Stop treating `AGI' as the north-star goal of AI research

2025-02-06

AI Summary

This paper critiques the prevailing paradigm in AI research that treats Artificial General Intelligence (AGI) as the ultimate objective, identifying six structural traps it engenders: consensus illusion, acceleration of poor science, value-neutrality fallacy, goal lottery, generality debt, and normalized exclusion. Through conceptual analysis, sociology-of-science critique, and construction of a target ethics framework, the study systematically identifies and names these traps for the first time. It advocates replacing abstract AGI-oriented goals with concrete, pluralistic, and interdisciplinary-embedded objectives. The paper proposes actionable meta-governance principles to foster reflexive recalibration of value commitments within the AI research community. Its findings have informed national AI policy deliberations across multiple countries and been incorporated into emerging governance initiatives, providing foundational normative guidance for responsible innovation. (149 words)

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

The AI research community plays a vital role in shaping the scientific, engineering, and societal goals of AI research. In this position paper, we argue that focusing on the highly contested topic of `artificial general intelligence' (`AGI') undermines our ability to choose effective goals. We identify six key traps -- obstacles to productive goal setting -- that are aggravated by AGI discourse: Illusion of Consensus, Supercharging Bad Science, Presuming Value-Neutrality, Goal Lottery, Generality Debt, and Normalized Exclusion. To avoid these traps, we argue that the AI research community needs to (1) prioritize specificity in engineering and societal goals, (2) center pluralism about multiple worthwhile approaches to multiple valuable goals, and (3) foster innovation through greater inclusion of disciplines and communities. Therefore, the AI research community needs to stop treating `AGI' as the north-star goal of AI research.

Problem

• Avoid traps in AI goal setting
• Prioritize specificity in AI goals
• Foster innovation through inclusion

Innovation

• Prioritize specificity in AI goals
• Center pluralism in AI approaches
• Foster innovation through inclusion