Why Synthetic Data is needed for AI Fairness and Explainability
summary
Argues synthetic tabular synthetic data can unlock locked datasets for product development and privacy, with a startup workflow used by banks, insurers and Telefonica and a 50-person team running pilots. Shows airline and insurance case studies where synthetic sets speeded projects and a Humana pilot where tuned synthetic data produced fairer model outcomes for some fairness metrics. Details methods for shaping data (programmable synthetic data), known bias transfer risks, and adoption hurdles: regulation, cost, and deployment.
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chapters (7)
- 0:00Introduction
- 4:24Synthetic data definition
- 10:44Privacy and compliance
- 17:05Bias and fairness challenges
- 24:39Synthetic data for explainability
- 31:03Global adoption differences
- 40:00Future of synthetic data
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