Mind the Gap! Pathways Towards Unifying AI Safety and Ethics Research

TL;DR

This paper analyzes the structural divide between AI safety and ethics research, revealing limited cross-disciplinary collaboration and emphasizing the need for integrated approaches to develop AI systems that are both safe and just.

Contribution

It provides a large-scale bibliometric analysis highlighting the institutional and conceptual split between AI safety and ethics communities, and advocates for integrated research strategies.

Findings

Over 80% of collaborations occur within either safety or ethics communities.

A small number of bridging papers account for most cross-field links.

Removing key bridging papers increases segregation, showing reliance on few actors for cross-disciplinary exchange.

Abstract

While much research in artificial intelligence (AI) has focused on scaling capabilities, the accelerating pace of development makes countervailing work on producing harmless, "aligned" systems increasingly urgent. Yet research on alignment has diverged along two largely parallel tracks: safety--centered on scaled intelligence, deceptive or scheming behaviors, and existential risk--and ethics--focused on present harms, the reproduction of social bias, and flaws in production pipelines. Although both communities warn of insufficient investment in alignment, they disagree on what alignment means or ought to mean. As a result, their efforts have evolved in relative isolation, shaped by distinct methodologies, institutional homes, and disciplinary genealogies. We present a large-scale, quantitative study showing the structural split between AI safety and AI ethics. Using a bibliometric and co-authorship network analysis of 6,442 papers from twelve major ML and NLP conferences (2020-2025), we find that over 80% of collaborations occur within either the safety or ethics communities, and cross-field connectivity is highly concentrated: roughly 5% of papers account for more than 85% of bridging links. Removing a small number of these brokers sharply increases segregation, indicating that cross-disciplinary exchange depends on a handful of actors rather than broad, distributed collaboration. These results show that the safety-ethics divide is not only conceptual but institutional, with implications for research agendas, policy, and venues. We argue that integrating technical safety work with normative ethics--via shared benchmarks, cross-institutional venues, and mixed-method methodologies--is essential for building AI systems that are both robust and just.