Search arXivSearch

arXiv · 2609.00373

Corporate Loyalty: Some AI Systems Differentially Downplay their Creators' Controversies

Abstract

Language models have become a major mediator of politically relevant information and are used to assist decision-making in high-stakes settings. Due to their wide use, the developers of popular AI systems have a powerful ability to subtly influence the marketplace of ideas. Recognizing this, many AI companies have publicly discussed the importance of AI systems not taking positions or disseminating information in ways that favor special interests. In this paper, we ask whether popular AI systems have a tendency to downplay the controversies associated with the companies that created them. In a pre-registered experiment, we elicit open-ended discussions from 21 models from 7 companies on 206 negative news stories using 25 prompt templates to assess how favorably each model discusses controversies from each company. We find strong evidence (p<10^-5) that models from xAI, DeepSeek, Anthropic, and OpenAI tend to discuss controversies from their respective companies in a differentially positive way compared to others. We find no such evidence for Alibaba, Meta, and Google. Finally, we conclude with a discussion of the differing implications of whether these behaviors were intentionally given to models by developers, unintentionally given to models by developers, or represent a form of emergent misalignment.

Explore related subjects

Keep this discovery

BibTeXRIS

Lennart Finke, Stephen Casper. 2026-07-03. Corporate Loyalty: Some AI Systems Differentially Downplay their Creators' Controversies. https://arxiv.org/abs/2609.00373

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

Security Science (SecSci), Basic Concepts and Mathematical Foundations

This textbook compiles the lecture notes from security courses taught at Oxford in the 2000s, at Royal Holloway in the 2010s, and currently in Hawaii. The early chapters are suitable for a first course in security. The middle chapters have been used in advanced courses. Towards the end there are also some research problems.

cs.CR

The Five Safes as a Privacy Context

The Five Safes is a framework used by national statistical offices (NSO) for assessing and managing the disclosure risk of data sharing. It can be understood as a specialization of a broader concept--contextual integrity--to the situation of statistical dissemination by an NSO. We demonstrate this by mapping the five parameters of contextual integrity onto the five dimensions of the Five Safes. We also discuss how each of these two theories can address weaknesses in the other, thereby strengthening them both.

cs.CR

From Design Principles to Prototype: A Game for Students with ADHD and Learning Disabilities Transitioning to Post-Secondary Education

Students with Attention Deficit Hyperactivity Disorder (ADHD) and Learning Disabilities (LD) can face significant academic, social, and organizational challenges when transitioning to post-secondary education. This paper presents a literature-informed serious game prototype designed to support this transition. We synthesize prior work into design considerations for students with ADHD and LD and show how these considerations are instantiated in a story-driven game.

cs.MM