AI Bias: 4 Disasters Every PM Must Know (Amazon, Apple, Healthcare) | AI for PMs Ep.8

AI Bias: 4 Disasters Every PM Must Know | AI for PMs Ep.8 AI bias isn't a technical bug — it's a product failure. And it's the PM's responsibility. In this episode, I break down WHERE bias comes from, show you 4 real-world disasters, and give you a practical Bias Audit Checklist you can use on your next AI feature. 🔑 Key Insight: 80% of AI bias comes from PRODUCT decisions — what data to collect, what metrics to optimize, who to design for. These are PM decisions, not engineering decisions. 📋 Bias Audit Checklist (8 Questions): 1. Who is in the training data? Who is missing? 2. What proxy variables could encode bias? 3. How does performance differ across subgroups? 4. What happens when the model is wrong? 5. Can we explain WHY a decision was made? 6. Is there a human appeal process? 7. How do we monitor for bias drift? 8. Have we tested with diverse users? 🔔 Subscribe for 2 new episodes every week! Next Episode: Build vs Buy vs Fine-Tune — The PM's Decision Framework 👇 Comment below: Has your product ever shipped a biased AI feature? What happened? #aibias #aiethics #productmanagement #responsibleai #aiforpms #AmazonHiringBias #FacialRecognitionBias #algorithmicbias #BiasAudit #FairnessInAI #euaiact #airegulations