As orgs race to adopt AI, there's a growing risk: When historical and social context is removed from data, AI outputs become less accurate, less fair and less trustworthy.
Katherine Cheng, a social impact and employee engagement leader, delivers this wake-up-call on why removing context and history from data breaks AI, and why that matters to anyone using it.
You'll learn how to:
- Spot gaps in context, from housing inequities to pay disparities, that surface in AI outputs and shape decisions and messaging.
- Re-evaluate "neutral" data that isn't always complete and see how missing context can lead to biased results.
- Apply the principles of truth, fairness and data integrity to help you use AI more responsibly while protecting inclusion, belonging and trust.
