AI Accountability Protocols

‼️𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗨.𝗦. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀! AI governance now falls squarely on your shoulders. Here are the critical threats to watch for and questions you 𝘮𝘶𝘴𝘵 ask before spending a cent on any AI solution: 🚨 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗚𝗮𝗽𝘀 ❓ What security protocols and certifications does your system follow? How is sensitive data protected in transit and at rest? 🚨 𝗠𝗼𝗱𝗲𝗹 𝗕𝗶𝗮𝘀 & 𝗗𝗶𝘀𝗰𝗿𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻 ❓ How do you audit for fairness? Can you share your latest bias audit results? 🚨 𝗠𝗼𝗱𝗲𝗹 & 𝗗𝗮𝘁𝗮 𝗗𝗿𝗶𝗳𝘁 (when models become less accurate as data patterns change) ❓ What’s your process for detecting and correcting drift? How frequently is the model retrained or evaluated? 🚨 𝗜𝗻𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝗶𝗲𝘀 & 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀 (confident-sounding but incorrect or fabricated outputs) ❓ What safeguards are in place to prevent hallucinations? How do you validate the accuracy of generated content? 🚨 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗠𝗮𝗸𝗶𝗻𝗴 𝗨𝗻𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗲𝗱 𝗼𝗿 𝗕𝗮𝗱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 ❓ How do you control and log agent behavior when interacting with users or systems to ensure quality and safety? 🚨 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗿 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 ❓ Can you provide human-readable explanations for your outputs? Who is liable when things go wrong? 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Get every single answer in writing. If a vendor can't or won't document their answers, that's your 🚩 red flag. Walk away!

🔶Bridging Compliance and Strategy: How, Why, and What🔶 By integrating measurement methodologies inspired by Doug Hubbard's “How to Measure Anything” and John Doerr’s OKRs from “Measure What Matters”, organizations can quantify ethical progress and drive meaningful change leveraging #ISO standards. ➡ Using ISO Standards with Empirical Measures 1. Fairness as a Measurable Outcome ISO/IEC TS 12791 offers practical tools to identify and reduce bias in AI systems.  ☑Example OKR:      🅰Objective: Ensure AI outputs are equitable.      🅱Key Results:     - Reduce demographic disparities in system recommendations by 20%.     - Conduct quarterly audits of datasets for bias detection. 💡Hubbard's Insight:  Even seemingly intangible metrics, like fairness, can be quantified. Use proxy variables like decision consistency across demographics to track progress. 2. Transparency Through Explainability ISO5339 emphasizes transparency by guiding organizations in creating explainable decision pathways.  ☑Example OKR:      🅰Objective: Improve user trust in AI systems.      🅱Key Results:     - Achieve 90% satisfaction in user surveys related to system explainability.     - Implement traceability mechanisms in 100% of deployed systems. 💡Hubbard's Insight: Measuring trust can use tools like Net Promoter Scores (#NPS) or user feedback metrics. Quantifying subjective experiences, such as transparency, makes iterative improvements possible. 3. Accountability in Governance ISO/IEC 38507 defines governance frameworks to ensure clear accountability for AI decisions.  ☑Example OKR:      🅰Objective: Establish organizational accountability for AI outcomes.      🅱Key Results:     - Reduce the number of unresolved AI governance incidents to zero.     - Conduct biannual accountability reviews with stakeholder input. 💡Hubbard's Insight: Accountability can be quantified by tracking the resolution time for identified governance issues or through compliance rates in internal audits. 4. Continuous Adaptation and Resilience ISO42001 and ISO/IEC 23894 support lifecycle monitoring to adapt to societal changes and emerging risks.  ☑Example OKR:      🅰Objective: Maintain alignment with evolving ethical standards.      🅱Key Results:     - Update AI risk assessments every 3 months.     - Maintain 95% compliance with new regulatory requirements. 💡Hubbard's Insight: Measuring adaptability involves monitoring the time taken to incorporate new standards and the percentage of systems updated within defined timelines. ➡Combining Hubbard’s Metrics with Doerr’s OKRs Doerr’s OKRs provide a clear structure for setting ambitious yet achievable objectives, while Hubbard’s methodology ensures that even qualitative goals, like ethical AI, are measured empirically: ✅Use OKRs to define the “What” (e.g., "Improve fairness in AI systems"). ✅Apply Hubbard’s approach to measure the “How” (e.g., using decision parity or user sentiment as proxy metrics for fairness).

Few AI systems in production today meet basic standards for accountability, oversight, or risk documentation. That creates real exposure — operationally, legally, and socially. Sinple document offers framework to manage AI risk across the full lifecycle. → Aligns with EU AI Act, ISO/IEC 42001, and U.S. risk management standards → Emphasizes traceability, human oversight, and impact measurement → Applicable to high-risk sectors: healthcare, finance, public services The four core functions: → GOVERN: Assign roles, policies, and accountability → MAP: Identify context, purpose, and risk areas → MEASURE: Evaluate fairness, drift, and performance → MANAGE: Prioritize, act, and adapt What to do next: → Run a gap analysis against NIST AI 100-1 → Assign governance owners → Establish continuous monitoring → Document assumptions, risks, and decisions If AI shapes decisions, it needs oversight. NIST AI 100-1 is a starting point. #AIgovernance #NIST #AIrisk #AIsafety #ResponsibleAI #AIcompliance #MLOps #AIstandards

4 AI Governance Frameworks To build trust and confidence in AI. In this post, I’m sharing takeaways from leading firms' research on how organisations can unlock value from AI while managing its risks. As leaders, it’s no longer about whether we implement AI, but how we do it responsibly, strategically, and at scale. ➜ Deloitte’s Roadmap for Strategic AI Governance From Harvard Law School’s Forum on Corporate Governance, Deloitte outlines a structured, board-level approach to AI oversight: 🔹 Clarify roles between the board, management, and committees for AI oversight. 🔹 Embed AI into enterprise risk management processes—not just tech governance. 🔹 Balance innovation with accountability by focusing on cross-functional governance. 🔹 Build a dynamic AI policy framework that adapts with evolving risks and regulations. ➜ Gartner’s AI Ethics Priorities Gartner outlines what organisations must do to build trust in AI systems and avoid reputational harm: 🔹 Create an AI-specific ethics policy—don’t rely solely on general codes of conduct. 🔹 Establish internal AI ethics boards to guide development and deployment. 🔹 Measure and monitor AI outcomes to ensure fairness, explainability, and accountability. 🔹 Embed AI ethics into product lifecycle—from design to deployment. ➜ McKinsey’s Safe and Fast GenAI Deployment Model McKinsey emphasises building robust governance structures that enable speed and safety: 🔹 Establish cross-functional steering groups to coordinate AI efforts. 🔹 Implement tiered controls for risk, especially in regulated sectors. 🔹 Develop AI Guidelines and policies to guide enterprise-wide responsible use. 🔹 Train all stakeholders—not just developers—to manage risks. ➜ PwC’s AI Lifecycle Governance Framework PwC highlights how leaders can unlock AI’s potential while minimising risk and ensuring alignment with business goals: 🔹 Define your organisation’s position on the use of AI and establish methods for innovating safely 🔹 Take AI out of the shadows: establish ‘line of sight’ over the AI and advanced analytics solutions  🔹 Embed ‘compliance by design’ across the AI lifecycle. Achieving success with AI goes beyond just adopting it. It requires strong leadership, effective governance, and trust. I hope these insights give you enough starting points to lead meaningful discussions and foster responsible innovation within your organisation. 💬 What are the biggest hurdles you face with AI governance? I’d be interested to hear your thoughts.

AI is no longer an experiment on the sidelines. It is fast becoming the infrastructure on which businesses, societies, and decisions are built. But with this power comes responsibility. In my latest blog, I introduce the 𝐒𝐀𝐅𝐄 𝐀𝐈 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 - Secure, Accountable, Fair, and Explainable. SAFE is not just a checklist. It is a mindset shift for how we design, deploy, and govern AI systems that people can truly trust. Too often, safety enters the conversation only as an afterthought. My view is simple: responsibility must scale with innovation. Security must be woven into the pipeline, accountability must be clear, fairness must be tested continuously, and explainability of underlying AI (models and infra) must be non-negotiable. In the blog, I’ve shared a practical guide on how organizations and developers can operationalize SAFE AI across their lifecycle. In the end, trust is not given to technology; it is earned through how we build it. SAFE AI is not about slowing down. It is about ensuring that our acceleration takes us somewhere worth going. #FutureofWork #EngineeringTidbits #AI #SAFE #Secure #Accountable #Fair #Explainable Read more here:

AI Impact Assessment AI impact assessments are a critical tool for organizations developing and deploying artificial intelligence systems. The AIIA ensures the responsible design and implementation of AI systems by evaluating their impact, ethical implications, and compliance with regulations like the AI Act. It emphasizes accountability, quality, and reproducibility while mitigating risks associated with AI use. The AIIA V2 released by the Dutch Government is structured as follows: Part A: Assessment: focuses on factors to consider before using an AI system, including its purpose and expected effects. It also assesses the proportionality of AI deployment. Part A has three sub-sections: (a) System purpose and necessity. (b) Impact - Fundamental Rights - Sustainability - other consequences on organizational vision, mission etc (c) Assessing whether or not to use the AI system Part B: Implementation and use of AI system: focuses on the design, implementation, and use of the AI system. (a) Technical Robustness - Bias(including bias input, model and output) - Accuracy. - Reliability. - Technical Implementation. - Reproducibility. - Explainability. Data Governance - focuses on procedures regarding data access, ownership, usability, integrity, and security. - Data quality and integrity. - Privacy and confidentiality. Risk Management- focuses on identifying and managing potential risks associated with the AI system. - Risk Prevention. - Alternative procedure. - Information security risks. Accountability - focuses on ensuring accountability for the use and results of AI systems. - Transparency towards users. - Communication to parties involved( end users). - Verifiability. - Archiving. In conclusion, the AI Impact Assessment (AIIA) is a critical tool for ensuring the responsible and ethical use of AI systems. By addressing potential risks, safeguarding fundamental rights, and promoting transparency, the AIIA helps organizations align their AI initiatives with regulatory and ethical standards. Its structured approach fosters accountability, technical robustness, and sustainability, making it an essential framework for navigating the complexities of AI deployment in a rapidly evolving digital landscape. #AIImpactAssessment #AIIA #ImpactAssessment #AISecurity #EthicalAI #AISecurity #ArtificialIntelligence #CybersecurityGRC #InformationGovernance

This paper introduces the Team Card (TC) protocol as a governance tool to reduce bias in medical AI systems by addressing the influence of researchers' positionality. 1️⃣ Bias in medical AI is not only caused by poor data but also by the worldviews and assumptions of developers, which often go unexamined. 2️⃣ The Team Card helps teams reflect on and disclose their composition, identities, and perspectives to identify potential sources of bias. 3️⃣ The protocol emphasizes epistemic diversity—differences in how people think and solve problems—as critical for improving fairness and effectiveness in AI systems. 4️⃣ The TC includes structured reflections on team roles, institutional affiliations, geographical context, and identity, all of which shape AI design decisions. 5️⃣ Evidence from multiple disciplines shows that socially and cognitively diverse teams make better decisions, innovate more, and produce more impactful research. 6️⃣ The paper presents two case studies—dementia detection and risk prediction algorithms—to show how TCs could have improved equity and performance. 7️⃣ The TC is flexible in format and can be adapted using text, visuals, or multimedia, allowing teams to tailor disclosures while maintaining privacy. 8️⃣ The protocol promotes transparency and accountability without prescribing rigid requirements, making it suitable for diverse AI development contexts. 9️⃣ Limitations include lack of a standardized template, the need for cultural change in AI teams, privacy concerns, and challenges with scaling across large projects. 🔟 The TC offers a novel, low-cost way to embed ethical reflection into AI development workflows and encourages a shift toward inclusive and accountable innovation. ✍🏻 Lesedi Mamodise Modise, Mahsa Alborzi Avanaki, Saleem Ameen, Leo Anthony Celi, Victor Xin Yuan Chen, Ashley Cordes, Matthew Elmore, Amelia Fiske, Jack Gallifant, Megan Maree Hayes, Alvin Marcelo, Joao Matos, Luis Nakayama, Ezinwanne Ozoani, Benjamin C. Silverman, MD, Donnella S. Comeau. Introducing the Team Card: Enhancing governance for medical Artificial Intelligence (AI) systems in the age of complexity. PLOS Digit Health. 2025. DOI: 10.1371/journal.pdig.0000495

4 key steps that should be implemented to operationalize Responsible AI (RAI): 1. Establish Governance & Policies Set up a strong foundation for AI accountability. 💠Define ethical AI principles and standards 💠Create cross-functional AI governance boards 💠Develop clear policies on data usage, fairness, and transparency 2. Design for Responsibility Build responsibility into the AI system from the start. 💠Conduct bias and risk assessments early 💠Use diverse and representative training data 💠Implement human-in-the-loop mechanisms for critical decisions 3. Monitor & Evaluate Continuously Ensure ongoing oversight after deployment. 💠Track model performance and drift 💠Detect ethical or operational anomalies 💠Enable post-launch audits and incident response plans 4. Engage & Educate Stakeholders Promote awareness and build trust through transparency. 💠Communicate AI system capabilities and limitations 💠Provide explainability tools for end users 💠Train internal teams on ethical AI practices

The European Confederation of Institutes of Internal Auditing (ECIIA) has published “The AI Act: Road to Compliance,” which outlines critical steps to achieve compliance and effectively manage risks related to artificial intelligence (AI). The European Union’s Artificial Intelligence Act, which came into force in August 2024, marks a significant milestone in AI regulation. This legislation introduces a phased approach to compliance requirements for organizations deploying or planning to deploy AI systems in the European market. The Act aims to balance protecting fundamental rights and personal data with fostering innovation and building trust in AI technologies. Key Obligations Under the AI Act 1. AI Literacy Organizations must ensure that those responsible for operating or using AI systems have an adequate understanding of AI principles and practices. 2. AI Registry High-risk AI systems must be submitted to a central AI repository. Companies should establish their own internal AI registries, documenting all AI systems they utilize or bring to market. 3. AI Risk Assessment All systems listed in the AI registry must undergo risk assessments based on the classification methods outlined in the Act. Compliance with these standardized methods is mandatory. The obligations and requirements vary depending on the risk level and the organization’s role in the AI value chain. This regulation represents a vital step toward aligning innovation with responsibility. To learn more, explore ECIIA’s full publication and begin preparing your organization for the future of AI compliance. #ArtificialIntelligence #AICompliance #AIACT #InnovationAndTrust #RiskManagement

𝐀𝐫𝐞 𝐲𝐨𝐮 𝐮𝐬𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥 𝐜𝐚𝐫𝐝𝐬 𝐟𝐨𝐫 𝐦𝐨𝐝𝐞𝐥 𝐫𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠? If not, it's time to start. With new large language models emerging at a rapid pace this year, transparency and accountability should be our top priorities when building AI systems. 𝐌𝐨𝐝𝐞𝐥 𝐜𝐚𝐫𝐝𝐬, introduced by Google in 2019 as a way to transparently document ML models have become an essential tool for documenting and communicating key info about the ML models. They help developers reflect on ethical considerations, fairness, and build trust with stakeholders. 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐫𝐝𝐬? Model cards provide detailed documentation about AI models, including their intended use, performance metrics, ethical considerations, and limitations. These could be thought of as the "nutrition labels" for AI, offering a clear and concise summary of a model's capabilities and risks. It is a critical reference apart from the other documentation we maintain for the AI systems within our organizations. 𝐖𝐡𝐲 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐫𝐝𝐬? Transparency: Clear documentation of model performance, intended use, and limitations. Accountability: Encourages developers to critically assess and document their models. Trust: Builds trust with stakeholders by providing detailed information about the model. Regulatory Compliance: Facilitates compliance with industry standards and regulations. 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭 𝐌𝐨𝐝𝐞𝐥 𝐂𝐚𝐫𝐝𝐬? During PoC: Define objectives, document data sources, and conduct initial evaluations. During AI Development: Continuously update the model card with new findings and performance metrics. During Productization: Conduct thorough evaluations, have in place comprehensive documentation, and establish post-launch monitoring. 𝐅𝐫𝐚𝐮𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐌𝐋 𝐦𝐨𝐝𝐞𝐥 𝐜𝐚𝐫𝐝 - This is an example model card that I am sharing, using a fraud detection model developed for the finance sector as a template. By sharing this model card, regardless of the specific ML model you create, product engineering teams across businesses integrating AI can aim to enhance transparency and accountability in their AI development process by implementing model cards in their documentation process. To all AI model builders and product engineering teams: 𝐀𝐝𝐨𝐩𝐭𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥 𝐜𝐚𝐫𝐝𝐬 𝐬𝐡𝐨𝐮𝐥𝐝 𝐛𝐞 𝐚 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐚𝐧𝐝 𝐚𝐥𝐬𝐨 𝐢𝐬 𝐭𝐡𝐞 𝐧𝐞𝐞𝐝 𝐨𝐟 𝐭𝐡𝐞 𝐡𝐨𝐮𝐫 for building trust, transparency, and facilitating regulatory compliance to make AI development more responsible and ethical. Check out the new progress release by Google on 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐀𝐈 𝐏𝐫𝐨𝐠𝐫𝐞𝐬𝐬 𝐑𝐞𝐩𝐨𝐫𝐭. The research report talks about model cards, AI safety and governance topics, and their approach to AI responsibility (link in the comments). #MachineLearning #Data #AI #ResponsibleAI #Trustworthy #Transparency #Technology #SoftwareEngineering