Trustworthy AI
AI systems that are reliable, fair, transparent, private, secure, and accountable. Trustworthy AI meets both technical standards and ethical requirements for safe deployment.
Why It Matters
Trust is the barrier to AI adoption. Organizations and users will not rely on AI they do not trust, and trust must be earned through demonstrated trustworthiness.
Example
An AI system that passes bias audits, provides explanations for decisions, protects user privacy, handles edge cases gracefully, and has clear accountability structures.
Think of it like...
Like trust in aviation — you trust planes because of rigorous engineering, testing, regulation, and transparency. AI needs the same systematic approach to earn trust.
Related Terms
Responsible AI
An approach to developing and deploying AI that prioritizes ethical considerations, fairness, transparency, accountability, and societal benefit throughout the entire AI lifecycle.
AI Ethics
The study of moral principles and values that should guide the development and deployment of AI systems. It addresses questions of fairness, accountability, transparency, privacy, and the societal impact of AI.
Fairness
The principle that AI systems should treat all individuals and groups equitably and not produce discriminatory outcomes. Multiple mathematical definitions of fairness exist, and they can sometimes conflict.
Transparency
The principle that AI systems should operate in a way that allows stakeholders to understand how they work, what data they use, and how decisions are made.
Accountability
The principle that there must be clear responsibility and liability for AI system decisions and their outcomes. Someone must be answerable when AI causes harm.
AI Safety
The research field focused on ensuring AI systems operate reliably, predictably, and without causing unintended harm. It spans from technical robustness to long-term existential risk concerns.