Best Practices for Ethical Data Governance

🔶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).

𝗧𝗵𝗲 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜: 𝗪𝗵𝗮𝘁 𝗘𝘃𝗲𝗿𝘆 𝗕𝗼𝗮𝗿𝗱 𝗦𝗵𝗼𝘂𝗹𝗱 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 "𝘞𝘦 𝘯𝘦𝘦𝘥 𝘵𝘰 𝘱𝘢𝘶𝘴𝘦 𝘵𝘩𝘪𝘴 𝘥𝘦𝘱𝘭𝘰𝘺𝘮𝘦𝘯𝘵 𝘪𝘮𝘮𝘦𝘥𝘪𝘢𝘵𝘦𝘭𝘺." Our ethics review identified a potentially disastrous blind spot 48 hours before a major AI launch. The system had been developed with technical excellence but without addressing critical ethical dimensions that created material business risk. After a decade guiding AI implementations and serving on technology oversight committees, I've observed that ethical considerations remain the most systematically underestimated dimension of enterprise AI strategy — and increasingly, the most consequential from a governance perspective. 𝗧𝗵𝗲 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 Boards traditionally approach technology oversight through risk and compliance frameworks. But AI ethics transcends these models, creating unprecedented governance challenges at the intersection of business strategy, societal impact, and competitive advantage. 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Beyond explainability, boards must ensure mechanisms exist to identify and address bias, establish appropriate human oversight, and maintain meaningful control over algorithmic decision systems. One healthcare organization established a quarterly "algorithmic audit" reviewed by the board's technology committee, revealing critical intervention points preventing regulatory exposure. 𝗗𝗮𝘁𝗮 𝗦𝗼𝘃𝗲𝗿𝗲𝗶𝗴𝗻𝘁𝘆: As AI systems become more complex, data governance becomes inseparable from ethical governance. Leading boards establish clear principles around data provenance, consent frameworks, and value distribution that go beyond compliance to create a sustainable competitive advantage. 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗜𝗺𝗽𝗮𝗰𝘁 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Sophisticated boards require systematically analyzing how AI systems affect all stakeholders—employees, customers, communities, and shareholders. This holistic view prevents costly blind spots and creates opportunities for market differentiation. 𝗧𝗵𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆-𝗘𝘁𝗵𝗶𝗰𝘀 𝗖𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 Organizations that treat ethics as separate from strategy inevitably underperform. When one financial services firm integrated ethical considerations directly into its AI development process, it not only mitigated risks but discovered entirely new market opportunities its competitors missed. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: 𝘛𝘩𝘦 𝘷𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴 𝘰𝘳 𝘳𝘦𝘭𝘢𝘵𝘦𝘥 𝘦𝘯𝘵𝘪𝘵𝘪𝘦𝘴. 𝘌𝘹𝘢𝘮𝘱𝘭𝘦𝘴 𝘥𝘳𝘢𝘸𝘯 𝘧𝘳𝘰𝘮 𝘮𝘺 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦 𝘩𝘢𝘷𝘦 𝘣𝘦𝘦𝘯 𝘢𝘯𝘰𝘯𝘺𝘮𝘪𝘻𝘦𝘥 𝘢𝘯𝘥 𝘨𝘦𝘯𝘦𝘳𝘢𝘭𝘪𝘻𝘦𝘥 𝘵𝘰 𝘱𝘳𝘰𝘵𝘦𝘤𝘵 𝘤𝘰𝘯𝘧𝘪𝘥𝘦𝘯𝘵𝘪𝘢𝘭 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯.

Data privacy and ethics must be a part of data strategies to set up for AI. Alignment and transparency are the most effective solutions. Both must be part of product design from day 1. Myths: Customers won’t share data if we’re transparent about how we gather it, and aligning with customer intent means less revenue. Instacart customers search for milk and see an ad for milk. Ads are more effective when they are closer to a customer’s intent to buy. Instacart charges more, so the app isn’t flooded with ads. SAP added a data gathering opt-in clause to its contracts. Over 25,000 customers opted in. The anonymized data trained models that improved the platform’s features. Customers benefit, and SAP attracts new customers with AI-supported features. I’ve seen the benefits first-hand working on data and AI products. I use a recruiting app project as an example in my courses. We gathered data about the resumes recruiters selected for phone interviews and those they rejected. Rerunning the matching after 5 select/reject examples made immediate improvements to the candidate ranking results. They asked for more transparency into the terms used for matching, and we showed them everything. We introduced the ability to reject terms or add their own. The 2nd pass matches improved dramatically. We got training data to make the models better out of the box, and they were able to find high-quality candidates faster. Alignment and transparency are core tenets of data strategy and are the foundations of an ethical AI strategy. #DataStrategy #AIStrategy #DataScience #Ethics #DataEngineering