When AI gets it wrong: Designing for trust and recovery
In this session we’ll explore how to design AI-powered experiences that remain resilient when things go wrong.
Drawing from real-world experiences, we’ll discuss the importance of preparing for AI failures, maintaining user trust through transparency and empathy, and providing practical strategies for creating effective fallback solutions. You’ll leave with actionable insights to ensure AI systems recover gracefully and continue to empower users even when the technology falls short.
In this session, you will:
- Learn about common AI failures and their impact on user trust.
- Discover strategies for designing transparent and empathetic AI experiences.
- Explore practical fallback solutions to help users recover from AI errors.
- Gain insights from real-world examples of AI resilience and trust-building.
- Leave with actionable techniques to improve AI system reliability and user confidence.
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