AI

Recently, we explored the ethical frameworks required to govern artificial intelligence safely. This week, we pull back the curtain on the machine itself. To use these tools well, you don’t need a PhD in machine learning, but you do need to understand the handful of mathematical processes that determine what an LLM says. Opening up […]

In our recent discussions on AI ethics, we established that automated systems lack human judgment and need strict boundaries. Yet across industries, tech teams routinely roll out artificial intelligence tools without understanding how the human brain actually interacts with them. This gap leads to embarrassing and costly failures—like an airline chatbot making up refund policies […]

Over the past 4 weeks, we explored how the deployment of high-stakes AI systems requires a moral code that moves beyond a simple desire for optimization. We established that machines operate solely on formal mathematical directives and lack moral agency. Because of this, the responsibility for protecting human dignity, critical thinking, and workplace capabilities rests […]

Last week, in the third installment of our series on AI ethics and governance, we explored deontology and the duty-based responsibilities leaders hold when deploying software systems. We examined how moral obligations and strict boundaries must guide how organizations handle technology. Today, we turn our focus inward to the individual, examining how everyday interactions with […]

Artificial intelligence is rapidly reshaping everyday life, powering everything from automated hiring tools to medical diagnostics. Last week, we exposed the structural flaws of utilitarianism, demonstrating how Meta’s pursuit of optimization uses aggregate averages and sterile statistics to numb the reality of severe individual harms. To counter the trap of viewing human beings as mere […]

Last week, we introduced a structured ethical framework designed for AI governance. Building upon that idea, we must now confront how modern data science naturally defaults to an operating model we’ll call “utilitarian optimization.” Because machine learning architectures are fundamentally quantitative and outcomes-focused, technology leaders routinely view moral responsibility through the narrow lens of aggregate […]

Part I: Introduction to Ethical Frameworks “Even if you know nothing about digital marketing analytics, digital marketing analytics knows plenty about you.” When I started my book, Digital Marketing Analytics: In Theory and in Practice, with that line in 2020, the silent and invisible harvesting of consumer data was already reaching unprecedented levels. At the […]

Last week, former Google CEO Eric Schmidt delivered the commencement address at the University of Arizona. He beseeched graduates to actively shape the inevitable rise of artificial intelligence. Schmidt knows more about AI than almost anyone, and there is a lot we can learn from his perspective. Yet, as you have probably seen, his remarks […]

Welcome to 2026! The time for generic resolutions is over. Last year, I challenged you to become a better storyteller. That remains true, but the ground has shifted beneath us. The rapid evolution of AI means the role of the “Junior Data Analyst” is effectively dead. If your plan for this year is simply to […]

The presentation isn’t over when you show the final visual. It’s over when you survive the Q&A. This is where reputations are forged or shattered. You’ve spent weeks prompting an LLM to help wrangle data, form hypotheses, build models, and craft a narrative. You know your work is solid. But what happens when the VP […]