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Review examines how to measure and govern trust in AI systems across industries

Study title: Evaluating Trustworthiness in AI: Risks, Metrics, and Applications Across Industries

As of its July 2025 publication, this peer-reviewed review examined how trust in AI systems can be systematically measured, analyzing frameworks including the NIST AI Risk Management Framework, the AI Trust Framework and Maturity Model, and ISO/IEC standards around fairness, transparency, privacy and security.

Read the study abstract

Ensuring the trustworthiness of artificial intelligence (AI) systems is critical as they become increasingly integrated into domains like healthcare, finance, and public administration. This paper explores frameworks and metrics for evaluating AI trustworthiness, focusing on key principles such as fairness, transparency, privacy, and security. This study is guided by two central questions: how can trust in AI systems be systematically measured across the AI lifecycle, and what are the trade-offs involved when op…

By the Truvace desk1 min read1 sourceEvidence: Emerging · 30/100 — Commentary, opinion, or narrative review · 1 independent sourcePeer-reviewedTRV-2026-0499· cite & verify
Evaluating Trustworthiness in AI: Risks, Metrics, and Applications Across Industries
A head start on assurance: proceedings of an invitational workshop on Information Technology (IT) Assurance and Trustworthiness, March 21-23, 1994, George Washington Inn, Williamsburg, Virginia by Abrams, Marshall D. Toth, Patricia R.. Public domain

In brief

It matters because integration into healthcare, finance, public administration and autonomous systems raises concrete risks to reliability and bias, and the paper shows that improving one trust dimension can degrade another, leaving open how adaptive governance can keep pace with advancing AI capabilities.

Main points

  1. Paper examines NIST AI Risk Management Framework, AI Trust Framework and Maturity Model, and ISO/IEC standards to measure trust across the AI lifecycle.
  2. Focuses on principles of fairness, transparency, privacy and security and metrics for system reliability, bias mitigation and model explainability.
  3. Includes comparative analysis of standards and real-world case studies in healthcare, financial services and autonomous systems.
  4. Findings emphasize interdisciplinary collaboration for robust AI governance and alignment with regulatory requirements and societal expectations.

The problem

AI systems face major risks across lifecycle stages including reliability failures and bias, and optimizing trustworthiness forces trade-offs such as fairness versus efficiency or privacy versus transparency.

The rundown

The study is guided by how trust can be systematically measured across the AI lifecycle and what trade-offs arise when optimizing different dimensions, bridging theoretical insights with practical applications.

By July 2025 it reviews existing standards, identifies risks at each lifecycle stage, and uses case studies to show how metrics are applied, concluding that adaptive, interdisciplinary governance is needed to meet regulatory and societal expectations.

Sources

  1. Peer-reviewedElectronics2025-07-04

The debate