The normalization of deviance framework — the organizational process by which safety violations become redefined as acceptable through repeated non-disaster — has preceded every major technological catastrophe of the last half-century, yet has never been systematically applied to AI development. This project investigates whether the structural conditions that produced Challenger, Three Mile Island, and the Boeing 737 MAX crashes are present in contemporary AI development organizations, and what that implies for AI safety.
About the project
This project applies the normalization of deviance (NoD) framework, developed by sociologist Diane Vaughan through her landmark study of the 1986 Challenger disaster, to contemporary AI development organizations. The central question is whether the organizational conditions that have reliably preceded major technological disasters are structurally present in AI development today.
The normalization of deviance describes the incremental process by which a practice that violates safety norms becomes, through repeated non-disaster, redefined as normal. Critically, this process does not require negligence or malice — it emerges from the ordinary dynamics of organizations under competitive pressure. Prior research has documented this pattern across oil and gas, nuclear power, aviation, healthcare, and rail industries. This project asks whether AI development organizations exhibit the same structural features: production pressure, false assurance from prior success, structural secrecy, and erosion of independent oversight.
The project has both a theoretical and an empirical dimension. On the theoretical side, the project develops a systematic mapping of the NoD framework onto the AI development landscape, drawing on the organizational sociology literature and the AI safety literature. On the empirical side, the project investigates whether observable signatures of normalization are present in the public record of AI development — including responsible scaling policy revision histories, public red-teaming disclosures, model evaluation practices, and documented instances of organizational safety culture changes at frontier AI laboratories.
The project builds on a workshop paper currently in preparation for NeurIPS 2026, which develops the theoretical argument and case study analysis. The SPAR project would extend this work by developing the empirical component, which requires systematic data collection and analysis beyond the scope of the workshop paper. Mentees with backgrounds in organizational sociology, science and technology studies, AI safety, or empirical social science methods are particularly well suited to contribute to this project.
Theory of change
The AI safety field has invested heavily in technical approaches to risk — alignment research, interpretability, and evaluation frameworks — but has paid comparatively little attention to the organizational dynamics that determine whether those technical efforts translate into safe outcomes in practice. This project addresses that gap by identifying the organizational conditions under which safety infrastructure erodes, even when individuals and institutions are acting in good faith. If the normalization of deviance is occurring in AI development organizations, understanding its mechanisms and early signatures is a prerequisite for interrupting it before a consequential failure occurs. The work contributes directly to the design of more robust safety institutions, governance frameworks, and oversight mechanisms for frontier AI development.
Your role
Mentees will function as independent researchers with guidance. After an initial onboarding period where we establish shared context on the normalization of deviance framework, the organizational sociology literature, and the AI safety landscape, each mentee will:
- Select one or more AI development organizations or domains to investigate, identifying publicly available evidence relevant to the four mechanisms identified in the framework: production pressure, false assurance from prior success, structural secrecy, and erosion of independent oversight.
- Design a systematic data collection approach drawing on publicly available sources such as responsible scaling policies and their revision histories, model cards, red-teaming disclosures, safety team organizational changes, and public statements from AI developers.
- Analyze collected evidence against the normalization of deviance framework, identifying whether and how the characteristic signatures of organizational drift are present in the public record.
- Write up findings with the goal of producing a publishable contribution, either as a standalone empirical paper or as a component of the broader research program.
I will provide supervision through regular check-ins, feedback on research design and methodology, and guidance on connecting empirical findings to the theoretical framework. Mentees should expect to drive their own projects forward between meetings and to develop their own analytical perspective on the material. This structure rewards initiative, careful reading, and the ability to reason across disciplinary boundaries — the project sits at the intersection of organizational sociology, science and technology studies, and AI safety, and mentees comfortable with that interdisciplinary space will thrive.
Prerequisites
Prerequisites:
Familiarity with the AI safety landscape, including basic knowledge of current frontier AI development organizations, evaluation practices, and governance frameworks. Comfort reading and synthesizing academic literature across disciplines, including organizational sociology, science and technology studies, and social science methodology. Strong analytical writing skills, demonstrated through prior academic writing, research papers, or policy documents. Ability to conduct systematic qualitative research, including identifying, collecting, and analyzing publicly available documentary evidence. Genuine intellectual curiosity about the intersection of organizational dynamics and AI safety — this project is not primarily technical and does not require a computer science background, but does require comfort reasoning carefully about complex sociotechnical systems. The following are not required but would be advantageous:
Prior exposure to organizational sociology or science and technology studies. Familiarity with qualitative research methods such as document analysis or case study methodology. Experience writing for academic venues in any discipline.
Application question(s)
Question 1 (250 words): The normalization of deviance framework argues that safety failures emerge from organizational dynamics rather than individual negligence, and that safety processes themselves can be captured by the deviations they were meant to prevent. Identify one concrete example from the public record of AI development — a policy revision, an organizational change, a public statement, or a documented shift in evaluation practice — that you think could be interpreted as an early signature of this dynamic. Explain your reasoning, being careful to distinguish between evidence that is consistent with normalization of deviance and evidence that would constitute proof of it.
Question 2 (200 words): This project sits at the intersection of organizational sociology and AI safety, two fields with different methodological traditions and different standards of evidence. What do you see as the primary methodological challenge in applying a qualitative organizational framework to AI development organizations, and how would you approach it?
Question 3 (link, no word limit): Please provide a link to one or more writing samples from a research or academic context. These need not be published or formally peer-reviewed. We are looking for evidence of careful analytical reasoning, precise use of evidence, and clear written argumentation across disciplinary boundaries.
About the mentor
Emilio is an independent researcher specializing in the social impacts of AI, with a focus on human-AI interaction, language model behavior, and organizational adoption of technology. Previously, he was a research manager at Columbia AI Alignment Club, where he led projects evaluating how language models exhibit human behaviors. He was also a graduate researcher in the Management Division at Columbia Business School, investigating how emerging player performance technologies are adopted in amateur competitive youth sports. He holds an MA in Media Studies and Sociology from Columbia University and a BA in Communication and Political Science from Brigham Young University–Hawaii.