Summary In today's episode, I explore when we can and can't agree to disagree on divisive issues by examining the role of intent and harm. Here's what this means for you. You gain a practical framework for deciding which disagreements deserve engagement and which ones demand resistance. You'll also learn these concepts: how intent to…
There are four levels of diversity and inclusion in terms of what outcomes we should realistically expect. Only one of the four is achievable. Can’t see anything? Watch it on YouTube here. Listen to the audio here: Download the MP3 audio here. Got a question for You Ask, I’ll Answer? Submit it here! Subscribe to…
Summary In today's episode, I explore whether artificial intelligence could eventually develop true consciousness and the computational breakthroughs required to reach that milestone. Here's what this means for you. You'll discover why current AI remains far from genuine thought and what technical hurdles stand between today's systems and machine sentience. You'll also learn these concepts:…
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,…
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…
