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Data Infrastructure for AI: Privacy First
07 May 2025
Women in Tech USA Stage
AI's reliance on data presents key privacy challenges in data infrastructure, data governance and compliance. This talk explores best practices for AI, emphasizing privacy by design, data minimization, and transparency and accountability. It further explores user control and data ownership, the role of standards and certifications, and the importance of tracking data with comprehensive lineage. It concludes by looking forward to the future of AI ethics and privacy.
The Why: Data privacy is paramount to AI development because models relies on data. We need to understand privacy challenges, governance, and best practices to develop AI responsibly. This knowledge is crucial for ethical AI development and navigating increasing privacy regulations.
Key Takeaways:
This session is for... AI developers and engineers; data scientists and analysts; privacy officers and legal counsels; technology executive and managers; anyone intereted in the ethical implications of AI.
The Why: Data privacy is paramount to AI development because models relies on data. We need to understand privacy challenges, governance, and best practices to develop AI responsibly. This knowledge is crucial for ethical AI development and navigating increasing privacy regulations.
Key Takeaways:
- AI models rely on large and diverse datasets, which often contain sensitive information.
- Key privacy challenges in AI data infrastructure involve data collection and ingestion, data storage and processing, and model training and deployment.
- Emphasis is needed for defining best practices for AI, including privacy by design, data minimization, and transparency and accountability.
This session is for... AI developers and engineers; data scientists and analysts; privacy officers and legal counsels; technology executive and managers; anyone intereted in the ethical implications of AI.
