A Landscape of Trustworthy AI Frameworks and Metrics

As Artificial Intelligence Systems (AIS) become ubiquitous, the need for standardized frameworks and quantifiable metrics to evaluate their trustworthiness has become more urgent, particularly in critical domains such as medicine, finance, and cybersecurity. In literature, there are number of frameworks presented for quantifying trust within AI across different domains, which often times do not use a unified vocabulary. This study provides a recent review of existing trustworthy AI (TAI) frameworks and associated metrics for assessing AI trustworthiness. We examine peer-reviewed publications from 2020 to 2025 to identify 8 TAI frameworks and extracted a total of 138 metrics for evaluating trustworthiness in AI-assisted systems across different aspects of trust. The conceptual elements of each framework are subsequently analyzed and mapped to the National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF), often referred to as NIST’s TAI framework. The NIST TAI framework identifies seven core characteristics of trustworthy AI systems, which we adopt as the TAI pillars in this paper for framework unification. We highlight commonalities and divergences across the reviewed frameworks based on their proposed pillars and metrics. Finally, all metrics were placed in an Excel spreadsheet sorted by NIST pillars for reproducibility.