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.

The importance of trust in AI systems is growing, as users need assurance that these systems will behave reliably and consistently.

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

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

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 challenge of ensuring AI systems do not exhibit undesired behaviors is more complex than simply achieving high performance metrics.

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 current environment fosters distrust among humans, leading to a paradox where people may trust AI systems more than their fellow humans, despite the potential dangers of AI. We are accelerating technology at the cost of transparency and regulation, which are essential checks and balances we need right now.