In this episode of the ODSC AIx Podcast, host Sheamus McGovern speaks with Alexandra Ebert, Chief AI and Data Democratization Officer at MOSTLY AI, and one of the foremost voices on privacy-preserving synthetic data and responsible AI. With a diverse background spanning AI ethics, data access policy, and generative AI regulation, Alexandra brings clarity to how synthetic data is not just a privacy tool but a lever for innovation, fairness, and scalable AI adoption.
The discussion explores what synthetic data is (and isn’t), its core advantages and limitations, its role in addressing fairness and access challenges in data-driven organizations, and how practitioners can actively shape better downstream model performance. The episode also dives into the MOSTLY AI Prize—a $100,000 global competition to advance privacy-safe, high-utility synthetic data generation.

Key Topics Covered:
- The different types and use cases of synthetic data (privacy-preserving, simulation-based, creative)
- How synthetic data helps solve the “data access paradox” in regulated industries
- Key advantages and limitations of synthetic data vs. real-world and legacy anonymized data
- Privacy mechanisms: Outlier suppression, statistical mimicry, empirical differential privacy
Real-world use cases in healthcare, finance, telco, and simulation environments
- Fairness-aware synthetic data generation using statistical parity constraints
- Imputing missing data with synthetic distributions
- Agentic AI and the role of synthetic data in enabling secure access layers for autonomous agents
- Up-sampling rare events (e.g. fraud) to support more explainable models
- Open innovation and the mission behind the MOSTLY AI Prize
- Tools, SDKs, and open-source workflows for getting started with synthetic data
-  The MOSTLY AI Prize—a $100,000 global competition

Memorable Outtakes
“We need to move beyond thinking of real data as the gold standard. It’s often inaccessible, messy, biased—and by design, it's limited to how it was collected. Synthetic data lets us ask: what if our data was as inclusive as we needed it to be?”
“So much of synthetic data’s value is in unlocking what’s been locked away—allowing teams to safely build, test, and deploy where real data just isn’t viable.”
“It’s not just about boosting performance. Synthetic upsampling lets you use simpler, more explainable models—ones you can actually audit.”

References & Resources
- Alexandra Ebert – Chief AI & Data Democratization Officer, MOSTLY AI
 Industry profile: https://mostly.ai/team/alexandra-ebert
 LinkedIn: https://www.linkedin.com/in/alexandraebert/
-MOSTLY AI Prize – Global competition to advance privacy-preserving synthetic data
 Website: https://www.mostlyaiprize.com/
- MOSTLY AI GitHub & SDK – Open-source tools for structured synthetic data
 https://github.com/mostly-ai
-  https://github.com/mostly-ai/mostly-ai-sdk
- Synthetic Data Fairness Paper (ICLR)
 "Representative & Fair Synthetic Data"
 Paper link: https://arxiv.org/abs/2104.03007
- Synthetic Data Vault (SDV)  https://sdv.dev
- TVAE under the SDV umbrella: https://github.com/sdv-dev/SDV

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Beyond Real: The Case for Synthetic Data + How to Win $100K with Alexandra Ebert

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Beyond Real: The Case for Synthetic Data + How to Win $100K with Alexandra Ebert

ODSC's Ai X Podcast