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Much of the current debate on AI ethics focuses on normative principles such as fairness, transparency, and accountability. While these principles remain essential, they often do not sufficiently explain why ethically problematic outcomes persist under competitive market conditions. This paper addresses that gap by applying Karl Homann\u2019s institutional economic ethics to the governance of large language models. From this perspective, ethical deficits in AI development are not merely the result of individual failures of responsibility, but are also shaped by institutional incentive structures that reward speed, scale, proprietary control, and strategic secrecy. The paper analyses three central ethical challenges in LLM development: linguistic and cultural asymmetries, transparency and accountability deficits, and contested practices of data governance and intellectual property. It then argues that the familiar opposition between open and closed AI systems is conceptually and institutionally inadequate. In response, the paper develops the concept of partially open AI governance, understood as a differentiated arrangement of access, disclosure, and oversight across distinct layers of AI systems. Such an approach offers a more realistic way of aligning innovation incentives with ethical and public objectives in the governance of large language models.<\/jats:p>","DOI":"10.1007\/s11948-026-00620-0","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T11:33:56Z","timestamp":1786707236000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Language Models as a Challenge for Business Ethics \u2013 A Partially Open-Source Approach"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0385-2829","authenticated-orcid":false,"given":"Patrick","family":"Hedfeld","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"620_CR1","doi-asserted-by":"publisher","unstructured":"Abiri, G. (2024). Public constitutional AI. 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This article does not contain any studies with human participants or animals performed by any of the authors.","order":1,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent to Publish","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This manuscript benefited from AI-assisted language editing (ChatGPT and Google Translate), limited to copy editing. All substantive content is the responsibility of the authors.","order":3,"name":"Ethics","label":"Use of AI Tools","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":4,"name":"Ethics","label":"Competing Interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"40"}}