Summary In today's episode, I explore why society drowns in conspiracy theories and trace the problem back to our false belief that all opinions carry equal weight. Here's what this means for you. You gain a sharper framework for evaluating claims by recognizing the real difference between genuine expertise and loud opinions. You'll also learn…
Summary In today's episode, I walk through how to master job interviews from both the hiring manager and candidate perspectives with practical advice for each side. Here's what this means for you. You gain a repeatable framework that treats interviewing as data collection so you make hiring decisions based on real signals instead of gut…
Summary In today's episode, I explore how the education system's industrial-era design leaves students unprepared for a world where AI, recommendation engines, and rampant misinformation dominate. Here's what this means for you. You gain a clear-eyed view of why your children's schools still emphasize obedience over inquiry, and what you must do at home to…
Summary In today's episode, I explore how AI bias originates from human data and what concrete steps organizations can take to identify and mitigate it. Here's what this means for you. You'll gain a practical framework for rooting out bias before it corrupts your models and erodes trust in your work. You'll also learn these…
Summary In today's episode, I sit down with Kathy McPhillips from the Marketing AI Institute for an honest fireside chat about how marketers are actually using AI and machine learning today, from content creation to ethics. Here's what this means for you. You'll hear a candid take on why most marketing AI investments fall flat…
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,…
Jessica asks, “When it comes to training data for marketing AI models, do you think vendors will anonymize/share data sources in the future? Will it be required?” It depends on the vendor and the model. The raw data for public models, even de-identified, probably will not be publicly available, but should be made available to…
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…
Jessica asks, “Who’s liable for violations in marketing AI models, the company that hires the vendor, or the vendor? Anything in service agreements to look for?” Who’s liable when someone uses a tool improperly or illegally? Companies have some responsibility, as we see with product warnings, but the ultimate responsibility – and who gets sued/arrested…
Tracy asks, “What are some questions you should ask vendors to better understand what data they use in their algorithms to make sure it’s not biased?” It’s not just questions we need to ask. Consider checking for bias to be like any other audit or due diligence. We will want to investigate the 6 main…
