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…
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…
Gianna asks, “What’s the difference between fair and unfair bias? What’s the fine line?” Fair and unfair comes down to two simple things: laws and values. Statistical bias is when your sample deviates from the population you’re sampling from. Bias isn’t inherently bad unless it crosses one of those two lines. Can’t see anything? Watch…
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…
Ashley asks, “If you choose to use public datasets for your ML models, like from Amazon or Google, can you trust that those are free of bias?” Can you trust a nutrition label on a food product? The analogy is the same. What’s in the box is important, but what went into the box is…
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…
Elizabeth asks, “What’s the most common type of bias you see that we as marketers should be aware of?” There are so many to choose from, but I’ll start with two: confirmation bias, and selection bias. Confirmation bias corrupts the entire process by looking for a result that fits a predetermined conclusion. Selection bias corrupts…
Summary In today's episode, I break down the two most common statistical biases that quietly wreck marketing research and AI models. Here's what this means for you. You'll gain a practical framework for spotting bias before it corrupts your data and leads your team to bad decisions. You'll also learn these concepts: why confirmation bias…
Jim asks, “I am skeptical of data democratization because the average decision maker does not understand data collection, transformation, integration etc. Doesn’t AI make this an even bigger problem?” It depends on how abstracted the decision-maker is. Certainly the pandemic has shown us the general population is completely incapable of parsing even basic scientific data,…
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…
