Add AI governance blog post on RSE Day B&B 2026

_posts/2026-08-25-ai-governance.md

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+---
+title: "AI for Research: Governance and Infrastructure — RSE Day 2026"
+layout: post
+author: "Carolin Odebrecht, Stephan Druskat, Johannes Schäffer"
+menulang: en
+---
+
+- *DOI: [10.5281/zenodo.22076822](https://doi.org/10.5281/zenodo.22076822)*
+
+The disruptive impact of generative Artificial Intelligence (GenAI)
+systems and methods concerns all research areas and disciplines, as well
+as many roles within research, albeit in different ways. In general,
+research conducted using AI tools introduces new responsibilities,
+relies on new social and technical infrastructure, and requires new
+skills and competencies.
+
+<!--more-->
+
+#### Responsibilities
+
+Research conducted using GenAI requires stakeholders to evaluate new —
+and re-evaluate existing — requirements on research ethics and
+integrity, legal requirements and IT security. Where research software
+and data outputs become (critical) research infrastructure and related
+services are being provided, researchers may become accountable as
+service providers, in addition to their responsibilities as software
+developers and data creators. Despite this, roles such as Research
+Software Engineers are often overlooked in discussions around
+responsibility profiles, as well as requirements for regulation and
+(research) infrastructure, in the context of GenAI in research. This
+means that the scholars who actually develop, adopt or fine-tune such
+systems do not get to share their first-hand experience, provide
+practical insights and influence the policies that will govern their
+work in this area. The workshop "AI Research Governance. Responsibility,
+regulation and guidelines in context of research developments",
+conducted by Carolin Odebrecht at the first *Research Software Day
+Berlin & Brandenburg* \[[7](#rseday)\], therefore
+aimed to open discussions around governance of GenAI in research to
+research software engineers, whose specific expertise makes them an
+important part of the social infrastructure required for GenAI in
+research.
+
+#### Social infrastructure
+
+Just like other computational work, the adoption of GenAI in and for
+research needs to be cross-functional and inter-disciplinary, and
+requires the close collaboration of individual research teams with many
+stakeholders, such as other research groups, computing centers,
+libraries and central RSE groups, all of which may be located within a
+team's organizational unit or institution, or outside of it. These
+collaborations need to be coordinated and often formalized. In contrast
+to well- or better-established scenarios such as research data, research
+software, and the collaboration on and sharing or provision of these
+digital research outputs, there are still only few experiences and good
+practices established for collaboration between stakeholders on
+GenAI-related activities, policies, ethics and strategies.
+
+#### Technical infrastructure
+
+Similarly, the design, development and use of technical infrastructure,
+such as hardware, AI-as-a-Service, and computational capacities for
+pre-training, fine-tuning, instruction, inference, etc., requires new
+models for collaboration and identification of responsibilities. These
+need to be developed in academic organizations with the participation of
+researchers. Some fundamental questions in this area include: *Which
+technical infrastructure is needed for our research life cycles?* *Can
+we build on existing internal or external infrastructure?* *How do we
+design the FAIR and CAREful access and use of technical infrastructure?*
+*How do we measure, and account for, the cost and ecological impact of
+GenAI?*
+
+An AI governance therefore should cover the different social and
+technical infrastructures. In general, AI governance is defined commonly
+as a
+
+> \[...\] a system of rules, practices, processes, and technological
+> tools that are employed to ensure an organization's use of AI
+> technologies aligns with the organization's strategies, objectives,
+> and values; fulfills legal requirements; and meets principles of
+> ethical AI followed by the
+> organization. \[[9, p. 604](#matti)\]
+
+Research organizations need to develop and implement a governance
+framework that clarifies the responsibilities and accountability with
+regard to the adoption and use of GenAI, and provides guidelines and
+guardrails with respect to the technical and social infrastructure
+required to support it. This framework must be operationalized through,
+e.g., guidelines and policies targeting all relevant roles, job
+descriptions and staff planning in research and administration, and IT
+infrastructure strategies that safeguard independence from proprietary
+solutions, avoid vendor lock-in, and support digital sovereignty in
+research. Importantly, the development of governance frameworks should
+be participatory, in that the process must include all relevant users,
+producers, providers and other stakeholders, including RSEs.
+
+<figure id="fig1" data-latex-placement="!ht">
+<embed src="/assets/img/blog/2026/2026-08-25-ai-governance-fig1.png" style="width:80.0%" />
+<figcaption><p><b>Figure 1:</b> Focal points for AI Governance in research contexts; purple:
+research cycles and workflows; yellow: people; pink: organisational
+infrastructure; green: organisation with goals and existing governance;
+petrol: external regulation.</p></figcaption>
+</figure>
+
+AI governance systems should therefore:
+
+1.  inform about relevant aspects in terms of and ethical, legal and
+    research integrity requirements, as well as IT security;
+
+2.  establish references to stages and objects of research life cycles
+    (and/or teaching) across disciplines and roles;
+
+3.  define the scope of accountability and responsibilities of
+    researchers, digital research technical professionals (dRTPs), other
+    staff, and the organization as such;
+
+4.  define and implement corresponding social and technical (research)
+    infrastructures that supports researchers with regard to points
+    (1)–(3).
+
+# Raising awareness and fostering collaboration
+
+To raise awareness of the importance of AI governance for research, and
+the lack of inclusion and participation of RSEs and other dRTPs in their
+development and implementation, Carolin Odebrecht ran the workshop "AI
+Research Governance. Responsibility, regulation and guidelines in
+context of research developments" at the *Research Software Day Berlin &
+Brandenburg 2026* on 3 June 2026.
+
+Participation from RSEs and researchers from biology, computer science,
+physics, chemistry, digital humanities, earth sciences, economics,
+sociology, life sciences, as well as dRTPs from museums and further
+interdisciplinary backgrounds clearly showed the cross-disciplinary
+relevance of the topic. For introduction, Carolin Odebrecht had prepared
+an input on (AI) governance that served as the basis for our discussion
+throughout the workshop. The session was enriched with interactive
+elements using a feedback app (Particify \[[1](#particify)\]).
+
+As not everyone in the room had a definition of the term 'governance' to
+hand, the introductory presentation began with a definition: Governance
+is the framework by which an organization is controlled, and how
+decisions are being made within the organization \[[2](#benz)\].
+Importantly for the context of the work presented in this blog post,
+governance is also a tool to transfer external requirements — such as
+laid out by law or funding requirements - into an organization's
+internal context, making them actionable.
+
+Using Particify, we discussed our (the participants') backgrounds and
+contexts — especially the roles we hold in our respective organizations
+and whether we were aware of guidelines for AI use. As AI guidelines are
+currently developed at many organisations, only half of the participants
+stated that they are aware of an AI guideline. Surprisingly, a third was
+not sure whether such guidelines exist at their organisation. This
+points to a general discussion about responsibility profiles: whose
+responsibility covers the dissemination and implementation of such
+guidelines? In more depth, we collected our multiple roles via Particify
+and discussed them ([Fig. 2](#fig2)).
+
+<figure id="fig2" data-latex-placement="!ht">
+<img src="/assets/img/blog/2026/2026-08-25-ai-governance-fig2.png" style="width:80.0%" />
+<figcaption><p><b>Figure 2:</b> Participants' roles in interdisciplinary research fields
+show a wide range of responsibility areas and functions. The roles were
+collected via Particify.</p></figcaption>
+</figure>
+
+Participants mentioned unclear responsibilities, i.e. the ambiguity
+regarding who in an organization should take the lead in implementing AI
+governance. Among others the following question came up: If everything
+is already addressed in legal frameworks, what is the need for a
+governance framework? Additionally, the lack of visible, immediate
+benefits of implementing AI governance made it difficult to justify the
+effort of defining a governance framework. Another point that was
+mentioned was the fear of limiting oneself (or the organization), and
+missing out on the opportunities that AI may provide. On the ethical
+level, some argued that the moral integrity implied in guidelines for
+good scientific practice alone - along with the assumption that
+researchers inherently use AI responsibly - reduced the perceived
+urgency for formal governance. Subsequently, we explored the potential
+content for an AI governance document by identifying requirements for AI
+use in our organizations, covering technical, personnel, and
+administrative infrastructures ([Fig. 3](#fig3)).
+
+<figure id="fig3" data-latex-placement="!ht">
+<img src="/assets/img/blog/2026/2026-08-25-ai-governance-fig3.png" style="width:80.0%" />
+<figcaption><p><b>Figure 3:</b>Answers from participants identifying the topics relevant to
+discuss in the context of AI guidelines.</p></figcaption>
+</figure>
+
+At the end of our workshop, we discussed multiple (partly hypothetical)
+challenges and obstacles on the path to developing and adapting AI
+governance standards.
+
+# Related Work
+
+Although AI for research is a broad topic, the specific focus on
+governance and infrastructure in the context of roles and responsibility
+areas is currently getting increasing attention, e.g., with the BUA
+Workshop "AI Policies at Universities: Hands-On Workshop on Guidelines,
+Implementation, and Open Science" on 2 June 2026 \[[3](#bua)\], the
+de-RSE workshop on "AI-supported Research Software Engineering" in
+September 2026 \[[4](#derse)\] and the Research Software Alliance's
+workshop "Research Software Engineering in the Age of Generative AI:
+Building a Community Vision" in Edinburgh (UK), March
+2026 \[[10](#resa)\]. The evolution of the RSE role is also discussed in
+the blog post "Research Software Engineers in the Age of GenAI: Same
+Value, Changing Practice" \[[5](#blog)\].
+Discussing roles and responsibility areas is not new to RSE contexts, as
+we also discussed this together with colleagues from King's College
+already in June 2025 \[[8](#izd2m)\].
+
+# Conclusion
+
+GenAI challenges many areas of responsibility and infrastructure -
+especially for RSE and dRTP in their various roles dealing with genAI
+directly in their research contexts. We argue that governance and
+infrastructure are heavily interdependent. There is no living governance
+without accessible infrastructure. Vice versa, establishing
+infrastructure without governance risks the creation of responsibility
+vacuums \[[6](#freeman)\]. Governance and infrastructure that are
+developed with reference to each other, and specifically to serve the
+inclusion of GenAI in research life cycles, enable research integrity,
+sovereignty and therefore independent research. Governance and
+infrastructure should be developed and adapt in a user-centred way to
+address the requirements of administrative, social and technical
+infrastructure mentioned above. However, governance and the development
+of guidelines or policies is often centred around leadership roles which
+in turn might risk a gap between researchers needs and leadership's
+focal points. Interdisciplinary discussions with researchers fulfilling
+their responsibility roles is needed to first raise awareness of topics,
+second to exchange in depth knowledge and experience - literacies
+exchange for both groups - and third to develop and adapt
+collaboratively living guidelines that also take infrastructure into
+account.
+
+# Acknowledgements
+
+The authors would like to thank Alexander Struck and Claudia Göbel for
+organising the RSE Day 2026 in Berlin! This contribution was created in
+the context of the [Interdisciplinary Centre for Digitality and Digital
+Methods](https://izd2m.hu-berlin.de/) Campus Mitte, Humboldt-Universität
+zu Berlin. SD's work was supported by the [Lower Saxony Digital Science
+Support Space (DS³)](https://ds3-nds.de) project as part of
+Hochschule.digital Niedersachsen, funded by zukunft.niedersachsen.
+
+# References
+
+- <a id="particify"></a>\[1\] Anon. [n. d.]. Particify. Particify GmbH. <https://www.particify.de/en/>
+- <a id="benz"></a>\[2\] Arthur Benz, Susanne Lütz, Uwe Schimank, and Georg Simonis (Eds.). 2007. Handbuch Governance. VS Verlag für Sozialwissenschaften. doi:[10.1007/978-3-531-90407-8](https://doi.org/10.1007/978-3-531-90407-8)
+- <a id="bua"></a>\[3\] Berlin University Alliance. 2026. KI-Policies an Hochschulen: Praxisworkshop zu Leitlinien, Umsetzung und Open Science. <https://www.berlin-university-alliance.de/commitments/research-quality/events/2026/20260602-ki-os-policies.html>
+- <a id="derse"></a>\[4\] de-RSE – Gesellschaft für Forschungssoftware. 2026. De-RSE Collaboration + AI in RSE Workshop in Germany 2026. <https://events.hifis.net/event/3249/page/968-workshop-on-ai-supported-research-software-engineering>
+- <a id="blog"></a>\[5\] Stephan Druskat, Michelle Barker, Ian Cosden, Cunliang Geng, Robert Haines, Daniel S. Katz, Joseph Shingleton, and Ben van Werkhoven. 2026. Research Software Engineers in the Age of GenAI: Same Value, Changing Practice. Technical Report. Zenodo. doi:[10.5281/zenodo.20320179](https://doi.org/10.5281/zenodo.20320179)
+- <a id="freeman"></a>\[6\] Jo Freeman. 1972. The Tyranny of Stuctureless. <https://www.jofreeman.com/joreen/tyranny.htm>
+- <a id="rseday"></a>\[7\] Claudia Göbel and Alexander Struck. 2026. First Research Software Day Berlin & Brandenburg, 3 June 2026. <https://forschungssoftware.info/RSdays/2026/Berlin/>
+- <a id="izd2m"></a>\[8\] Henrik Schönemann. 2025. Retrospect: RSE Networking Event June 24th/25th. <https://izd2m.hu-berlin.de/blog-posts/2025/09/02/bp-rse-network-event.html>
+- <a id="matti"></a>\[9\] Matti Mäntymäki, Matti Minkkinen, Teemu Birkstedt, and Mika Viljanen. 2022. Defining Organizational AI Governance. AI and Ethics 2, 4 (Nov. 2022), 603–609. doi:[10.1007/s43681-022-00143-x](https://doi.org/10.1007/s43681-022-00143-x)
+- <a id="resa"></a>\[10\] Research Software Alliance. 2025. Research Software Engineering in the Age of Generative AI: Building a Community Vision. <https://www.researchsoft.org/events/rse-ai-workshop/>