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Principles and practices ensuring AI systems are lawful, ethical, robust, and aligned with societal and regulatory expectations.
DESCRIPTION
Trustworthy AI encompasses guidelines and controls that make AI reliable, fair, transparent, secure, and accountable. Frameworks from the EU, NIST, OECD, and ISO codify requirements for lawfulness, ethics, robustness, privacy, and human oversight. Organizations implement governance processes, risk assessments, documentation (e.g., model cards), and monitoring to ensure trustworthiness across the AI lifecycle. It is essential for regulatory compliance and public acceptance.
ALTERNATIVE DEFINITIONS
European Commission
To be trustworthy, AI systems should be lawful, ethical and robust, while respecting fundamental rights and applicable regulation.
Defines three core components: lawful, ethical, robust.
NIST
The AI RMF aims to help achieve trustworthy AI systems that are valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed.
Enumerates characteristics of trustworthiness and risk-focused approach.
OECD
OECD AI Principles promote innovative and trustworthy AI that respects human rights and democratic values, including transparency, accountability, robustness, security and safety.
Connects trustworthiness to values-based international principles.
ISO/IEC TR 24028
This Technical Report provides an overview of trustworthiness in artificial intelligence, including its characteristics, enabling methods and evaluation measures.
Focuses on standardized overview and evaluation measures.
RELATED TERMS
AI Risk Management Explainable AI Fairness Bias Mitigation Model Cards Transparency Accountability AI Act
COMPETING TERMS
KNOWLEDGE AREAS
AI Governance Data Privacy & Compliance Data Security Data Management
TARGET AUDIENCE
Chief AI Officer Chief Data Officer Data Governance Manager Data Privacy Officer Chief Information Security Officer AI Product Manager AI Engineer MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Artificial intelligence ethics
- NIST - US framework for trustworthy AI
- European Commission - EU ethics guidelines
- OECD - International AI principles
TOP QUESTIONS
What is Trustworthy AI?
AI that is lawful, ethical, and robust, with transparency, fairness, and accountability built into its design and operations.
Category: Definition
Why is Trustworthy AI crucial for organizations?
It ensures compliance, mitigates risk, and builds user trust, enabling sustainable AI adoption.
Category: Relevancy
What frameworks guide Trustworthy AI?
EU Ethics Guidelines, NIST AI RMF, OECD AI Principles, and ISO/IEC TR 24028 provide comprehensive guidance.
Category: Overview
How is Trustworthy AI implemented?
Through governance, risk assessments, documentation (model cards), monitoring, audits, and human oversight.
Category: Management
What are common mistakes in Trustworthy AI programs?
Treating it as a one-off compliance exercise rather than continuous lifecycle management.
Category: Mistakes
What risks arise when ignoring Trustworthy AI?
Legal penalties, reputational damage, biased outcomes, and security vulnerabilities.
Category: Risks
How does Trustworthy AI differ from AI Governance?
Governance structures decision rights; trustworthiness focuses on ethical and technical qualities of AI systems.
Category: Alternatives
What is a hot topic around Trustworthy AI?
Operationalizing fairness and explainability for foundation models under evolving regulations like the EU AI Act.
Category: Topic
Where should organizations start with Trustworthy AI?
Define an AI governance framework, conduct risk assessments, and establish lifecycle controls with measurable metrics.
Category: Requirements