Summary In today's episode, I break down the difference between fair and unfair bias in AI and machine learning, and explain why laws and values define the line between acceptable targeting and illegal discrimination. Here's what this means for you. You bear personal liability whenever your models disadvantage protected classes, even if you never intended…
Summary In today's episode, I explore whether you can trust public datasets and pre-trained models from vendors like Amazon and Google to be free of bias in your machine learning work. Here's what this means for you. You gain a risk-based framework for deciding when to build your own model versus when you can rely…
Summary In today's episode, I explore whether AI makes the challenges of data democratization even worse and what determines the outcome. Here's what this means for you. You gain a clear framework for understanding why expertise matters more than ever when deploying AI tools and analytics systems. You'll also learn these concepts: why naive users…
Summary In today's episode, I critique TED.com's top 10 careers list for 2030 and explain why AI-driven roles offer more durable opportunities than pandemic-focused ones. Here's what this means for you. You get a longer-term framework for spotting careers that survive well beyond the next couple of years. You'll also learn these concepts: how AI…
Summary In today's episode, I explain how to evaluate racial and gender bias in digital marketing platforms and walk through a practical framework for auditing your own data. Here's what this means for you. You gain a repeatable method for checking whether your marketing data skews away from your target population in ways that could…
Summary In today's episode, I walk through a year-over-year analysis of FBI hate crime data comparing 2017 to 2018 and reveal what the numbers actually say versus what they appear to say. Here's what this means for you. You'll see how a metric can look great on paper while the underlying data quality quietly collapses…
Oz asks, “There’s still the ethical questions that my marketing friends avoid: should Google even have all that data? How ethical is it to use that data? What about pending legislation that would hopefully criminalize lots of common data acquisition methods?” Two points I think are super important: minimum viable data, and behavior rather than…
Summary In today's episode, I unpack the ethical questions around Google's consumer data collection and what marketers should do about it. Here's what this means for you. You can cut your legal risk and boost your results by collecting only the data you can prove matters to your outcomes. You'll also learn these concepts: minimum…
Summary In today's episode, I explore how marketers can ensure the ethical use of AI by grounding decisions in utilitarian principles and examining their company's moral foundation before adopting powerful tools. Here's what this means for you. You learn that AI amplifies existing behavior rather than creates new ethics, so your organization's character determines whether…
