As AI takes on roles once reserved for human judgment, the question of accountability becomes increasingly urgent. From autonomous vehicles to decision-making algorithms, we must ask: Who’s liable when things go wrong? In this entry of the ABCs of AI Ethics series, we explore: Legal gray zones around AI-caused harm The challenges of assigning responsibility […]
Read More… from L is for Liability: Who’s Accountable When AI Goes Rogue?
AI is often portrayed as a universally accessible tool, but most of the world is still locked out of its development and decision-making power. As a few tech giants control the majority of AI resources, the global knowledge gap continues to widen. Our latest installment in the ABCs of AI Ethics series highlights: The risks […]
Read More… from K is for Knowledge Gaps: Bridging AI’s Growing Divide
AI is not impartial—it reflects the values of those who build it.
This piece in the #ABCsOfAIethics series unpacks how systemic bias can get coded into seemingly neutral systems, and what it takes to course-correct. […]
Read More… from J is for Justice: Building Equitable AI in an Unequal World
This article explores how organizations can prevent AI-related harm by bridging technical excellence with human-centered impact, ensuring responsible and sustainable AI deployment. […]
How do the data centers powering AI influence its ethical impact? Discover how projects like Stargate are shaping the future of AI and learn why ethical infrastructure drives reduced reviews, fewer complaints, and higher ROI. Explore the business case for building a fairer AI ecosystem. […]
Read More… from I is for Infrastructure – Building Better AI Through Ethical Foundations
AI governance isn’t just about compliance—it’s about trust, transparency, and transformation. Explore how shifting from rigid controls to enabling governance can drive better AI adoption and innovation. […]
When a financial AI system showed perfect fairness metrics across all demographics, its creators were proud. But examining the weekend data revealed an uncomfortable truth: the system was 40% less likely to approve transactions outside traditional banking hours, inadvertently encoding socioeconomic bias into its “fair” decisions. After analyzing hundreds of AI systems throughout 2024, I’ve discovered that the most sophisticated approaches to fairness often create the most insidious biases. Join me as we explore the hidden complexities of AI fairness and uncover practical strategies for building AI systems that truly work for everyone. Drawing from real-world implementations and hard-learned lessons, we’ll examine why perfect metrics often hide deeper problems, and how organizations can move beyond surface-level equality to achieve genuine equity in their AI systems. […]
Read More… from F is for Fairness: Building AI Systems That Work for Everyone
Building excellent AI systems requires more than just technical performance. At the UN Science Summit, global leaders revealed that only 23% of AI deployments undergo comprehensive ethical assessments. From cross-border governance challenges to balancing technical and ethical debt, discover practical frameworks for implementing AI that excels both technically and ethically. Learn how leading organizations are measuring excellence beyond accuracy metrics and building AI systems that create lasting positive impact […]
Future-Proof Your Enterprise with Advanced AI Governance Strategies
AI is reshaping industries, and governance is the key to success. Discover cutting-edge strategies to create ethical, compliant, and scalable frameworks for your enterprise. Unlock actionable insights to navigate the complexities of AI governance and drive innovation. […]
Read More… from Building Future-Proof AI Governance: Advanced Strategies for Enterprise Success
Sometimes the most profound insights emerge from unexpected moments. While waiting for my Thanksgiving ham to cook, I discovered something unsettling about AI that would challenge my assumptions about enterprise implementation. As an IEEE CertifAIEd Lead Assessor, I’ve evaluated countless AI systems, but this holiday experiment in my kitchen revealed a critical gap between AI confidence and competence that every technology leader needs to understand.
The results were stark: leading AI models showed remarkably high confidence while failing at basic rule-following tasks. ChatGPT achieved 13% accuracy, Claude reached 21%, and Gemini performed best at 46% – yet all displayed confidence levels above 90%. This disconnect mirrors patterns I’ve witnessed in enterprise settings, where sophisticated AI implementations often mask fundamental governance gaps.
In this article, I share both personal insights from this unexpected experiment and professional guidance for implementing effective AI governance. Drawing from years of certification experience and real-world testing, I offer a practical framework for ensuring your AI systems don’t just appear competent, but actually follow critical operational rules.
[Read more to discover the four pillars of effective AI governance and a practical implementation roadmap for 2025…] […]
