EUSSET aims to support early career researchers in undertaking research stays at other European EUSSET institutions.

To this end, we have explored the Erasmus+ program and outline below what needs to be done in order to obtain financial support.

At the same time, we want to strengthen knowledge about researchers and research topics within EUSSET institutions. Resources for this already exist, such as the competence network (https://www.eusset.eu/about-eusset/the-eusset-competence-network/) on the EUSSET website. We also aim to facilitate networking at EUSSET conferences through dedicated activities, such as ECR lunches. We are pursuing the development of a long-term mobility network based on the following three pillars:

1. The Erasmus+ Funding Structure

The first requirement is an agreement between your home institution and the host institution. This agreement is called an Inter-Institutional Agreement (IIA) (https://erasmus-plus.ec.europa.eu/resources-and-tools/mobility-and-learning-agreements/inter-institutional-agreements?facets__field_eac_themes=1996)

By signing an inter-institutional agreement, the institutions involved agree to cooperate on the exchange of students and/or staff within the framework of Erasmus+. If your institution does not yet have an IIA with a partner you are interested in, we can help initiate this (please contact the ECR officers via email).

The EUSSET Steering Committee has begun establishing IIAs between the University of Siegen and other European research institutes within the EUSSET network.

What can you apply for?

Once these agreements are in place, ECRs can apply for Erasmus+ funding in various formats:

Individual mobility programs:

• Long-term (up to 1 year)
• and Short-term Doctoral Mobility (up to 30 days)  (example infos of Uni Siegen: https://www.uni-siegen.de/outgoing/phd/short-term/index.html.en?lang=en)
• Teaching mobility (several days) (https://erasmus-plus.ec.europa.eu/opportunities/individuals/teaching-opportunities-for-staff/higher-education-teaching-staff)

Group formats:

• Blended Intensive Programs (BIP)
  • Up to 5 days onsite
  • Combined with online pre- and post-meetings
  • Up to 20 participants
  • At least 3 different European countries involved

Exact conditions, funding amounts, and deadlines may vary between institutions. Please contact your International Office for details specific to your institution.

If you are not based at an EU institution

Erasmus+ only supports activities within Europe. For collaborations beyond Europe, the following organizations can be good alternatives:

• DAAD
• DFG
• Horizon Europe
• Alexander von Humboldt Foundation

Your research support office can advise you on suitable options.

2. Getting Information at ECR Lunches at EUSSET Conferences

At every EUSSET conference, we organize ECR lunches where we provide information about networking opportunities and give you the chance to connect with each other and with EUSSET institutions.

• In 2025, ECR lunches were organized at:
  • ECSCW 2025
  • C&T 2025
• In 2026:
  • An ECR lunch will take place at ECSCW 2026 (watch out for announcements!)
• For 2027:
  • ECR activities are being planned for the ECSCW, C&T and InfraHealth conferences

In 2025, we focused on the theme Research in Rural Areas and organized two accompanying online workshops.

3. Personal Consultation

If you would like to plan a research stay but are unsure where to start, feel free to reach out to us. We are happy to arrange an online meeting to discuss your ideas and help you figure out the best way to get started.

Contact us:
ecr@eusset.eu

Best Regard,
Claudia & Stephan

There is a systemic issue with the lack of gender diversity in computer science. This talk will identify structural exclusions of historically marginalized gender identities and gender presentations in Computer Supported Cooperative Work (CSCW) and beyond, and build a theory that is critical to closing this gap.

In this talk, Rode will overview old feminist theory and studies of women in science and tech that are sometimes gender-essentialist and will extend such works’ relevance to a world that is not heteronormative. Doing so will allow her to use its arguments to advance the rights of gender-divergent people. Conversely, she will take queer theory and interpret and show its relevance to cis women. Doing so allows her to build an integrated trans-feminist theory, a theoretical coalition against patriarchy in computing.

Developing an integrated trans-feminist theory will allow us to critically examine our cis/heteronormative tendencies, which perpetuate a vicious cycle of erasure, and instead frame scholarship in terms of gender identities and presentations allowing us to create technologies in CSCW that are inclusive and serve our diverse communities.

 Register for the colloquium through this link: https://events.teams.microsoft.com/event/9bfa4da6-1177-4ae0-8294-53f01c11c389@096e524d-6929-4030-8cd3-8ab42de0887b 

Looking forward to seeing many of you there!

Geertje Slingerland and Khuloud Abou Amsha
EUSSET Community Building Officers

As the year comes to a close, we would like to pause and reflect on what 2025 has brought. It has been an inspiring and rewarding year for our community. Here are some highlights:

• This year, Gerhard Fischer was awarded the IISI-EUSSET Lifetime Achievement Award for 2025 for outstanding contribution to the CSCW corpus of exploring how computer artefacts could contribute to the quality of life and specifically to the quality of education for all human actors, by touching upon areas of interdisciplinary socio-technical environments, including software ergonomics, domain-oriented design environments, critiquing systems, assistive technologies, computer-supported collaborative learning, creativity-enhancing environments, meta-design, and cultures of participation.
• We were fortunate to have hosted an inspiring ECSCW 2025 conference in the beautiful city of Newcastle upon Tyne, UK. We would like to thank Tom Bartindale, Clara Crivellaro, Vasilis Vlachokyriakos, and the organizing committee, as well as all those in our community who contributed to the conference’s success. Special congratulations also go to Ben Wilson, Chiara Natali, Matt Roach, Darren Scott, Alma Rahat, David Rawlinson and Frederico Cabitza, winners of this year’s David B. Martin Best Paper Award for the paper “Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts”.
• In August, we organized the 9th International Summer School on Computer-Supported Cooperative Work: Foundations, Methods, and Technologies in Vienna, Austria. Many thanks to Babak Farshchian, Kathleen Pine, Khuloud Abou Amsha, Lars Rune Christensen, and all our great lecturers for making this summer school unforgettable for all the young researchers in our community. We had the opportunity to discuss the theses of 22 PhD students with us, who also learned about our approaches and concepts in order to widen their views. On this occasion, we were delighted to welcome Mark Ackerman and Kathleen Pine for additional talks that further enriched our students’ understanding.
• This has been a particularly eventful year for the EUSSET steering committee. We continued our monthly online meetings, strengthening our collaborative spirit and carrying this momentum into 2026. Our retreats provided space to address more complex topics and to jointly shape our future services and organizational structure as an association. In addition, we substantially expanded our digital library and established working groups to better support the EUSSET community. Our EUSSET Colloquia continued to serve as a space for active exchange and discussion within the community. By the way, you may recall that ECSCW has changed its name to the “EUSSET Conference on Computer-Supported Cooperative Work,” inviting colleagues from around the world to our conferences.

 Finally, we would like to remind you of some exciting events to look forward to in 2026:

• ECSCW 2026 in Munich from June 29th to July 3rd
• EUSSET Summer School 2026 in Troyes from August 24th to August 28th

If you are interested in joining us, please don’t hesitate to contact us.

Happy Holidays and a wonderful New Year!

The idea was introduced at the EUSSET conferences in 2025 and is now gaining real momentum. The goal is to give early-career researchers the opportunity to spend short and exploratory research stays at other institutes, labs, or research centers, where they can explore new methods, perspectives, and collaborations.

With this, EUSSET is responding directly to the community’s call for more genuine, hands-on exchange beyond conference hallway conversations. The Winter Workshop is a physical space for networking, finding fellows with similar interests and having the opportunity to get to know a research institution in an intensive way. The phase of brainstorming is over — concrete action will take place at the University of Siegen from February 22-28, 2026:

We invite early-career researchers who do rural research as well as everyone else who is interested to join our multi-day workshop “Researching Rural Spaces”.

Dates: February 22-28, 2026 (multi-day retreat workshop)
Location: University of Siegen, Germany

The five-day on-site workshop provides space for academic exchange combined with site visits of rural places that are part of our Siegen living labs (PRAXLABS) research infrastructure (forest walks, Fab Lab visits, Community initiatives and more). The research group in Siegen would like to present their multi-facetted rural region research in fields such as industries, health and aging, sustainable community development and more. Also, Siegen people are happy to talk about their concepts and methods in regard to their socio-informatics approaches (practice based computing, participatory and sustainable collaborative research).

Besides working on the program, we are setting up inter-institutional agreements between European EUSSET Labs as part of the Erasmus+ program. This may provide possible funding for participants. Most universities offer the Erasmus+ Short Term Mobility program which can be applied for through your universities international office. If you want to participate and apply for funding, we like to ask you to talk to your universities international office as soon as possible. Our international office will gladly help you if their counselling is needed.

Are you interested? Here is how you can join:

1. Please send an email to philipp.rutz(a)uni-siegen.de indicating that you would like to participate until Decembre 19^th 2025.
2. Also include in your email an information whether your institution already has an inter-institutional agreement of the Erasmus+ program with University of Siegen.
3. Until 19^th of January 2026 please submit a workshop paper (3–6 pages) on your research topic, either in relation to rural regions or methodological aspects within practice-based computing.

Preliminary Program:

Date: December 15, 2025
Time: 10:00 – 11:00 CET
Format: Online (Microsoft Teams)

For this edition, Lahila Fanta Diaby and Maurizio Teli will present their joint reflections on working together across disciplinary boundaries. Their talk explores how the collaboration between a health professional and a non-healthcare CSCW scholar shaped their research trajectory—from initial conversations about technology in radiography to a shared investigation of the digital workflows that support everyday collaborative work in the field.

They will discuss the negotiations, learning processes, and mutual adjustments that guided their journey, illustrating what it means to build research at the intersection of different professional worlds.

To participate, please register via the event page:
Click here to access the event and subscribe

We look forward to seeing you there!

Geertje Slingerland and Khuloud Abou Amsha
EUSSET Community Building Officers

Each EUSSET Colloquium features one discussion idea or topic proposed by members of the community. The colloquium lasts one hour in which the speaker will briefly introduce the topic, and starts the discussion through a provoking statement or question. Proponents of discussion topics can submit materials, e.g. a paper, for the community to read in advance. Furthermoe, proponents prepare a 2-slide presentation to introduce their topic (title and question slides excluded). Meetings will happen over Zoom and people interested in participating are required to enroll to the event until 1 day before the event per e-mail. Confidentiality and conflicts of interest will be handled by the Community Building chairs.

If you have any queries about this format, please contact the Community Building chairs.

Make sure to register by 14 October to be able to participate. Registration is easy: just send an email to communitybuilding@eusset.eu.

Looking forward to seeing many of you there!
Geertje Slingerland and Khuloud Abou Amsha
EUSSET Community Building Officers

EUSSET: Asbjørn Malte, your paper “Cultivating Data Practices Across Boundaries: How Organizations Become Data-driven” was awarded the 2024 David B. Martin Best Paper Award during ECSCW 2024 – congratulations! Your paper is based on an empirical, ethnographic study in the field of healthcare business intelligence. Could you provide some additional background information about this paper? What led you to focus on data work and boundary objects in your research?

Asbjørn Malte: The paper is one strand of a larger research project called Making Data Work Visible, whose overarching aim is to reveal and investigate the largely invisible human labor that turns data into practical value in healthcare. Within that program, I conducted ethnographic fieldwork inside a Danish regional Business Intelligence unit. The unit’s mission is to repurpose healthcare data from electronic health records, finance, and logistics into ‘Data Reports’ (self-service analytic tools) and a regional data warehouse that clinicians, medical secretaries, and managers can use.

Initially, I set out to understand what working with data involved in this particular unit. Throughout the process, I realized two things: their data-driven technologies could be understood as boundary objects enabling cooperation across different boundaries between healthcare professionals, data professionals, administration, and managers. This concept became a natural analytical lens for me to understand the work revolving around the different data objects. And 2) beyond data extraction, analytics, and data warehousing, a lot of the unit’s work focused on disseminating these products. I observed BI developers teach nurses how to interrogate a dashboard and promote their BI setup through workshops, annual events, and merchandise, which was all very surprising to me. But this also made me realize that the Data Reports were not only doing a specific kind of socio-technical work as Boundary Objects but also needed work to become useful in practice. In short, I followed the practice, and the practice led me to Boundary Objects and to the complementary notion of collaborative boundary work that the BIU engages in to make those objects actually travel in everyday healthcare.

EUSSET: How would you summarize the main points of the paper to someone new to your work?

Asbjørn Malte: One of the central points is that we cannot assume that strategies, high-quality data, or having user-friendly data products are enough to disseminate data throughout the organization. It requires work beyond the technical, as well as intra-organizational dynamics, where multiple actors must collaborate across boundaries and take on new roles. We label these efforts ‘cultivating data practices across boundaries’ and point to three specific categories of (data) work: 1) ‘Mobilizing interest’ concerns the BI unit’s activities, events, and artifacts to make others interested in using data and demonstrate the potential to cultivate a cultural shift; 2) ‘building local capabilities’ designates their work of providing end-users (healthcare professionals and administrative workers) with capabilities necessary to work with data and apply different data-driven technologies in practice; and 3) ‘propagating data locally’ concerns the consolidation and extension of new collaborative data practices where healthcare professionals take on new responsibilities to use, implement, and disseminate data locally in their departments.

Regarding self-service analytic tools, the intention is often to shift the work from the IT department to the end user. In the healthcare sector, this raises questions about professional boundaries and the competencies required to succeed in practice, as well as questions about resources. Conceptually, the paper contributes by (a) showing how Boundary Objects become objects-in-use, (b) extending the literature on healthcare data work to include data professionals, and (c) providing an empirical counterpoint to the otherwise normative Business Intelligence and Self-Service BI literature.

EUSSET: What would you say were the main challenges to conducting this kind of empirical study? Any lessons learned that you might want to share?

Asbjørn Malte: First and foremost, to gain confidentiality and familiarity with my field and the people I met along the way. Building rapport was, of course, essential, but COVID-19 added another wrinkle: for months, fieldwork moved to Teams-based staff meetings and remote screen-sharing sessions. However, my experience was that both BI staff and healthcare professionals were curious and open to the study. Another challenge was disciplinary translation. To understand a data warehouse conversation, a medical secretary’s DRG vocabulary, and an ICU nurse’s workflow, you must become bilingual in clinical shorthand and SQL logic. My takeaway is that naivety can be constructive in the beginning, but long-term ethnography gives you enough time to acquire the dual literacy you need in the long run.

EUSSET: …and what are the positive experiences you have had in the field?

Asbjørn Malte: Without a doubt, all the passionate people I have met along the way: BI developers celebrating that they identified and corrected a small error in a table; a medical secretary proudly demonstrating her customized reporting tools used for quality assurance in her department; and clinicians critically assessing data visualizations to determine a patient’s options and treatment. Witnessing the impact firsthand is the upside of deep immersion, and those moments where data moves from an abstract data warehouse to a concrete purpose of use are professionally and personally rewarding. They remind you of all the hard labor and passion that goes into healthcare and data every day.

EUSSET: The paper presents an intriguing contribution regarding the role of data in healthcare systems and the work practices surrounding it. This topic is particularly relevant given the recent surge in machine learning, natural language processing, and data science applications. What are your thoughts on the potential opportunities and risks associated with the automatic processing of data in this context?

Asbjørn Malte: There is enormous promise: automated pattern-detection can uncover adverse-event hotspots faster than monthly audits; predictive models can help allocate staff before a surge hits; and natural-language processing can surface clinically relevant details hidden in free-text notes. But automation amplifies whatever data goes in. If registration practices are uneven, algorithms may reinforce local biases or silently exclude clinically meaningful nuance. Equally, the seductive clarity of a probability score can obscure the assumptions and labor that went into its creation. That is why it is so important to keep the hermeneutic, interpretive side of data work visible; teaching end-users to stay ‘critically attentive’ to data provenance, model limits, and ethical implications. Automatic processing should augment, not replace, situated clinical judgement.

EUSSET: Is this a line of research you plan to continue pursuing, or do you intend to shift your focus to something entirely different?

Asbjørn Malte: Although the Making Data Work Visible project has formally ended, I still think data work is an important part of the discussion about the digitalization of the healthcare sector, so for now, I still have a bit of focus on data work in healthcare BI. Moreover, I remain fascinated by how health-sector digitalization keeps redrawing professional boundaries. My next step is to follow this thread into clinical AI-enabled prediction and decision support: How does work change when the “object” is an algorithm rather than a dashboard? What new competencies will front-line clinicians need to make sense of probabilistic outputs? What does it take to successfully implement clinical AI? In other words, I will stay with datafication in healthcare but follow it into its emerging ML and AI articulations.

EUSSET: Is there anything you’d like to share with the community members?

Asbjørn Malte: First, a heartfelt thank you to the ECSCW award committee. Receiving the David B. Martin Best Paper Award as an early-career researcher is humbling and motivating. I also owe much to the anonymous CSCW reviewers whose constructive comments undeniably made the paper – and me – better. Finally, I would like to extend my gratitude to the community for always being so engaging and accommodating.

EUSSET: Thank you so much for sharing your insights with us. Your research and insights are of great value to healthcare practitioners and the EUSSET community. We express our congratulations and wish all the best to you for the future.

The interview was conducted by Mateusz Dolata.

EUSSET: Ben and Chiara, your paper “Dimensions of Human-Machine Combination: Prompting the Development of Deployable Intelligent Decision Systems for Situated Clinical Contexts” co-authored by Ben Wilson, Chiara Natali, Matt Roach, Darren Scott, Alma Rahat, David Rawlinson, and Federico Cabitza was awarded the 2025 David B. Martin Best Paper Award during ECSCW 2025 – congratulations! Could you introduce yourselves and tell us how this very international team came together to tackle the challenge of clinical AI implementation?

Chiara: I am a PhD candidate in Computer Science at University of Milano-Bicocca, where my research focuses on Human-AI Interaction: particularly, on assessing interaction quality beyond performance accuracy and usability, and on design principles for reflective use that supports user agency. I was introduced to Ben by my PhD supervisor, Prof. Federico Cabitza. Ben’s idea of the eight dimensions of combination, and its relevance for situated design, immediately struck us as something that could offer a pragmatic vocabulary for CSCW. At the very beginning, my role was first and foremost to help ground the work in the theories and ethos of the CSCW community, ensuring alignment with its foundational perspectives on socio-technical systems. I took particular pride in drawing from the line of research developed over the past thirty years in Milan by Prof. Carla Simone (one of EUSSET’s founders and recipient of the 2021 EUSSET-IISI Lifetime Achievement Award), a scholarly tradition now carried forward by Prof. Cabitza, my doctoral supervisor for the past three years: he began his academic path under Prof. Simone’s mentorship and developed his own research within the same intellectual lineage. I’m delighted, and a little amazed, by how this work resonated with the community and the conversation it sparked.

Ben: My background is in clinical data and clinician-facing technology in the UK’s National Health Service. I had the core of the dimensions as they’d arisen from a review of the literature undertaken as part of a PhD at Swansea’s Computational Foundry. Having formulated the ideas, I wanted to reach out to other researchers in the field. So I wrote to Federico Cabitza, whose work I had found inspiring. I thought he was going to be at a conference I was attending, but I missed him at the conference since neither of us could attend the whole event. He got back to me saying he liked the outline presentation I’d sent him. At our first meeting, he introduced me to Chiara and we took off from there.

EUSSET: What sparked the idea for this paper? Was there a particular moment or experience that made you realize the need to map out these “dimensions of combination”?

Ben: The core of the paper was part of the work for my PhD which was in collaboration with a specialist service in the National Health Service in Wales. The service provides pre-hospital critical care for the whole of Wales and we were looking at decisions in the early part of their dispatch process. The literature review had a fairly conventional motivation. It aimed at establishing the context for experimental and development work. But the different aspects of human-machine combination arose as themes from looking at all these papers. And this part of the work grew into the dimensions framework. It was as if formulating the dimensions of combination was the only way to make sense of the diversity of process and practice that researchers were engaging in. Underlying this process was a desire to explore what would make it more likely for technology solutions to bring real benefit. I was aware of the poor translation record in parts of the health system in the UK – how repeatedly, there were patterns showing money spent on the promise of huge gains for everyone without that translating into meaningful improvement for patients and for the clinicians providing their care. I wanted to dig into how the developers of technology might better anticipate what’s really needed, rather than assuming the best solution was a system that gave a fractionally improved machine performance on some (often idealised) benchmark dataset. So the paper is both a call to action and a guide to action in the form of the dimensions of combination.

Chiara: The first spark that made it all possible was, of course, Ben’s preliminary sketch of dimensional mapping. Once we began collaborating, further sparks came from exploring the implications for design of these dimensions, making sure the mapping wasn’t just an abstract exercise, but a theory-informed, pragmatic tool for both scholars and practitioners – exactly what Ben refers to as “a call to action and a guide to action”. We refined these dimensions to explicitly account for CSCW theory and real-world observations of the AI chasm, so that it could help bridge the gap between promising technical performance and meaningful, situated use.

EUSSET: What were the biggest challenges you faced during this research, especially given that you were working across disciplines?

Chiara: Building a shared vocabulary. Our calls were always fun and productive, but we kept discovering that the same words (combination, collaboration, even control – which at first was dubbed authority) meant different things across our scholarly communities. This friction led directly to the “Disambiguations” section, where we take a brief excursus through the history and theory of HCI and CSCW. That section is not just a roadmap for the reader: it was a real alignment tool for us as a team. Writing it forced us to make our own assumptions visible and negotiate them, which made the rest of the framework much stronger.

Ben: Yes, it’s easy to assume we understand each other based on having a shared set of words. But it’s more work to establish a real shared understanding and a vocabulary in common. Our different backgrounds gave rise to the different baggage we brought with us. But our shared motivations meant we were able to navigate the terrain together and work out a common position. As Chiara says, the need to address different understandings was a friction that forced us to strengthen our fundamental alignments and clarify what we wanted to say. So embracing the differences was a challenge that really paid off.

EUSSET: Were there any surprising discoveries along the way? Perhaps something that challenged your initial assumptions?

Ben: Yes, I had always expected studying artificial intelligence and machine learning to be a process of technical discovery – finding the possibilities and limits of computational processes. But it’s much more a process of social discovery. The more I learn about developments in machine capabilities, the more amazed I am about the familiar human capabilities we take for granted in problem-solving and pattern recognition. There’s also a sense in which studying human-machine combination reveals a lot about how we navigate and develop human-human combinations. If we reach a level of dependency on a machine’s complex behaviours, we tend to fall into treating it as we would another human. Knowing it’s a machine isn’t quite as powerful as the affective response it can generate within us. As a society, we don’t have the skills or even the language to describe appropriate relationships between ourselves and complex systems. This has the potential to alter our human relationships in ways we have yet to learn.

Chiara: I agree. Also, I was struck by the magnitude of the gaps in the literature that became visible once we started thinking through the eight dimensions. In particular, the scarcity of prospective, situated evaluations, and the near-absence of studies looking beyond simple one-to-one human-AI pairings, really stood out. Seeing those blind spots laid out so clearly was, for me, both surprising and motivating.

EUSSET: Your paper introduces eight “contextual dimensions of combination” as a framework for understanding human-AI interaction in clinical settings. Could you explain why this framework is crucial for closing what you call the “AI chasm”?

Chiara: Because it compels explicit, situated design choices long before a system reaches deployment. The eight dimensions make visible the interactional and informational constraints that determine whether algorithmic performance will actually translate into clinical benefit. In other words, if you don’t know where you sit in this combination space, you can’t design the right interaction patterns, anticipate order effects, or select an evaluation approach that’s fit for purpose. The framework therefore supports co-design and early formative evaluation tied to the concrete realities of the workflow — exactly the kind of attention to context needed to bridge the chasm between lab performance and meaningful use.

Ben: It’s a good question to ask. Why should it make any difference to clinical translation if developers are made aware of these dimensions? Chiara has laid out clearly how they support co-design and anticipate needs. But how do they compel better design choices? The eight dimensions don’t tackle this issue directly. They don’t provide design choices. What they do is indirect. They present straightforward technical questions that together help form a more situated perspective on the problem space. That view of the problem space being the real combination of human and machine rather than the abstracted decision is what’s important, I think. It requires the developer to consider the sociotechnical context. And it’s this situated sociotechnical perspective that’s crucial for closing the ‘AI Chasm’. I should say that my view here is based on what I saw as the weaknesses of so many ‘solutions’ that weren’t designed for real workflows. If the user has extra work to do to access a system, it had better be really useful when it it’s brought into play. If your task is to decide between fifteen possible outcomes, it’s likely that a binary system that predicts between two classes of outcome will be of minimal use a lot of the time. If what would help you most is an assistive prediction that’s extremely sensitive, it’s less useful to have a model with average sensitivity, even if it has high accuracy. If you have twenty real decision tasks an hour, it’s of less practical use if you find that the new system can help with three of them, but you can’t tell in advance which three  – and each one requires an investment of your precious time. It has to be said, and you can see from these user frustrations, that addressing the dimensions of combination isn’t guaranteed to eliminate problems. But it’s also true that you can’t address the dimensions of combination without having engaged with the situated workflow of the human user. So they are a practical means of moving from the abstract to the concrete and foregrounding the combination in early design steps.

EUSSET: How did you work together as a team, especially balancing perspectives from computer science, healthcare, and HCI?

Ben: I think this is where Matt Roach was invaluable in the research process. He’s very good at posing a question and prompting an alternative perspective in a way that is critically supportive. He recognises the value of diversity and encourages cross-discipline exploration. But he’s also very good at seeing a critical path and identifying how to achieve progress with the resources at hand. So Matt was probably the key to getting those balancing tasks right. We were also lucky in meeting and aligning with the right people. Chiara is highly productive and brings lots of energy and insight to any writing task. She and Prof Federico Cabitza also have a rich knowledge of the CSCW tradition. My reading was more centered around Suchman’s research at PARC. They helped me round out my perspectives and form a broader base for the theoretical lens. David Rawlinson is one of those invaluable clinicians who has developed their work with technology to benefit their colleagues without letting go of his clinical involvement. So he really straddles the disciplines and feeds back very efficiently. Darren Scott and Alma Rahat have each been able to step in and provide critical effort and advice exactly when needed – perfect teammates. I guess I’m saying there was a lot of luck involved.

Chiara: Well, you could say we were a good “combination”!Luck is a component in finding the right people, but I’d like to credit the way we approached the writing and reviewing itself. A good part of the process took place “live” in many Zoom sessions. I’d never worked quite this way before, and it really helped to ensure our ideas were not only conceptually clear but also mutually intelligible across disciplines – sometimes they were being shaped in real time through dialogue. That process made the balancing of perspectives a methodological commitment, rather than an afterthought or post-hoc edit.

EUSSET: What was the most rewarding or enjoyable part of working on this project?

Chiara: A bit cheesy, sure – But the most enjoyable part was “the friends we made along the way”. From not knowing Ben at all to now having him addicted to my hometown’s traditional wafers, it’s been a genuine pleasure. And in terms of “rewarding”, the response to the paper has been beyond anything I expected. The road to completion was long and bumpy, but seeing it resonate so strongly with the CSCW community has made every bit of that effort worthwhile.

Ben: Yes. I think we’ve made lasting connections through this work. There’s something very fulfilling about finding people whose skills and insights are complementary to your own, but with whom you can work, dialogue, reflect and share critical feedback. As an early career researcher, it’s humbling to find others, including seasoned academics, who are able to see value in what you do.

EUSSET: What impact do you hope this paper will have on how clinical AI systems are developed and evaluated?

Ben: My impression is that most machine learning researchers see themselves as being heavily involved in human-computer interaction or in researching human-machine combinations. But they rarely see their task as finding out what a human needs in a given combination scenario. I hope that looking at the dimensions of combination will prompt greater attention to be paid by the machine learning and AI development communities to what makes for synergistic systems. Let’s make it more a research space where metrics for appropriate reliance are foregrounded along with evaluating the combination. The paper shows that we still have a skew towards papers that evaluate the machine in isolation. And this has an impact on what is developed.

Chiara: I hope it encourages teams to treat the human-machine combination as a design object in its own right. That means making the intended configuration explicit early on, so that interaction patterns and evaluation methods are tied to a clearly defined combination. I would like to see the eight dimensions used as prompts during co-design, to guide early formative evaluations, and to make “situated validity” as central as accuracy.

EUSSET: What’s next for your research? Are there particular dimensions or aspects you’re excited to explore further?

Chiara: The dimensions of combination feed directly into the beyond-accuracy ethos of my work, arguing that system development and evaluation must consider socio-technical factors alongside (and sometimes even in tension with) accuracy and usability. I am in the final stages of writing my PhD thesis, focusing on the idea of beneficial friction in human-AI interaction: designing interactions that deliberately slow or challenge users in order to calibrate reliance and preserve skill. The dimensions framework gives me a structured way to embed and test these ideas in situated contexts. I’m starting to look for a postdoc role where I can keep deepening and expanding this line of research.

Ben: I’m pursuing two directions of further work, and considering a third. One of the two is in exploring further situated use of decision assistance – taking the research to specific use-cases. The other is in exploring the number and type of participating agents dimensions. Multiple humans seeking a common decision with assistance. There are so many sub-dimensions to this space that you could spend years exploring just this aspect. One of the impacts of the paper, I think, is that it illuminates just how much work there is to do to research human-machine combination. The third area I’m considering is how we should analyse the dimensions of combination in respect of generative models – especially language models. I think there’s a lot of work to be done in analysing current research on the use of language models. The pace of development and volume of research is likely to build up the incessant news of workplace transformations. But I think there’s a lot that isn’t known about how LLMs might or might not combine with humans who seek to do different tasks with their help.

The interview was conducted by Mateusz Dolata.