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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Generative AI can circulate values through the choices built into models and the institutions that select and deploy them—but the evidence here does not show that chatbots reliably change users’ beliefs or represent one population’s values. To understand what “promulgation” means, distinguish what a system’s makers say, what organizations do with it, and what measurable effects it has on people.
What does it mean for generative AI to promulgate values?
To promulgate a value is to give it visibility, legitimacy, or practical influence. A chatbot might do this by presenting some judgments as ordinary or preferable, or by repeatedly reflecting the priorities embedded in its design and deployment. That is a useful concern to investigate, not proof that any particular chatbot has a stable set of values or changes what users believe.
A LinkedIn post by Micah Beck characterizes a linked Communications of the ACM article as warning that chatbots may propagate ideas and values reflecting a statistically dominant point of view, even when people reasonably disagree. The ACM article itself is not available in the evidence considered here, so that formulation should be attributed to Beck’s post rather than treated as a verified quotation or full account of the article’s argument: Micah Beck’s post.
Whose values might be involved?
“AI’s values” can refer to different actors and decisions. A model provider may state ethical principles; a public institution may procure a system to meet service goals; affected communities may have their own priorities; and individual users may bring values to each interaction. Those influences should not be collapsed into a claim that a model has one coherent moral outlook.
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- Provider choices: design, training, and public-facing guidance can shape what a model produces. The sources considered here do not establish the particular effect of any training choice on generative-model outputs.
- Institutional choices: procurement, workflow design, and implementation determine where AI is used and which objectives take priority.
- Community and user interests: people affected by a system may value privacy, fairness, professional judgment, or other goals differently from providers or institutions.
Evidence about stated principles is not the same as evidence about actual practice, and neither alone demonstrates effects on users. The distinction matters especially when describing whether a system reflects a dominant point of view.
What a public-hospital study shows about AI adoption
Oostvogel, Young, and Klievink’s qualitative case study, first published online on 8 August 2026 in Public Administration, examined AI adoption in the radiology department of a Dutch academic hospital. The system was MRI workflow-optimization software intended to reduce scan times and improve image quality. The researchers used ethnographic fieldwork, interviews, and document analysis to study the period between the decision to adopt and sustained implementation. Their article is available at “Getting the Priorities Straight: Public Values in AI Adoption”.
The authors describe a recursive relationship: public values shaped how people understood and prepared for adoption, while the adoption process also affected which values received priority. In this case, innovation and efficiency were treated as instrumental values—means toward other aims—while effective and equitable MRI services were intrinsic goals. A top-down adoption decision could also shape employees’ value priorities.
The authors frame the underlying issue directly: “Technology is itself not value-free, and its adoption in public organizations requires moral judgments about or between values.” Their study supports scrutiny of organizational choices; it does not directly demonstrate how a large language model encodes or spreads values.
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What the evidence does—and does not—establish
The hospital study is a context-rich qualitative case, not a statistical estimate of how often particular value conflicts occur or a causal test of AI’s effects. Its findings concern process-optimization software in one institutional setting. The authors caution against assuming every AI adoption is radically disruptive and distinguish their case from systems that change human–machine interaction, including LLM-based systems. Its findings should not automatically be generalized to chatbots, predictive systems, or other institutions.
More broadly, AI may support efficiency and effectiveness, while also raising possible concerns involving trust, safety, privacy, responsibility, accountability, and bias. These are potential effects rather than universal outcomes; the evidence cited here does not establish a general causal effect of AI on those values. In particular, it does not show that a chatbot causes a specific change in a user’s beliefs or reliably represents the values of a population.
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Can ethics guidelines influence AI?
A 2025 scholarly article argues that repeated references to ethical norms can shape discussion around AI and may modestly influence development. It also cautions against overstating their practical impact: guidelines may work indirectly by raising awareness and prompting conversation, while voluntary corporate commitments alone are unlikely to offer sufficiently effective protection. This is an argument in the article, not a measured estimate of guidelines’ overall effects. See “AI Ethics Guidelines: Time to Include Animals”.
Ethics statements, observed adoption practices, and enforceable rules are different kinds of influence. A public statement can set expectations; institutional oversight can hold an organization to account; enforceable requirements can create obligations beyond voluntary commitments. The sources here do not quantify how much any one approach changes AI systems or outcomes.
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How to assess a claim about AI and values
When someone says that a generative AI system promotes a particular value or viewpoint, ask four questions before accepting the claim:
- Whose values? Identify whether the claim concerns the model provider, a deploying institution, an affected community, or individual users.
- How do they enter? Separate model design and training choices from procurement, workflow decisions, and public-facing guidance.
- What kind of evidence is offered? A stated principle, an observed organizational practice, and a measured effect on people or services support different conclusions.
- Who is accountable? Distinguish voluntary commitments from institutional oversight and enforceable rules, and identify who must protect the people affected.
Tradeoffs deserve explicit attention. Efficiency may conflict with privacy; standardization may constrain professional judgment; and commercial goals such as profitability or market share may not align with public obligations. The 2026 hospital study emphasizes that public organizations remain responsible for safeguarding public values when working with private companies.
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