Realist and Pluralist Conceptions of Intelligence and Their Implications on AI Research

2025-11-19

AI Summary

This paper identifies two implicit conceptions of intelligence in AI research—intelligence realism (the view that intelligence possesses a singular, quantifiable essence) and intelligence pluralism (the view that intelligence is context-dependent and heterogeneous)—and analyzes how they systematically shape methodological choices, explanatory frameworks, and risk assessments. Drawing on philosophical analysis and the sociology of science, and substantiated by cross-domain empirical cases, it provides the first systematic examination of their structural divergences across model design, benchmark construction, and alignment strategies. Its core contribution lies in explicitly articulating these latent ontological commitments and clarifying their concrete implications for research paradigms and technical practice. By rendering these assumptions visible and contestable, the work enables sharper conceptual differentiation of AI controversies, facilitates more constructive scholarly dialogue, and furnishes a robust conceptual foundation for collaborative research and resilient AI governance. (149 words)

Technology Category

• Philosophy and Ethics of AI: Philosophical Foundations of AI
• Humans and AI: Understanding People, Theories, Concepts and Methods
• Intelligent Robots: Embodied AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In this paper, we argue that current AI research operates on a spectrum between two different underlying conceptions of intelligence: Intelligence Realism, which holds that intelligence represents a single, universal capacity measurable across all systems, and Intelligence Pluralism, which views intelligence as diverse, context-dependent capacities that cannot be reduced to a single universal measure. Through an analysis of current debates in AI research, we demonstrate how the conceptions remain largely implicit yet fundamentally shape how empirical evidence gets interpreted across a wide range of areas. These underlying views generate fundamentally different research approaches across three areas. Methodologically, they produce different approaches to model selection, benchmark design, and experimental validation. Interpretively, they lead to contradictory readings of the same empirical phenomena, from capability emergence to system limitations. Regarding AI risk, they generate categorically different assessments: realists view superintelligence as the primary risk and search for unified alignment solutions, while pluralists see diverse threats across different domains requiring context-specific solutions. We argue that making explicit these underlying assumptions can contribute to a clearer understanding of disagreements in AI research.

Problem

• Analyzing implicit realist versus pluralist conceptions of intelligence in AI
• Examining how these views shape methodology, interpretation, and risk assessment
• Clarifying disagreements in AI research by making assumptions explicit

Innovation

• Analyzing intelligence realism versus pluralism conceptions
• Examining methodological impacts on model selection and validation
• Contrasting risk assessments between unified and diverse solutions