Societal AI Research Has Become Less Interdisciplinary

TL;DR

This study analyzes over 100,000 AI papers from 2014 to 2024, revealing a decline in interdisciplinary research's role in societal AI concerns, with technical teams increasingly addressing societal issues independently.

Contribution

It provides empirical evidence that computer science-only teams are now leading societal AI research, challenging assumptions about interdisciplinary collaboration's role.

Findings

Interdisciplinary teams are less dominant in societal AI research.

Technical teams are increasingly integrating societal concerns.

Most societal AI research is now conducted by computer science-only teams.

Abstract

As artificial intelligence (AI) systems become deeply embedded in everyday life, calls to align AI development with ethical and societal values have intensified. Interdisciplinary collaboration is often championed as a key pathway for fostering such engagement. Yet it remains unclear whether interdisciplinary research teams are actually leading this shift in practice. This study analyzes over 100,000 AI-related papers published on ArXiv between 2014 and 2024 to examine how ethical values and societal concerns are integrated into technical AI research. We develop a classifier to identify societal content and measure the extent to which research papers express these considerations. We find a striking shift: while interdisciplinary teams remain more likely to produce societally-oriented research, computer science-only teams now account for a growing share of the field's overall societal output. These teams are increasingly integrating societal concerns into their papers and tackling a wide range of domains - from fairness and safety to healthcare and misinformation. These findings challenge common assumptions about the drivers of societal AI and raise important questions. First, what are the implications for emerging understandings of AI safety and governance if most societally-oriented research is being undertaken by exclusively technical teams? Second, for scholars in the social sciences and humanities: in a technical field increasingly responsive to societal demands, what distinctive perspectives can we still offer to help shape the future of AI?