Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
When AI Gets It Wrong and No One Notices
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Recently, I’ve been approached on more than one occasion by consulting clients asking me to review statistical methodologies developed with AI assistance. In all cases, my clients came from non-technical backgrounds. And what they’ve managed to produce is impressive: detailed statistical specifications that would otherwise have required years of study to develop. Their work is living proof of just how far AI has come. But on reviewing the work, one thing I invariably notice is that while on the surface what the AI has produced seems polished and professional, dig deeper and subtle methodological issues emerge - issues that are invisible without specialist expertise. In these situations, this is exactly why I’ve been brought in. But conducting these reviews has made me wonder about the situations where no one in the room has the expertise to validate AI outputs - including situations where I’m the one producing AI outputs in areas beyond my own expertise. Depending on the circumstances, the results could range from embarrassing to career-limiting or even catastrophic. AI has the potential to amplify human capabilities. But it also makes us vulnerable in ways we’ve never had to face before. In the latest episode of Value Driven Data Science, Derek Gibson, decision scientist and author of the upcoming Data, AI, and the Noise, joins me to share practical strategies for identifying unreliable AI outputs and building the defences necessary to keep AI-generated misinformation from reaching your stakeholders. You’ll discover:
Don’t wait for AI to get better at telling the truth. Build your defences now. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 111: Building Your Defences Against AI Misinformation Talk again soon, Dr Genevieve Hayes |
Data Science Impact Algorithm
Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
“AI destroys jobs but creates businesses.” That one statement crystallised what I’ve long believed about building a data science career. The statement was made by best-selling author and futurist Peter H. Diamandis in a recent post. Diamandis argues that while AI will lead to the destruction of white-collar jobs, as organisations replace human workers with AI, AI has made it easier than ever for entrepreneurs to launch one-person companies. The jobs that AI creates, therefore, won’t look like...
Quick quiz: If you randomly sampled just 5 people from a population of 20,000 or more, could you use that data to tell your stakeholders anything useful? For most data scientists, the answer is probably no. But according to Douglas Hubbard, author of How to Measure Anything, “you need far less data than you think.” Here’s the proof: If you randomly sample just five people from an organisation of any size and record the lowest and highest values of whatever you’re measuring, there’s a 93.75%...
I learned the true meaning of accountability on a Sunday morning, many years ago, when I had to go into the office to fix a calculation mistake made by a member of my team. I was an insurance pricing manager at the time. My team performed premium calculations that brought in $2 billion of revenue. It was complex work, spread across multiple staff, and the margin for error was incredibly low. When my boss spotted an error in a table my team had produced, it was all I could do not to say:...