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

TL;DR

This paper explores how two different underlying conceptions of intelligence—realist and pluralist—shape AI research approaches, interpretations, and risk assessments, highlighting the importance of making these assumptions explicit.

Contribution

It analyzes the influence of implicit intelligence conceptions on AI research practices and advocates for explicit acknowledgment to improve clarity and progress.

Findings

Different conceptions lead to distinct research methodologies.

Interpretations of empirical data vary based on underlying intelligence views.

Risk assessments differ significantly between realist and pluralist perspectives.

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.