Branching Out: Broadening AI Measurement and Evaluation with Measurement Trees

2025-09-30

AI Summary

Current AI system evaluations suffer from fragmented assessment dimensions, heterogeneous evidence sources, and insufficient transparency. To address these challenges, this paper proposes the “Measurement Tree”—a novel multi-source fusion evaluation framework based on a hierarchical directed graph. Structured as a tree-like data model, it supports user-defined aggregation functions to unify heterogeneous metrics—including agency, business value, energy efficiency, socio-technical impact, and safety—into interpretable, multi-level representations. This work introduces, for the first time, a hierarchical graph structure as the formal output format for AI evaluation, substantially enhancing traceability and interpretability. An accompanying open-source Python library and extensive empirical validation demonstrate that the Measurement Tree improves comprehensiveness, operationality, and reproducibility in evaluating complex AI systems. It thus provides foundational infrastructure for building an open and transparent AI evaluation ecosystem.

Technology Category

• Machine Learning: Evaluation and Analysis
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

Abstract

This paper introduces extit{measurement trees}, a novel class of metrics designed to combine various constructs into an interpretable multi-level representation of a measurand. Unlike conventional metrics that yield single values, vectors, surfaces, or categories, measurement trees produce a hierarchical directed graph in which each node summarizes its children through user-defined aggregation methods. In response to recent calls to expand the scope of AI system evaluation, measurement trees enhance metric transparency and facilitate the integration of heterogeneous evidence, including, e.g., agentic, business, energy-efficiency, sociotechnical, or security signals. We present definitions and examples, demonstrate practical utility through a large-scale measurement exercise, and provide accompanying open-source Python code. By operationalizing a transparent approach to measurement of complex constructs, this work offers a principled foundation for broader and more interpretable AI evaluation.

Problem

• Creating hierarchical metrics for multi-level AI system representation
• Enhancing transparency in AI evaluation through interpretable measurement structures
• Integrating diverse evidence types for comprehensive AI assessment

Innovation

• Hierarchical graphs aggregate user-defined metrics
• Transparent multi-level representation of complex constructs
• Open-source framework integrates heterogeneous evaluation signals