Incorporating AI into workflows presents challenges, particularly in building trust with users and ensuring that AI helps them progress rather than causing frustration.

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.

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

AI represents a shift towards productivity, automating knowledge activities, while also raising concerns about security and trust in digital interactions.

The introduction of AI could exacerbate existing inequalities, as those with ill intent may benefit more from AI advancements than those who are pro-social and wealth-creating.

The stakes are higher when AI has access to sensitive information and systems, making safety a critical concern.

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.