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Designing Trustworthy AI Products

Tuesday, March 3, 1 PM ET

Key Takeaways

  • Learn how design decisions directly affect the trustworthiness of AI-powered products.
  • Discover “fair patterns” to include—and “dark patterns” to avoid—when building AI features.
  • Understand the ethical considerations and business risks introduced by design flaws in AI systems.

Your Presenter(s)

Marie Potel-Saville headshot

Marie Potel-Saville

CEO & Co-founder at FairPatterns

Marie Potel-Saville is a human-centric AI entrepreneur, the Co-Founder & CEO of Fair Patterns, a multimodal AI that protects humans from digital harms and violations.She is also the Paris Chair of Women in AI Governance, and a member of the Support Pool of Experts in dark patterns at the European Data Protection Board. She's a former Magic Circle competition lawyer and EMEA General Counsel in listed groups, and teaches at Singapore Management University, Sciences Po and Paris II University. Marie also serves as an advisory board member at the BASIL research initiative, the Serpentine Galleries and Pickering Pierce.

Sara Vienna headshot

Sara Vienna

Chief Design Officer at Metalab

Sara leads Metalab’s Research, Strategy, and Design Department. She has two decades of design experience across TV, cosmetics, and technology. Sara was a founding member of the Tech Ladies New York community.

Why this matters

As AI becomes embedded in everyday products, the stakes for ethical, human-centered design have never been higher. Design choices in AI systems and products affect not only usability, but also fairness, transparency, and trust. From subtle UX patterns to systemic bias, product teams must navigate complex decisions that shape how users interact with—and perceive—AI.

In this panel interview webinar, Marie Potel-Saville, CEO & Co-founder at FairPatterns, and Sara Vienna, Chief Design Officer at Metalab, will explore what it takes to build AI products that are not only effective, but responsible. You’ll learn how to spot and avoid harmful “dark patterns,” integrate “fair patterns” into your user experience, and assess the business and ethical risks that come with poorly designed AI.

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