What is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety
Not on the map. Why?
Abstract (arXiv)
This work examines how leading generative artificial intelligence companies construct and communicate the concept of "safety" through public-facing documents. Drawing on critical discourse analysis, we analyze a corpus of corporate safety-related statements to explicate how authority, responsibility, and legitimacy are discursively established. These discursive strategies consolidate legitimacy for corporate actors, normalize safety as an experimental and anticipatory practice, and push a perceived participatory agenda toward safe technologies. We argue that uncritical uptake of these discourses risks reproducing corporate priorities and constraining alternative approaches to governance and design. The contribution of this work is twofold: first, to situate safety as a sociotechnical discourse that warrants critical examination; second, to caution human-computer interaction scholars against legitimizing corporate framings, instead foregrounding accountability, equity, and justice. By interrogating safety discourses as artifacts of power, this paper advances a critical agenda for human-computer interaction scholarship on artificial intelligence.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 36 of its 150 references that are in the map. First posted on arXiv 26 Jan 2026.
Builds on
36 of 150 references.
References · 36
- 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,429
- 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
- 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 157
- 2022Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reductioncited by 138
- 2022The Fallacy of AI Functionalitycited by 88
- 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 87
- 2019Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptionscited by 81
- 2025Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providerscited by 69
- 2024"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trustcited by 67
- 2024The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationshipscited by 62
- 2025Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluationcited by 60
- 2025Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistenciescited by 39
24 more
- 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
- 2022Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethicscited by 28
- 2023Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairnesscited by 26
- 2023A Systematic Review on Fostering Appropriate Trust in Human-AI Interactioncited by 21
- 2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Reviewcited by 19
- 2024A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harmscited by 19
- 2024Epistemic Injustice in Generative AIcited by 16
- 2025WeAudit: Scaffolding User Auditors and AI Practitioners in Auditing Generative AIcited by 14
- 2024Generative AI Literacy: Twelve Defining Competenciescited by 13
- 2024A Taxonomy of Systemic Risks from General-Purpose AIcited by 12
- 2022The Algorithmic Imprintcited by 10
- 2025"Ownership, Not Just Happy Talk": Co-Designing a Participatory Large Language Model for Journalismcited by 10
- 2023An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactionscited by 9
- 2024Benchmark Early and Red Team Often: A Framework for Assessing and Managing Dual-Use Hazards of AI Foundation Modelscited by 9
- 2019Human-Computer Insurrection: Notes on an Anarchist HCIcited by 7
- 2025Children's Mental Models of AI Reasoning: Implications for AI Literacy Educationcited by 5
- 2025AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Reviewcited by 4
- 2024Addressing Social Misattributions of Large Language Models: An HCXAI-based Approachcited by 3
- 2024Structural Interventions and the Dynamics of Inequalitycited by 3
- 2024Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variabilitycited by 3
- 2025Let's Talk Futures: A Literature Review of HCI's Future-Orientationcited by 2
- 2025What Is AI Safety? What Do We Want It to Be?cited by 2
- 2023Othered, Silenced and Scapegoated: Understanding the Situated Security of Marginalised Populations in Lebanoncited by 1
- 2025What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunitycited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.