Trustworthy AI refers to systems that consistently adhere to key values—such as safety, fairness, reliability, transparency, and accountability—throughout their design, deployment, and use. In AI assurance, trustworthiness is the ultimate objective: it reflects the cumulative outcome of rigorous testing, governance, oversight, and ethical alignment.

Trustworthy AI is not a single feature but a holistic outcome. A trustworthy system is:

• Technically sound and secure

• Respectful of privacy and human rights

• Governed by clear responsibilities and controls

• Transparent in its purpose, logic, and limitations

• Aligned with its intended use and societal context

Achieving trustworthy AI requires a multi-layered assurance approach:

• Testing for performance, robustness, fairness, and explainability

• Documentation that clarifies system behaviour and limitations

• Oversight mechanisms for accountability and human intervention

• Monitoring and adaptation over time to prevent drift or misuse

Assurance providers evaluate trustworthiness through structured frameworks (e.g., NIST AI RMF, ISO/IEC 24028) and issue certifications, audit reports, or conformance statements. These help organisations demonstrate that they have met acceptable thresholds for safety and integrity.

In defence, healthcare, and public sector deployments, trustworthiness is critical to adoption. Without demonstrated integrity, AI systems may be rejected by operators, constrained by regulators, or challenged by the public.