How do errors, gaps, and biases in training data impact AI fairness, reliability, and real-world decision-making?
In this clip from the “The Hidden Costs of Poor Data Quality in AI” panel hosted by Data Science Connect, Jon Malloy, Senior Technical Account Manager at Snowplow, shares a real customer example that perfectly illustrates how biased datasets can completely mislead an AI model.
Jon explains:
- Why AI can produce confident but wrong insights when training data isn’t representative
- How data from only self-selected, measurable users created false engagement signals
- Why the AI model predicted extremely high engagement—but real-world performance collapsed
- How hidden dataset bias led to inaccurate decisions and failed content rollouts
- Why understanding who is not in your data matters just as much as understanding who is
- The importance of continuously monitoring data representativeness to avoid misleading outputs
This clip is essential for teams working in machine learning, AI fairness, MLOps, analytics, data governance, and product decision-making.
🔗 Watch the full webinar here:
https://snowplow.io/events/the-hidden-costs-of-poor-data-quality-in-ai
#dataquality #datagovernance
AI bias, training data quality, representativeness, machine learning errors, AI fairness, Snowplow, model reliability, data governance, Data Science Connect.