Trust in AI systems is becoming more critical than raw performance, as users need to feel confident in the reliability and consistency of these systems.

Trust in AI systems involves verifying their reliability and consistency, which is crucial for user acceptance and enterprise success.

Trust in AI requires more than just improved accuracy metrics; it involves understanding the nuances of human trust and ensuring that AI technology is developed with user needs in mind.

The behavior of AI systems is not just about what they produce but also how they produce it, which can significantly impact user trust.

The reliability of AI systems is a major concern as we transition from consumer applications to mission-critical systems like healthcare and defense.

We need to manage transparency and control while using AI, ensuring users can trust the AI's actions on their behalf.

The main issue preventing people from gaining value from AI systems isn't performance optimization; it's the ability to confidently trust these systems.

As AI becomes more integrated into daily life, there will be a growing need to ensure that relationships with AI are healthy, emphasizing the importance of understanding user behaviors and values.

The introduction of AI challenges the foundational trust in institutions, as people may struggle to differentiate between human and AI interactions, impacting their trust in data security.