Paradigms of AI Evaluation: Mapping Goals, Methodologies and Culture

TL;DR

This paper surveys the diverse evaluation paradigms in AI, clarifying their goals, methods, and cultures to foster better understanding and collaboration across the field.

Contribution

It identifies six main AI evaluation paradigms, characterizes their key features, and highlights gaps to promote cross-paradigm integration and progress.

Findings

Six main evaluation paradigms identified

Characterization of goals, methodologies, and cultures for each paradigm

Identification of gaps and future research directions

Abstract

Research in AI evaluation has grown increasingly complex and multidisciplinary, attracting researchers with diverse backgrounds and objectives. As a result, divergent evaluation paradigms have emerged, often developing in isolation, adopting conflicting terminologies, and overlooking each other's contributions. This fragmentation has led to insular research trajectories and communication barriers both among different paradigms and with the general public, contributing to unmet expectations for deployed AI systems. To help bridge this insularity, in this paper we survey recent work in the AI evaluation landscape and identify six main paradigms. We characterise major recent contributions within each paradigm across key dimensions related to their goals, methodologies and research cultures. By clarifying the unique combination of questions and approaches associated with each paradigm, we aim to increase awareness of the breadth of current evaluation approaches and foster cross-pollination between different paradigms. We also identify potential gaps in the field to inspire future research directions.