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Advancing Accountable AI: A Readiness Guide For Privacy

Reduce risk with AI governance

Adoption and advancements of AI is ushering in a new set of opportunities — and risks — for organizations and privacy teams. Navigate the intersection of AI and privacy with robust AI data governance. Address the challenges posed by AI head on with actionable insights and steps to reduce risk.

Key takeaways
  • Reduce AI risks with security, model bias, discrimination, data privacy, data poisoning, false results, and model explainability.

  • Implement algorithmic accountability and governance across the software development life cycle (SDLC).

  • Operationalize technical accountability by embracing design transparency, auditing, monitoring, and controls.

  • Ensure transparency in AI systems to uphold individual rights.

AI is a dynamic new frontier that demands a clear and urgent approach to handling personal, sensitive, and confidential information. By adapting established privacy methods and tools, enterprises can be well-prepared to confront these challenges with assurance, upholding accountability and transparency in a rapidly evolving landscape.

– Jason Wesbecher, CEO of TrustArc

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