Category: Machine Learning
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Building Trusted AI Systems: A Fireside Chat with IBM
I recently had the opportunity to sit down with Lauren Frazier from IBM to discuss how we go about building trusted AI systems in a fireside chat livestream. We covered a ton of ground. Can’t see anything? Watch it on YouTube here. What is Fair? Fairness is a difficult subject to tackle, because people have…
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AI for Marketers, Third Edition, Available Now!
Is AI still a mystery shrouded in an aura of mystique? Have you read report after report, article after article proclaiming its magical powers and wondered what exactly the big deal is? With every software vendor and services provider proclaiming that they too are an AI-powered company, it’s more difficult to demystify artificial intelligence and…
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2020 Rewind: Marketing and Machine Learning
Welcome to 2020 Rewind! In this series, we’re taking a look at the year that was and the wonderful shows and podcasts that had me on as a guest this past year. A fair amount of the time, I’d rather read than listen, but podcasts, live streams, webinars, and videos were how we made media…
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2020 Rewind: Artificial Intelligence in Sales
Welcome to 2020 Rewind! In this series, we’re taking a look at the year that was and the wonderful shows and podcasts that had me on as a guest this past year. A fair amount of the time, I’d rather read than listen, but podcasts, live streams, webinars, and videos were how we made media…
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You Ask, I Answer: Third Party Data and Model Audits?
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…
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You Ask, I Answer: Liability for Marketing AI Models?
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…
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You Ask, I Answer: Vetting Marketing AI Vendors for Bias?
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…
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You Ask, I Answer: Difference Between Fair and Unfair Bias?
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…
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You Ask, I Answer: Third Party Data and Trustworthiness?
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…
