How Data Scientists Reduce Model Bias with Fairness Metrics
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
Bias in machine learning isn't just a fairness problem — it's a business risk. In this episode, Lucas and Luna unpack how data scientists are using fairness metrics like demographic parity and equalized odds to audit models before they ship. They walk through a real case: a credit approval model that quietly denied loans to applicants from certain postal codes, and how the team caught it with a bias audit. Along the way, they discuss the limits of fairness metrics, the trade-off between accuracy and equity, and why bias is often a data problem, not just an algorithm problem. If you're building ML systems, this episode gives you a practical framework for measuring and mitigating bias — plus the questions you should ask your data team before deploying a model that affects people's lives. #BiasInAI #FairnessMetrics #MachineLearning #DataScience #CreditModeling #AlgorithmicFairness #DataEthics #ResponsibleAI #AI #ML #Tech #Business #FexingoBusiness #BusinessPodcast #DataSciencePodcast #Fexingo #Technology #Podcast Keep every episode free: buymeacoffee.com/fexingo
Embed this episode
No transcript yet.