trust
What It Means
Trust in AI systems is how much confidence users, customers, and stakeholders have that the AI will work as expected and help them achieve their goals. It's shaped by people's direct experiences with the system and their perceptions of its reliability, especially when outcomes are uncertain or when they're vulnerable to the AI's decisions.
Why Chief AI Officers Care
Trust directly impacts AI adoption rates, user engagement, and business outcomes - low trust means people won't use your AI systems or will override their recommendations. Trust issues can lead to regulatory scrutiny, reputational damage, and failed AI investments, while high trust enables faster scaling and competitive advantage.
Real-World Example
A bank's AI loan approval system may be technically accurate 95% of the time, but if loan officers don't understand why it makes certain decisions and have seen it approve risky loans, they'll stop trusting its recommendations and revert to manual processes, defeating the purpose of the AI investment.
Common Confusion
People often confuse trust with technical accuracy or performance metrics - an AI system can be highly accurate but still not trusted if users don't understand its decisions or have had bad experiences with it.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, trust is critical because patients and clinicians must rely on AI systems for life-critical decisions ...
Finance: In finance, trust in AI systems is critical for algorithmic trading, credit decisions, fraud detection, and robo-advisor...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"the system status in the mind of human beings based on their perception of and experience with the system; concerns the attitude that a person or technology will help achieve specific goals in a situation characterized by uncertainty and vulnerability."Source: DOD_TEVV
"degree to which a user or other stakeholder has confidence that a product or system will behave as intended"Source: aime_measurement_2022, citing ISO/IEC TR 24029-1
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