preventing them. 3. Integrate Ethics Throughout the AI Lifecycle ISO42001 embeds ethics at every stage of AI development: 🔸Design: Define fairness, security, and explainability objectives (Clause A.6.1.2). 🔸Development: Apply bias mitigation and explainability tools (Clause A.7.4). 🔸Deployment: Establish oversight, audit trails, and human intervention mechanisms (Clause A.9.2). 👉Ethical AI is not a last-minute check, it must be integrated/operationalized from the start. 4. Enforce AI Accountability & Human Oversight AI failures occur when accountability is unclear. ISO42001 requires: 🔸Defined responsibility for AI decisions (Clause A.9.2). 🔸Incident response plans for AI failures (Clause A.10.4). 🔸Audit trails to ensure AI transparency (Clause A.5.5). 👉Your governance must answer: Who monitors bias? Who approves AI decisions? Without clear accountability, ethical risks will become systemic failures. 5. Continuously Audit & Improve AI Ethics Governance AI risks evolve. Static governance models fail. ISO42001 mandates: 🔸Internal AI audits to evaluate compliance (Clause 9.2). 🔸Management reviews to refine governance practices (Clause 10.1). 👉AI ethics isn’t a magic bullet, but a continuous process of risk assessment, policy updates, and oversight. ➡️ AI Ethics Requires Real Governance AI ethics only works if it’s enforceable. Use ISO42001 to: ✅Turn ethical principles into actionable governance. ✅Proactively assess AI risks instead of reacting to failures. ✅Ensure AI decisions are explainable, accountable, and human-centered.

Change is the only constant in AI. Here are 3 ways Chief AI Officers (CAIOs) drive responsible AI adoption by balancing ethics and regulatory concerns: 1. Adopt a Change Management Mindset 💡🤝 AI adoption is about more than tech. It’s about people. Smart CAIOs turn to change management to address the human side of AI. - Upskilling & Education 📚 Educate teams on AI’s real impact. Bust myths and address biases together. - Open Communication 🗣️ Create channels for employees to voice concerns on job changes and bias. Promote AI as a teammate, not a threat. - Example 🔍 Matt Lewis launched a program showing employees how AI enhances their roles, creating a culture of trust and collaboration. 2. Set Ethical Guardrails & Governance 🛡️🌐 Ethics in AI isn’t a “nice to have.” CAIOs are creating frameworks aligned with ethical and regulatory standards, especially in high-risk cases. - Guidelines & Transparency 📜 Clear guidelines are non-negotiable for privacy, bias, and accountability. - Human Oversight 👀 High-risk applications need a “human-in-the-loop” to make sure decisions stay accountable and transparent. - Ongoing Monitoring 🔄 This isn’t “set it and forget it”. CAIOs stress the need for continuous updates to match an evolving landscape. - Example 📊: Dr. Philipp Herzig launched an ethical review board to vet AI projects, ensuring diverse voices help avoid bias blind spots. 3. Navigate Global vs. Regional Regulations 🌍🗺️ Fragmented regulations force CAIOs to manage compliance without stifling innovation. - Global vs. Regional Flexibility 🌐 Some CAIOs push for a global baseline with regional adaptations, while others prefer ethical standards that may go beyond the minimum. - Example 🛠️ Nils o. Janus developed a core set of global standards, letting regional teams adapt to specific regulations. This enabled compliance without slowing things down too much. ____ 1. Change management 2. Ethical guardrails 3. Regulatory agility 👆 For CAIOs, these aren't guardrails. They're the backbone of successful AI deployment that benefits everyone.

The most dangerous AI isn't the one that fails. It’s the one that succeeds—at the wrong thing. After years of working with companies implementing AI, I’ve noticed something troubling: We obsess over capabilities but often neglect consequences. Here’s my practical framework for ethical AI implementation that won’t slow your progress: ✅ Define your ethical boundaries. The question isn’t just “Can we?” but “Should we?” Every company needs clear guidelines on AI applications they won’t pursue—no matter the ROI. Example: “We will never implement facial recognition systems that could enable unauthorized surveillance.” ✅ Scrutinize your data sources. Your AI is only as unbiased as the data feeding it. Development teams must understand what biases exist in their training data before writing a single line of code. 💡 Remember: AI doesn’t create bias—it amplifies what’s already there. ✅ Implement independent evaluation. The team building the AI shouldn’t be the only one testing it. Create separate evaluation teams tasked with actively trying to break, manipulate, or expose weaknesses in your AI systems. This isn’t slowing innovation—it’s preventing expensive mistakes. Smart businesses anticipate ethical concerns before they become PR disasters. What ethical boundaries have you established for AI in your organization? Drop your thoughts below. 👇

"This white paper offers a comprehensive overview of how to responsibly govern AI systems, with particular emphasis on compliance with the EU Artificial Intelligence Act (AI Act), the world’s first comprehensive legal framework for AI. It also outlines the evolving risk landscape that organizations must navigate as they scale their use of AI. These risks include: ▪ Ethical, social, and environmental risks – such as algorithmic bias, lack of transparency, insufficient human oversight, and the growing environmental footprint of generative AI systems. ▪ Operational risks – including unpredictable model behavior, hallucinations, data quality issues, and ineffective integration into business processes. ▪ Reputational risks – resulting from stakeholder distrust due to errors, discrimination, or mismanaged AI deployment. ▪ Security and privacy risks – encompassing cyber threats, data breaches, and unintended information disclosure. To mitigate these risks and ensure AI is used responsibly, in this white paper we propose a set of governance recommendations, including: ▪ Ensuring transparency through clear communication about AI systems’ purpose, capabilities, and limitations. ▪ Promoting AI literacy via targeted training and well-defined responsibilities across functions. ▪ Strengthening security and resilience by implementing monitoring processes, incident response protocols, and robust technical safeguards. ▪ Maintaining meaningful human oversight, particularly for high-impact decisions. ▪ Appointing an AI Champion to lead responsible deployment, oversee risk assessments, and foster a safe environment for experimentation. Lastly, this white paper acknowledges the key implementation challenges facing organizations: overcoming internal resistance, balancing innovation with regulatory compliance, managing technical complexity (such as explainability and auditability), and navigating a rapidly evolving and often fragmented regulatory landscape" Agata Szeliga, Anna Tujakowska, and Sylwia Macura-Targosz Sołtysiński Kawecki & Szlęzak

An AI policy is not AI governance. Too many organizations stop at writing policies, believing they've addressed their AI risks. But when regulators scrutinize your AI practices or when a model produces outputs that cost millions, that policy document won't protect you. Real AI governance requires mechanisms, not manifestos. It demands a comprehensive framework that connects people, processes, and practices across the entire AI lifecycle. The disconnect between policy and governance creates critical vulnerabilities: ⚖️ Legal and compliance risks extend beyond data privacy to intellectual property infringement, misleading conduct, and breach of industry obligations. Models trained on questionable data create IP landmines. Without proper governance, you can't demonstrate compliance when regulators come knocking. ⚙️ Technical and operational risks emerge when AI systems drift, hallucinate, or fail silently. Poor monitoring means problems compound before anyone notices. Dependencies on third-party models create vulnerabilities you can't patch. 🤝 Ethical and reputational risks destroy stakeholder trust. Algorithmic bias, opaque reasoning, or discriminatory outputs can eliminate your social license to operate faster than any traditional business risk. Moving beyond policy requires concrete actions: Who decides which AI systems get approved? What happens when a model starts producing garbage? How do you verify your vendor's training data was legally sourced? Who monitors for drift in production? ✅ Successful organizations establish clear ownership from board to operations. They create risk-based assessment processes with approval gates that match actual risk levels. They demand contractual terms that address model behavior, not just data handling. They implement continuous monitoring instead of annual reviews. Some classify AI systems by risk and apply proportionate controls. Others require vendors to prove training data sources and commit to performance thresholds. All connect procurement, legal, risk, and technical teams in ways that make oversight practical, not ceremonial. The organizations that will thrive understand that AI governance isn't a compliance exercise but a business enabler. They build living frameworks that protect while unlocking value, creating confidence and capability across the organization. 💡 If your answer to "Who's accountable when AI goes wrong?" involves pointing to a policy document, you have work to do. #legaltech #innovation #law #business #learning

💥 𝗧𝗵𝗲 𝗙𝗮𝗹𝗹 𝗼𝗳 𝗕𝘂𝗶𝗹𝗱𝗲𝗿.𝗮𝗶: 𝗔 𝗪𝗮𝗸𝗲-𝗨𝗽 𝗖𝗮𝗹𝗹 𝗳𝗼𝗿 𝗜𝗻𝘃𝗲𝘀𝘁𝗼𝗿𝘀 𝗮𝗻𝗱 𝗦𝘁𝗮𝗿𝘁𝘂𝗽𝘀 Builder.ai, once a rising star in the AI startup ecosystem with backing from giants like Microsoft and the Qatar Investment Authority, 𝗵𝗮𝘀 𝗳𝗶𝗹𝗲𝗱 𝗳𝗼𝗿 𝗯𝗮𝗻𝗸𝗿𝘂𝗽𝘁𝗰𝘆. The collapse stems from allegations of fraudulent financial practices. Most notably “𝘳𝘰𝘶𝘯𝘥-𝘵𝘳𝘪𝘱𝘱𝘪𝘯𝘨” with VerSe Innovation, where both companies reportedly invoiced each other without actual services rendered to inflate revenues. 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘄𝗲 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗶𝘀? 𝗗𝘂𝗲 𝗗𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗶𝘀 𝗡𝗼𝗻-𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲 Investors must move beyond glossy decks and headlines. Dig deep into financials, verify customer contracts, and ask uncomfortable questions. If something seems too good to be true — it usually is. 𝗘𝘁𝗵𝗶𝗰𝘀 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗠𝗮𝘁𝘁𝗲𝗿 𝗳𝗿𝗼𝗺 𝗗𝗮𝘆 𝗢𝗻𝗲 Startups need to embed governance, not treat it as an afterthought. A lightweight but effective ethics and compliance programme can reduce risk and build long-term trust. 𝗕𝗲𝘄𝗮𝗿𝗲 𝗼𝗳 "𝗔𝗜 𝗪𝗮𝘀𝗵𝗶𝗻𝗴" Inflated claims about AI capabilities are becoming common. Investors must pressure-test the tech, understand the actual product, and talk to real users — not just rely on founder vision. 🛠 𝗙𝗼𝗿 𝗶𝗻𝘃𝗲𝘀𝘁𝗼𝗿𝘀 𝘄𝗶𝘁𝗵 𝗯𝗼𝗮𝗿𝗱 𝘀𝗲𝗮𝘁𝘀, 𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝟯 𝘄𝗮𝘆𝘀 𝘁𝗼 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗼𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁: ↳𝗔𝘀𝗸 𝗳𝗼𝗿 𝗿𝗲𝗴𝘂𝗹𝗮𝗿 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝘂𝗽𝗱𝗮𝘁𝗲𝘀 Request quarterly summaries of financial controls, internal whistleblowing stats, and risk flags. If nothing is flagged for multiple quarters — that’s a red flag in itself.🚩 ↳𝗣𝘂𝘀𝗵 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗮𝘂𝗱𝗶𝘁 𝗼𝗿 𝘁𝗵𝗶𝗿𝗱-𝗽𝗮𝗿𝘁𝘆 𝗿𝗲𝘃𝗶𝗲𝘄𝘀 Even in early-stage companies, simple third-party audits or external reviews of high-risk areas (like revenue recognition or key partnerships) can surface major issues early. ↳𝗖𝗿𝗲𝗮𝘁𝗲 𝘀𝗽𝗮𝗰𝗲 𝗳𝗼𝗿 𝗶𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝘃𝗼𝗶𝗰𝗲𝘀 Make sure the CFO or GC (if there is one) has a direct line to the board. 𝗙𝗼𝘂𝗻𝗱𝗲𝗿𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗻𝗼𝘁 𝗯𝗲 𝘁𝗵𝗲 𝗼𝗻𝗹𝘆 𝘃𝗼𝗶𝗰𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗼𝗼𝗺. 𝘉𝘶𝘪𝘭𝘥 𝘢 𝘤𝘶𝘭𝘵𝘶𝘳𝘦 𝘸𝘩𝘦𝘳𝘦 𝘤𝘰𝘯𝘤𝘦𝘳𝘯𝘴 𝘤𝘢𝘯 𝘣𝘦 𝘳𝘢𝘪𝘴𝘦𝘥 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘧𝘦𝘢𝘳. You need to be using Builder.ai’s as a flashing warning light for the entire ecosystem. 𝗢𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 𝗶𝘀 𝗮 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆, 𝗻𝗼𝘁 𝗮 𝗯𝗼𝘅-𝘁𝗶𝗰𝗸. 🤙🏾 𝗜𝗳 𝘆𝗼𝘂 𝗱𝗼𝗻'𝘁 𝗸𝗻𝗼𝘄 𝘄𝗵𝗲𝗿𝗲 𝘁𝗼 𝗴𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱, 𝗼𝗿 𝗷𝘂𝘀𝘁 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗵𝗮𝘃𝗲 𝗮𝗻 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝗹 𝗰𝗵𝗮𝘁 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗶𝘀 𝘁𝗼𝗽𝗶𝗰, 𝗴𝗲𝘁 𝗶𝗻 𝘁𝗼𝘂𝗰𝗵. Also, highly recommend chatting with Rupert Evill who has built some great tools for both investors and founders. #BuilderAI #Governance #StartupBoards #DueDiligence #AIStartups #EthicsInTech #ImpactLawyers

The New Face of Risk: When AI Becomes Your Biggest Vulnerability Hook: Artificial Intelligence has become every organization’s favorite ally, and its most underestimated adversary. As enterprises rush to automate, optimize, and predict, they are quietly introducing a new class of risks that traditional frameworks were never designed to handle. Why This Matters AI is no longer a future trend, it’s an operational dependency. From fraud detection to predictive analytics, organizations are embedding machine learning models into their critical workflows. Yet, few are embedding AI governance into their risk programs. The result? A silent explosion of model drift, data bias, hallucinations, privacy exposure, and regulatory uncertainty. In essence, AI has become both the engine of innovation and the epicenter of organizational vulnerability. The Emerging Risk Landscape Here’s how the risk matrix is shifting: Data Integrity Risks: Unverified data sources and uncontrolled training pipelines distort outcomes and decisions. Privacy & Regulatory Risks: Sensitive data fed into AI tools can violate GDPR, HIPAA, and the forthcoming EU AI Act. Operational & Reputational Risks: Unchecked AI outputs can lead to discrimination, misinformation, or reputational collapse. Third-Party & Shadow AI Risks: Employee use of unapproved AI tools leads to hidden data leaks and compliance gaps. Cybersecurity Risks: AI models are becoming targets of prompt injection, model poisoning, and adversarial attacks. The Governance Imperative Mitigating these emerging risks requires structured, proactive AI risk governance ,not reactive compliance. Organizations must: Implement NIST AI RMF or ISO/IEC 23894 frameworks for AI risk management. Establish AI Governance Boards to bridge technical, ethical, and compliance oversight. Integrate continuous model validation to detect bias and performance degradation. Build AI transparency and accountability policies to maintain trust. Embed AI risk indicators into enterprise GRC dashboards for real-time visibility. AI isn’t inherently a risk; the absence of governance is. As the digital economy accelerates, the next major corporate crisis won’t stem from human error, but from machine confidence without human control. “In the age of intelligent systems, risk management is no longer about controlling humans, it’s about governing the minds we’ve built.” @ChiefRiskOfficer @ChiefInformationSecurityOfficer @ChiefDataOfficer @HeadOfCompliance @AI_Ethics_Community @Cybersecurity_Professionals_Network @RiskManagementProfessionals @Governance_Risk_Compliance_Group #AI #RiskManagement #AIGovernance #Cybersecurity #Compliance #DataGovernance #ArtificialIntelligence #GRC #RiskAssessment #TechnologyEthics #ModelRisk #NIST #ISO27001 #AIRegulation #AITrust #BusinessContinuity #OperationalRisk #Leadership #Innovation

There is no AI without AI governance (The 5 strategic imperatives for technical leaders) As AI proliferates in enterprises, a new paradigm for responsible implementation has been emerging. It's not just about compliance - it's about strategic advantage. Here are the 5 key imperatives for integrating responsible AI: 1. Align with corporate governance: • Integrate AI governance into existing GRC (Governance, Risk, and Compliance) frameworks • Implement explainable AI (XAI) techniques for model transparency • Develop data lineage tracking systems for GDPR and CCPA compliance 2. Implement robust risk management: • Adopt NIST AI Risk Management Framework, focusing on the Map, Measure, Manage, and Govern functions • Deploy AI risk registers with automated risk scoring and mitigation tracking • Implement continuous monitoring for model drift and performance degradation in high-risk AI systems 3. Establish clear accountability: • Form cross-functional AI Ethics Review Boards with defined escalation paths • Develop quantifiable KPIs for AI system fairness, accountability, and transparency (FAT) • Implement audit trails and version control for AI model development and deployment 4. Prioritize regulatory compliance: • Conduct impact assessments aligned with EU AI Act risk classifications (unacceptable, high, limited, minimal) • Implement technical measures for data minimization and purpose limitation • Develop compliance documentation systems for AI lifecycle management 5. Balance innovation and responsibility: • Establish AI sandboxes for controlled experimentation with novel algorithms • Implement federated learning techniques to enhance privacy in collaborative AI development • Develop internal AI ethics training programs with practical case studies and hands-on workshops The ROI? Reduced regulatory risk, enhanced reputation, and controlled innovation. Responsible AI isn't just risk mitigation - it's your ticket to becoming an ethical AI leader. What specific technical challenges are you facing in implementing responsible AI? #ResponsibleAI #AIGovernance #EnterpriseAI Please share your experiences in the comments! 👇

Did you know that in July this year, an AI coding tool wiped out a startup's production database and, on top of it, lied about it? Earlier in the summer, a global newspaper published a summer reading list of fake books because it had used an AI tool to research the list. Last February, a global airline had to pay damages because its AI-powered chatbot had lied. If you are a founder or a CXO looking to deploy AI responsibly and ethically, such that your company doesn't end up in an AI-soup, what are the key factors that you need to bear in mind? Here are some pearls of wisdom that I picked up from Kitman Cheung at IBM during the #ThinkSingapore event earlier this year: 🌟 Fairness: You need to train your models on an inclusive data set to ensure that there are as few biases as possible. At the end of the day, AI needs to treat people without prejudice 🌟 Transparent: You need to make sure that the AI systems are understandable, and disclose how they operate and reason, thus building trust and confidence. 🌟 Robustness: You want to ensure that AI can withstand attacks of various scales. The right guardrails and mechanisms need to be in place to not just alert management about attacks, but have an action planned out for various scenarios, including exception handling 🌟 Privacy: You have to protect customers' data and ensure that they are not shared or monetized without consent; it is archived for limited time periods and deleted thereafter. 🌟 Accountability: You need to ensure that clear responsibilities are mapped out and redressal mechanisms are in place when issues arise Such a framework will ensure that risk is appropriately mitigated; brand trust and organizational reputation are protected; regulations are complied with, whilst ensuring a culture of innovation that thrives within the enterprise. To implement a responsible and ethical AI framework, there needs to be buy-in from the leadership, and they need to encourage, enable, and empower their teams to: 👉 document AI training and testing data throughout its lifecycle 👉 put in place governance structures to keep a check and balance, and 👉 more importantly, provide tools, processes, and training to equip them If you haven't already done so, make it a point to discuss this with your management and leadership at the next town hall or board meeting and protect your AI initiative from derailing and your enterprise being in the press for the wrong reason! #ThinkSingapore #IBMPartner

Most AI tools in healthcare fail governance checks Risking compliance failures and loss of trust Companies that balance innovation with ethical accountability build stronger trust. Here are 10 steps to start with AI Governance: 1. Establish AI Governance Structure - Define AI governance roles: appoint an AI governance leader and form an oversight board. - Formalize AI policies aligned with corporate strategy and regulations. - Clarify definitions and scope of AI within the organization. 2. Inventory and Prioritize AI Use Cases - Catalog all AI applications in clinical and operational settings. - Identify high-risk, high-impact use cases for focused governance. - Prioritize based on clinical value, regulatory impact, and ethical concerns. 3. Assess Regulatory and Legal Risks - Map applicable regulations (FDA, HIPAA, EU AI Act, etc.) and compliance requirements. - Conduct risk assessments to identify legal, privacy, and intellectual property risks. - Embed automated compliance checks and audit trails in AI lifecycle management. 4. Ensure Data Quality and Integrity - Implement strong data governance: validation, classification, and metadata management. - Use diverse, representative datasets to reduce bias and improve fairness. - Maintain data privacy and consent management protocols rigorously. 5. Mitigate Bias and Promote Fairness - Regularly audit AI models for bias using fairness metrics. - Adjust training data and algorithms to correct detected biases. - Ensure AI tools are accessible and equitable across patient demographics. 6. Promote Transparency and Explainability - Develop explainable AI models and clear documentation of AI decision logic. - Communicate AI capabilities and limitations to clinicians and patients. - Maintain version control and detailed model documentation. 7. Monitor AI Performance and Safety Continuously - Set up ongoing monitoring for accuracy, errors, and model drift. - Use “red teams” or independent testing groups to stress-test AI applications. - Establish incident response protocols for AI-related adverse events. 8. Embed Ethical Principles and Accountability - Create ethical guidelines involving multidisciplinary stakeholders. - Define clear accountability for AI decisions and outcomes. - Document governance processes and decisions for transparency. 9. Balance Innovation with Risk Management - Use risk frameworks to evaluate new AI projects before deployment. - Encourage innovation while enforcing safety and compliance guardrails. - Adapt governance policies as AI technologies evolve. 10. Engage Stakeholders and Build Trust - Educate clinicians, patients, and partners on AI governance practices. - Report governance activities and AI outcomes transparently. - Collaborate with regulators, industry groups, and standards bodies. Want to learn more about this topic? Join our webinar on 24th April about AI Governance for HealthTech companies. Link in the comments.