Summary In today's episode, I sit down with Lauren Frazier from IBM to unpack what it really takes to build responsible and trustworthy AI systems that serve businesses and society fairly. Here's what this means for you. You'll walk away with a practical framework for evaluating whether your organization's AI operates fairly, holds itself accountable,…
Sit down with any group of marketers who are self-aware enough to be able to think as both marketers and consumers, and you’ll inevitably hear this expression at some point in the conversation: "Does that really work?" Usually, it’s in reference to a specific tactic of some kind, particularly one that seems far-fetched or distasteful.…
Summary In today's episode, I explore whether AI vendors will need to anonymize or share their training data sources, breaking down the trend toward opaque black-box models and the growing case for third-party auditing. Here's what this means for you. You gain a practical framework for knowing when pushing vendors for bias audits makes sense…
Summary In today's episode, I break down who bears legal liability when AI models cause harm or violate marketing and discrimination laws. Here's what this means for you. You discover that as the end user, you typically assume full responsibility for how you deploy AI in your business, not the vendor who built it. You'll…
Summary In today's episode, I break down how to vet AI and machine learning vendors for bias by walking through the six areas where bias commonly creeps into their systems. Here's what this means for you. You gain a practical due-diligence framework you can apply to any vendor relationship so you avoid partnering with companies…
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…
