Today I gave a short talk at a teach-in on the politics of data centres. The teach-in was part of the Stop Dirty Data Centres European Days of Action on 6 and 7 November, when local groups across Europe protested and called for a moratorium on large data centres.
The teach-in took place during a support demonstration organised by Fossielvrij NL at the Plimsollweg in Amsterdam Sloterdijk. That’s the site where Pure DC is building a hyperscale data centre for Microsoft, despite the city’s ban on hyperscales. Fieke Jansen and Charis Papaevangelou organised a line-up of Dutch university teachers, each of whom had five minutes, outdoors and without slides, to explain one part of what is at stake. Thanks to them for inviting me, to Fossielvrij NL for organising the demo and to Geef Tegengas for their direct action.
A rough abstract of what I said:
I don’t need to convince anyone at a demo like this that we should resist the buildout of big corporate AI. The question I asked was: what could computing be instead? I shared three lessons from my research as a designer. First, look past the app to the infrastructure underneath it. To really be able to shape applications, people need a say in the stuff they are built on top of. Second, you can leave an app, but you can’t leave the infrastructure, so pushing back takes durable organisations with members, in addition to individual protest. I used De Digitale Stad from back in 1994 as an early example of community computing. Third, in addition to opposing big corporate AI, I am mainly interested in exploring alternative forms of computing. Like the Luddites, we can reject exploitative technological practices without rejecting all technology. One test is whether a system resists extraction or accelerates it. This brings me to design. In my view design is always political, so designers should join communities and communities should become designers, both to resist extractive computing and to build regenerative computing that meets social needs.
Update 9 November 2026: Delta interviewed me about the teach-in. Read it in English or Dutch.
Speaking at the teach-in at the Plimsollweg, Amsterdam, 6 November 2026. Photo: Dorotea Pace / Fossielvrij NL.
Slides from my keynote at the CLICKNL Design Innovation Sessions, Digital Futures theme session, Dutch Design Week, 21 October 2026, Natlab, Eindhoven.
CLICKNL invited me to give this keynote. I saw it as a great opportunity to intervene in the debate about design and generative AI. My take is informed by my past work on contestability. That work is all about approaching “AI” as an arena where various parties compete over how it is made and how it is used. My take is also informed by my commitments to practice theory and political realism. Practice theory means looking at what people and things do together, rather than at technologies in isolation. Political realism means looking at politics as it actually happens. It asks who does what to whom, for whose benefit, instead of starting from abstract moral principles.
In the talk I argue that neither wholesale rejection nor uncritical embrace of generative AI will get us very far. GenAI deskills designers and opens up new ways of designing at the same time. It is built on the past work of designers, which raises the question of whose work we build on, and who gets to call the results their own. And it automates work, but in the process work may get delegated more than replaced, transforming practices as a result. Rather than trying to resolve these tensions, I look for emerging design practices that hold them and use them as an engine for moving forward. I discuss three cases: Vera van der Burg’s Text-to-Clay, Blaze Type’s test of Monotype’s AI type tool, and The Call by Holly Herndon and Mat Dryhurst. They all go beyond consuming off-the-shelf models. They treat pipelines and workflows as an object of design. And in all three there is an awareness of the value of effort and friction. I end with an open question. If AI is not one thing, not an agent, and neither our savior nor our doom, what metaphor does your mind go to?
I hope this talk helps designers think in concrete terms about how we might “do AI” differently from the dominant ways. And I hope it helps them break free from the simplistic for-or-against debates that get us nowhere.
Vera van der Burg, The Material Gaze: Reflective AI as a Design Practice, PhD dissertation, Delft University of Technology, 2026. https://doi.org/10.4233/uuid:fb78a0d0-0eb9-46e9-b7af-d54bca8796ca (Text-to-Clay is Chapter 7, with technical details in Appendix D.)
Blaze Type and Monotype’s AI type tool
Sebastien Hayez, “Designing Fonts with AI: Future of Type Design”, Blaze Type Blog, 18 June 2025. https://blazetype.eu/blog/designing-fonts-with-ai/ (It reports Matthieu Salvaggio’s talk at Brand Talks, London, 12 June 2025.)
Victoria Ivanova and Jennifer Ding, Choral Data ‘Trust’ Experiment White Paper, Serpentine Arts Technologies, Zenodo, 17 Feb 2025. https://doi.org/10.5281/zenodo.14859320
Jennifer Ding, Eva Jäger, Victoria Ivanova and Mercedes Bunz, “My Voice, Your Voice, Our Voice: Attitudes Towards Collective Governance of a Choral AI Dataset”, arXiv, 2 Dec 2024. https://arxiv.org/abs/2412.01433
CRTA visual identity, Bruketa&Žinić&Grey
Bruketa&Žinić&Grey, CRTA visual identity, for the Regional Centre of Excellence for Robotic Technology, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, 2021.
Fieke Jansen, Sunjoo Lee, Kars Alfrink and Lena Trotereau, From Mud to Models: Regenerative Futures for Community AI (conference workshop), TH/NGS 2025, Amsterdam, 12 Dec 2025, and From Soil to Servers: Regenerative Futures for Community Computing (workshop), Creative Coding Utrecht, Utrecht, 27 Mar 2026; Kars Alfrink and Sunjoo Lee, with Advaita Jairam, From Soil to Servers: Pairing Material Practice with Speculative Design to Rethink Computing Infrastructure (conference workshop), Philosophy of Human-Technology Relations Conference (PHTR 2026), The Hague, 22 Sep 2026.
Kars Alfrink, Sunjoo Lee, Lena Trotereau and Fieke Jansen, “Mud and models: Does making matter for regenerative AI speculation?”, in Iskander Smit and Andrea Krajewski (eds.), The State of Responsible Technology – RESIZE<REMIX<REGEN (RIOT 2026), Stichting ThingsCon Amsterdam, June 2026, pp. 34–39. https://doi.org/10.5281/zenodo.20569440
Further reading
Alfrink, K. (2025). People’s compute: Design and the politics of AI infrastructures. OSF. https://doi.org/10/q8kp
Caplan, L. (2025). Introduction: Contesting models – art and democracy since the 1990s. In L. Caplan & K. Greaves (Eds.), Model collapse: European contemporary art in a time of democratic crisis (pp. 1–20). Manchester University Press. http://www.jstor.org/stable/jj.36233585.6
Geuss, R. (2008). Philosophy and real politics. Princeton University Press.
Kuutti, K., & Bannon, L. J. (2014). The turn to practice in HCI: Towards a research agenda. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 3543–3552). ACM. https://doi.org/10.1145/2556288.2557111
Löwgren, J., & Stolterman, E. (2004). Thoughtful interaction design: A design perspective on information technology. MIT Press.
McGowan, T. (2016). Capitalism and desire: The psychic cost of free markets. Columbia University Press.
McGowan, T. (2019). Emancipation after Hegel: Achieving a contradictory revolution. Columbia University Press.
Parsons, P. C. (2026). Myths and ironies of AI-assisted design. In Designing Interactive Systems Conference (DIS ’26) (pp. 523–535). ACM. https://doi.org/10.1145/3800645.3813086
Porfirio, D., McDermott, I., Chen, H.-M., Satake, S., Kanda, T., & LaToza, T. D. (2026). Designing for robot wranglers: A synthesis of literature and practice. In Designing Interactive Systems Conference (DIS ’26) (pp. 4284–4298). ACM. https://doi.org/10.1145/3800645.3813070
Retkowsky, J., Hafermalz, E., & Huysman, M. (2026). Harnessing a “spirited technology”: How working with generative AI collapses the creative process. Academy of Management Discoveries, 12(3), 363–387. https://doi.org/10.5465/amd.2024.0280
Shumailov, I., Shumaylov, Z., Zhao, Y., Gal, Y., Papernot, N., & Anderson, R. (2024). The curse of recursion: Training on generated data makes models forget. arXiv. https://arxiv.org/abs/2305.17493
This post was taken down on 26 October 2026. A revised version will appear at this address.
About the original
Transcript of a talk delivered at LXDCON’25 on June 12, 2025. The original ran to about 1,706 words and 32 images.
Abstract
Generated by Claude, a language model, from the original text; not written by the author.
Starting from ChatGPT’s impact on higher education, it contrasts Ethan Mollick’s adapt-and-redesign optimism with Ian Bogost’s realist diagnosis of a crisis that technology cannot fix, using Harvard’s StatGPT Slackbot as a running example. It proposes “Conscious Adaptation”, built on three strategies: institutional realism, loss-conscious innovation, and keeping solution-building and critique as distinct but mutually informing work. It ends with three imperatives for learning experience designers: protect meaningful human effort, design for real institutions rather than ideal ones, and recognise the limits of design.
The following is a section from a manuscript in press on the similarities and differences in approaches to explainable and contestable AI in design and law (Schmude et al., 2025). It ended up on the cutting room floor, but it is the kind of thing I find handy to refer back to, so I chose to share it here.
The responsible design of AI, including practices that seek to make AI systems more explainable and contestable, must somehow relate to legislation and regulations. Writing about responsible research and innovation (RRI) more broadly, Stilgoe et al. (2013) assert that RRI, which we would say includes design, must be embedded in regulation. But does it really make sense to think of the relationship between design and regulation in this way? Understood abstractly, there are in fact at least four ways in which we can think about the relationship between the design and regulation of technology (Figure 1).
Figure 1: We see four possible ways that the relationship between (D) the design and (R) regulation of technology can be conceptualized: (1) design and regulation are independent spheres, (2) design and regulation partially overlap, (3) design is embedded inside of regulation, or (4) regulation is embedded inside design. In all cases, we assume an interactional relation between the two spheres.
To establish the relationship between design and regulation, we first need to establish how we should think about regulation, and related concepts such as governance and policymaking more generally. One straightforward definition would be that regulation entails formal rules and enforcement mechanisms that constrain behavior. These are backed by authority—typically state authority, but increasingly also non-stake actors. Regulation and governance are interactive and mutually constitutive. Regulation is one mechanism within governance systems. Governance frameworks establish contexts for regulation. Policymaking connects politics to governance by translating political contestation into actionable frameworks. Politics, then, influences all these domains: policymaking, governance, and regulation. And they, in turn, operate within and reciprocally shape society writ large. See Table 1 for working definitions of ‘regulation’ and associated concepts.
Concept
Definition
Regulation
Formal rules and enforcement mechanisms that constrain behavior, typically state-backed but increasingly emerging from non-state actors (industry self-regulation, transnational regulatory bodies).
Governance
Broader arrangements for coordinating social action across public, private, and civil society spheres through both formal and informal mechanisms.
Policymaking
Process of formulating courses of action to address public problems.
Politics
Contestation of power, interests, and values that shapes governance arrangements.
Society
Broader context of social relations, norms, and institutions.
Table 1: Working definitions of ‘regulation’ and associated concepts.
What about design? Scholars of regulation have adopted the notion of ‘regulation by design’ (RBD) to refer to the inscribing of rules into the world through the creation and implementation of technological artifacts. Prifti (2024) identifies two prevailing approaches to RBD: The essentialist view treats RBD as policy enactments, or “rules for design.” By contrast, the functionalist view treats design as a mere instrument, or “ruling by design.” We agree with Prifti when he states that both approaches are limited. Essentialism neglects the complexity of regulatory environments, while functionalism neglects the autonomy and complexity of design as a practice.
Prifti proposes a pragmatist reconstruction that views regulation as a rule-making activity (“regulativity”) performed through social practices including design (the “rule of design”). Design is conceptualized as a contextual, situated social practice that performs changes in the environment, rather than just a tool or set of rules. Unlike the law, markets, or social norms, which rely on incentives and sanctions, design can simply disable the possibility of non-compliance, making it a uniquely powerful form of regulation. The pragmatist approach distinguishes between regulation and governance, with governance being a meta-regulative activity that steers how other practices (like design) regulate. This reconceptualization helps address legitimacy concerns by allowing for greater accountability for design practices that might bypass democratic processes.
Returning to the opening question then, out of the four basic ways in which the relationship between design and regulation can be drawn (Figure 1), if we were to adopt Prifti’s pragmatist view, Type 3 would most accurately capture the relationship, with design being one of a variety of more specific ways in which regulation (understood as regulativity) actually makes changes in the world. These other forms of regulatory practice are not depicted in the figure. This seems to align with Stilgoe et al.’s aforementioned position that responsible design must be embedded within regulation. Although there is a slight nuance to our position: Design is conceived of as a form of regulation always, regardless of active work on the part of designers to ‘embed’ their work inside regulatory practices. Stilgoe et al.’s admonition can be better understood as a normative claim: Responsible designers would do well to understand and align their design work with extant laws and regulations. Furthermore, following Prifti, design is beholden to governance and must be reflexively aware of how governance steers its practices (cf. Figure 2).
Figure 2: Conceptual model of the relationship between design, ‘classical’ regulation (i.e., law-making), and governance. Both design and law-making are forms of regulation (i.e., ‘regulativity’). Governance steers how design and law-making regulate, and design and law-making are both accountable to (and reflexively aware of) governance.
Bibliography
Prifti, K. (2024). The theory of ‘Regulation By Design’: Towards a pragmatist reconstruction. Technology and Regulation, 2024, 152–166. https://doi.org/10/g9dr24
Stilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568–1580. https://doi.org/10/f5gv8h
I am over one year into my postdoc at TU Delft. Where did the time go? By way of an annual report, here’s a rundown of my most notable outputs and activities since the previous update from June 2024. And also, some notes on what I am up to now.
Happenings
Participatory AI and ML Engineering: On 13 February 2024 at a Human Values for Smarter Cities meeting and on 11 June 2024 at a Cities Coalition for Digital Rights meeting, I presented a talk on participatory AI and ML engineering (blogged here). This has since evolved into a study I am currently running with the working title “Vision Model Macroscope.” We are designing, building, and evaluating an interface that allows municipal workers to understand and debate value-laden technical decisions made by machine learning engineers in the construction of camera vehicles. For the design, I am collaborating with CLEVER°FRANKE. The study is part of the Human Values for Smarter Cities projected headed up by the Civic Interaction Design group at AUAS.
Envisioning Contestability Loops: My article “Envisioning Contestability Loops: Evaluating the Agonistic Arena as a Generative Metaphor for Public AI” (with Ianus Keller, Mireia Yurrita Semperena, Denis Bulygin, Gerd Kortuem, and Neelke Doorn) was published in She Ji on 17 June 2024. (I had already published the infographic “Contestability Loops for Public AI,” which the article revolves around, on 17 April 2024.) Later in the year, on 5 September 2024, I ran the workshop that the study builds on as a ThingsCon Salon. And on 27 September 2024, I presented the article at Lawtomation Days in Madrid, Spain, as part of the panel “Methods in law and technology research: inter- and cross-disciplinary challenges and opportunities,” chaired by Kostina Prifti (slides). (Also, John Thackara said nice things about the article online.)
Contestability Loops for Public AI infographic
Envisioning Contestability Loops workshop at ThingsCon Salon in progress.
Democratizing AI Through Continuous Adaptability: I presented on “Democratizing AI Through Continuous Adaptability: The Role of DevOps” at the TILTing Perspectives 2024 panel “The mutual shaping of democratic practices & AI,” which was chaired and moderated by Merel Noorman on 14 July 2024. I later reprised this talk at NWO ICT.OPEN on 16 April 2025 as part of the track “Human-Computer Interaction and Societal Impact in the Netherlands,” chaired by Armağan Karahanoğlu and Max Birk (PDF of slides).
From Stem to Stern: I was part of the organizing team of the CSCW 2024 workshop “From Stem to Stern: Contestability Along AI Value Chains,” which took place as a hybrid one-day session on 9 November 2024. I blogged a summary and some takeaways of the workshop here. Shoutout to Agathe Balayn and Yulu Pi for leading this endeavor.
Designing Responsible AI: Over the course of 2024, Sara Colombo, Francesca Mauri, and I developed and taught for the first time a new Integrated Product Design master’s elective, “Designing Responsible AI” (course description). Later, on 28 March 2025, I was invited by my colleagues Alessandro Bozzon and Carlo van der Valk to give a single-morning interactive lecture on part of the same content at the course AI Products and Services (slides).
Books that represent the range of theory covered in the course “Designing Responsible AI.”
Stop the Cuts: On 2 July 2024, a far-right government was sworn in in the Netherlands (it has since fallen). They intended to cut funding to education by €2 billion. A coalition of researchers, teachers, students, and others organized to protest and strike in response. I was present at several of these actions: The alternative opening of the academic year in Utrecht on 2 September 2024. Local walkouts on 14 November 2024 (I participated in Utrecht). Mass demonstration in The Hague on 25 November 2024. Local actions on 11 December 2024 (I participated in Delft). And finally, for now at least, on 24 April 2025, at the Delft edition of the nationwide relay strike. If you read this, work in academia, and want to act, join a union (I am a member of the AOb), and sign up for the WOinActie newsletter.
End of the march during the 24 April 2025 strike in Delft.
Panels: Over the past months, I was a panelist at several events. On 22 October 2024, at the Design & AI Symposium as part of the panel “Evolving Perspectives on AI and Design,” together with Iohanna Nicenboim and Jesse Benjamin, moderated by Mathias Funk (blog post). On 13 December 2024 at TH/NGS as part of the panel “Rethink Design: Book Launch and Panel Discussion on Designing With AI” chaired by Roy Bendor (video). On 12 March 2025, at the panel “Inclusive AI: Approaches to Digital Inclusion,” chaired by Nazli Cila and Taylor Stone.
Slide I used during my panel contribution at the Design & AI symposium.
Design for Human Autonomy: I was part of several activities organized by the Delft Design for Values institute related to their annual theme of autonomy (led by Michael Klenk). I was a panelist on 15 October 2024 during the kick-off event (blog post). I wrote the section on designing AI for autonomy for the white paper edited by Udo Pesch (preprint). And during the closing symposium, master’s graduation student Ameya Sawant, whom I am coaching (with Fernando Secomandi acting as chair), was honored as a finalist in the thesis competition.
Master Graduation Students: Four master students that I coached during their thesis projects graduated, which between them explored technology’s role in society through AI-mediated civic engagement, generative AI implementation in public services, experimental approaches to AI trustworthiness, and urban environmental sensing—Nina te Groen (with Achilleas Psyilidis as chair), Romée Postma (with Roy Bendor), Eline Oei (with Giulia Calabretta), and Jim Blom (with Tomasz Jaskiewicz).
Architecting for Contestability: On 22 November 2025, I ran a single-day workshop about contestability for government-employed ICT architects participating in the Digital Design & Architecture course offered by the University of Twente, on invitation from Marijn Janssen (slides).
Qualitative Design Research: On 17 December 2024, I delivered a lecture on qualitative design research for the course Empirical Design Research, on invitation from my colleague Himanshu Verma (slides). Later, on 22 April 2025, I delivered a follow-up in the form of a lecture on reflexive thematic analysis for the course Product Futures Studio, coordinated by Holly McQuillan (slides).
Democratic Generative Things: On 6 June 2025 I joined the ThingsCon unconference to discuss my contribution to the RIOT report, “Embodied AI and collective power: Designing democratic generative things” (preprint). The report was edited by edited by Andrea Krajewski and Iskander Smit.
Me, holding forth during the ThingsCon RIOT unconference.
Learning Experience Design: I delivered the closing invited talk at LXDCON on 12 June 2025, reflecting on the impact of GenAI on the fields of education and design for learning (slides). Many thanks to Niels Floor for the invitation.
People’s Compute: I published a preprint of my position paper “People’s Compute: Design and the Politics of AI Infrastructures” over at OSF on 14 April 2025. I emailed it to peers and received over a dozen encouraging responses. It was also somehow picked up by Evgeny Morozov’s The Syllabus with some nice commentary attached.
On deck
So what am I up to at the moment? Keeping nice and busy.
I am co-authoring several articles, papers, and book chapters on topics including workplace automation, AI transparency, contestability in engineering, AI design and regulation, computational argumentation, explainable and participatory AI, and AI infrastructure politics. I do hope at least some of these will see the light of day in the coming months.
I am preparing a personal grant application that builds on the vision laid out in People’s Compute.
I will be delivering an invited talk at Enterprise UX on 21 November 2025.
I am acting as a scientific advisor to a center that is currently being established, which focuses on increasing digital autonomy within Dutch government institutions.
I will be co-teaching Designing Responsible AI again in Q1 of the next academic year.
I’ll serve as an associate chair on the CHI 2026 design subcommittee.
And I have signed up to begin our university’s teaching qualification certification.
Whew. That’s it. Thanks for reading (skimming?) if you’ve made it all the way to the end. I will try to circle back and do another update, maybe a little sooner than this one, say in six months’ time.
In my thesis, I use autonomy to build the normative case for contestability. It so happens that this year’s theme at the Delft Design for Values Institute is also autonomy. On October 15, 2024, I participated in a panel discussion on autonomy to kick things off. I collected some notes on autonomy that go beyond the conceptualization I used in my thesis. I thought it might be helpful and interesting to collect some of them here in adapted form.
The notes I brought included, first of all, a summary of the ecumenical conceptualization of autonomy concerning automated decision-making systems offered by Alan Rubel, Clinton Castro, and Adam Pham (2021). They conceive of autonomy as effective self-governance. To be autonomous, we need authentic beliefs about our circumstances and the agency to act on our plans. Regarding algorithmic systems, they offer this notion of a reasonable endorsement test—the degree to which a system can be said to respect autonomy depends on its reliability, the stakes of its outputs, the degree to which subjects can be held responsible for inputs, and the distribution of burdens across groups.
Second, I collected some notes from several pieces by James Muldoon, which get into notions of freedom and autonomy that were developed in socialist republican thought by the likes of Luxemburg, Kautsky, and Castoriadis (2020, 2021a, 2021b). This story of autonomy is sociopolitical rather than moral. This approach is quite appealing for someone interested in non-ideal theory in a realist mode like myself. The account of autonomy Muldoon offers is one where individual autonomy hinges on greater group autonomy and stronger bonds of association between those producing and consuming technologies. Freedom is conceived of as collective self-determination.
And then third and finally, there’s this connected idea of relational autonomy, which to a degree is part of the account offered by Rubel et al., but in the conceptions here more radical in how it seeks to create distance from liberal individualism (e.g., Christman, 2004; Mhlambi & Tiribelli, 2023; Westlund, 2009). In this, individual capacity for autonomous choice is shaped by social structures. So freedom becomes realized through networks of care, responsibility, and interdependence.
That’s what I am interested in: accounts of autonomy that are not premised on liberal individualism and that give us some alternative handle on the problem of the social control of technology in general and of AI in particular.
From my point of view, the implications of all this for design and AI include the following.
First, to make a fairly obvious but often overlooked point, the degree to which a given system impacts people’s autonomy depends on various factors. It makes little sense to make blanket statements about AI destroying our autonomy and so on.
Second, in value-sensitive design terms, you can think about autonomy as a value to be balanced against others—in the case where you take the position that all values can be considered equally important, at least in principle. Or you can consider autonomy more like a precondition for people to live with technology in concordance with their values, making autonomy take precedence over other values. The sociopolitical and relational accounts above point in this direction.
Third, suppose you buy into the radical democratic idea of technology and autonomy. In that case, it follows that it makes little sense to admonish individual designers about respecting others’ autonomy. They may be asked to privilege technologies in their designs that afford individual and group autonomy. But designers also need organization and emancipation more often than not. So it’s about building power. The power of workers inside the organizations that develop technologies and the power of communities that “consume” those same technologies.
With AI, the fact is that, in reality, in the cases I look at, the communities that AI is brought to bear on have little say in the matter. The buyers and deployers of AI could and should be made more accountable to the people subjected to AI.
This is a version of the opening statement I contributed to the panel “Evolving Perspectives on AI and Design” at the Design & AI symposium that was part of Dutch Design Week 2024. I had the pleasure of joining Iohanna Nicenboim and Jesse Benjamin on stage to explore what could be called the post-GenAI possibility space for design. Thanks also to Mathias Funk for moderating.
The slide I displayed:
My statement:
There’s a lot of magical thinking in the AI field today. It assumes intelligence is latent in the structure of the internet. Metaphors like AGI and superintelligence are magical in nature. AI practice is also very secretive. It relies on demonstrations. This leads to a lack of rigor and political accountability (cf. Gilbert & Lambert in VentureBeat, 2023).
Design in its idealist mode is easily fooled by such magic. For example, in a recent report, the Dutch Court of Audit states that 35% of government AI systems are not known to meet expectations (cf. Raji et al., 2022).
What is needed is design in a realist mode. Realism focuses on who does what to whom in whose interest (cf. Geuss, 2008, 23 in von Busch & Palmås, 2023). Applied to AI the question becomes who gets to do AI to whom? This isn’t to say we should consider AI technologies completely inert. It mediates our being in the world (Verbeek, 2021). But we should also not consider it an independent force that’s just dragging us along.
The challenge is to steer a path between, on the one hand, wholesale cynical rejection and naive, optimistic, unconditional embrace, on the other hand.
In my own work, what that looks like is to use design to make things that allow me to go into situations where people are building and using AI systems. And to use those things as instruments to ask questions related to human autonomy, social control, and collective freedom in the face of AI.
The example shown is an animated short depicting a design fiction scenario involving intelligent camera cars used for policy execution in urban public space. I used this video to talk to civil servants about the challenges facing governments who want to ensure citizens remain in control of the AI systems they deploy (cf. Alfrink et al., 2023).
Why is this realist? Because the work looks at how some groups of people use particular forms of actually existing AI to do things to other people. The work also foregrounds the competing interests that are at stake. And it frames AI as neither fully autonomous nor fully passive, but as a thing that mediates peoples’ perceptions and actions.
There are more examples besides this. But I will stop here. I just want to reiterate that I think we need a realist approach to the design of AI.
At a TU Delft spring symposium on AI education, Hosana and I ran a short workshop titled “AI pedagogy through a design lens.” In it, we identified some of the challenges facing AI teaching, particularly outside of computer science, and explored how design pedagogy, particularly the practices of studios and making, may help to address them. The AI & Society master elective I’ve been developing and teaching over the past five years served as a case study. The session was punctuated by brief brainstorming using an adapted version of the SQUID gamestorming technique. Below are the slides we used.
I’ll be at Beyond Smart Cities Today the next couple of days (18-19 September). Below is the abstract I submitted, plus a bibliography of some of the stuff that went into my thinking for this and related matters that I won’t have the time to get into.
In the actually existing smart city, algorithmic systems are increasingly used for the purposes of automated decision-making, including as part of public infrastructure. Algorithmic systems raise a range of ethical concerns, many of which stem from their opacity. As a result, prescriptions for improving the accountability, trustworthiness and legitimacy of algorithmic systems are often based on a transparency ideal. The thinking goes that if the functioning and ownership of an algorithmic system is made perceivable, people understand them and are in turn able to supervise them. However, there are limits to this approach. Algorithmic systems are complex and ever-changing socio-technical assemblages. Rendering them visible is not a straightforward design and engineering task. Furthermore such transparency does not necessarily lead to understanding or, crucially, the ability to act on this understanding. We believe legitimate smart public infrastructure needs to include the possibility for subjects to articulate objections to procedures and outcomes. The resulting “contestable infrastructure” would create spaces that open up the possibility for expressing conflicting views on the smart city. Our project is to explore the design implications of this line of reasoning for the physical assets that citizens encounter in the city. Because after all, these are the perceivable elements of the larger infrastructural systems that recede from view.
Alkhatib, A., & Bernstein, M. (2019). Street-Level Algorithms. 1–13. https://doi.org/10.1145/3290605.3300760
Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media and Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645
Centivany, A., & Glushko, B. (2016). “Popcorn tastes good”: Participatory policymaking and Reddit’s “AMAgeddon.” Conference on Human Factors in Computing Systems – Proceedings, 1126–1137. https://doi.org/10.1145/2858036.2858516
Crawford, K. (2016). Can an Algorithm be Agonistic? Ten Scenes from Life in Calculated Publics. Science Technology and Human Values, 41(1), 77–92. https://doi.org/10.1177/0162243915589635
DiSalvo, C. (2010). Design, Democracy and Agonistic Pluralism. Proceedings of the Design Research Society Conference, 366–371.
Hildebrandt, M. (2017). Privacy As Protection of the Incomputable Self: Agonistic Machine Learning. SSRN Electronic Journal, 1–33. https://doi.org/10.2139/ssrn.3081776
Jackson, S. J., Gillespie, T., & Payette, S. (2014). The Policy Knot: Re-integrating Policy, Practice and Design. CSCW Studies of Social Computing, 588–602. https://doi.org/10.1145/2531602.2531674
Jewell, M. (2018). Contesting the decision: living in (and living with) the smart city. International Review of Law, Computers and Technology. https://doi.org/10.1080/13600869.2018.1457000
Lindblom, L. (2019). Consent, Contestability, and Unions. Business Ethics Quarterly. https://doi.org/10.1017/beq.2018.25
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 205395171667967. https://doi.org/10.1177/2053951716679679
Van de Poel, I. (2016). An ethical framework for evaluating experimental technology. Science and Engineering Ethics, 22(3), 667–686. https://doi.org/10.1007/s11948-015-9724-3
Thijs Turèl of AMS Institute and myself presented a version of the talk below at the Cities for Digital Rights conference on June 19 in Amsterdam during the We Make the City festival. The talk is an attempt to articulate some of the ideas we both have been developing for some time around contestability in smart public infrastructure. As always with this sort of thing, this is intended as a conversation piece so I welcome any thoughts you may have.
The basic message of the talk is that when we start to do automated decision-making in public infrastructure using algorithmic systems, we need to design for the inevitable disagreements that may arise and furthermore, we suggest there is an opportunity to focus on designing for such disagreements in the physical objects that people encounter in urban space as they make use of infrastructure.
We set the scene by showing a number of examples of smart public infrastructure. A cyclist crossing that adapts to weather conditions. If it’s raining cyclists more frequently get a green light. A pedestrian crossing in Tilburg where elderly can use their mobile to get more time to cross. And finally, the case we are involved with ourselves: smart EV charging in the city of Amsterdam, about which more later.
We identify three trends in smart public infrastructure: (1) where previously algorithms were used to inform policy, now they are employed to perform automated decision-making on an individual case basis. This raises the stakes; (2) distributed ownership of these systems as the result of public-private partnerships and other complex collaboration schemes leads to unclear responsibility; and finally (3) the increasing use of machine learning leads to opaque decision-making.
These trends, and algorithmic systems more generally, raise a number of ethical concerns. They include but are not limited to: the use of inductive correlations (for example in the case of machine learning) leads to unjustified results; lack of access to and comprehension of a system’s inner workings produces opacity, which in turn leads to a lack of trust in the systems themselves and the organisations that use them; bias is introduced by a number of factors, including development team prejudices, technical flaws, bad data and unforeseen interactions with other systems; and finally the use of profiling, nudging and personalisation leads to diminished human agency. (We highly recommend the article by Mittelstadt et al. for a comprehensive overview of ethical concerns raised by algorithms.)
So for us, the question that emerges from all this is: How do we organise the supervision of smart public infrastructure in a democratic and lawful way?
There are a number of existing approaches to this question. These include legal and regulatory (e.g. the right to explanation in the GDPR); auditing (e.g. KPMG’s “AI in Control” method, BKZ’s transparantielab); procurement (e.g. open source clauses); insourcing (e.g. GOV.UK) and design and engineering (e.g. our own work on the transparent charging station).
We feel there are two important limitations with these existing approaches. The first is a focus on professionals and the second is a focus on prediction. We’ll discuss each in turn.
First of all, many solutions target a professional class, be it accountants, civil servants, supervisory boards, as well as technologists, designers and so on. But we feel there is a role for the citizen as well, because the supervision of these systems is simply too important to be left to a privileged few. This role would include identifying wrongdoing, and suggesting alternatives.
There is a tension here, which is that from the perspective of the public sector one should only ask citizens for their opinion when you have the intention and the resources to actually act on their suggestions. It can also be a challenge to identify legitimate concerns in the flood of feedback that can sometimes occur. From our point of view though, such concerns should not be used as an excuse to not engage the public. If citizen participation is considered necessary, the focus should be on freeing up resources and setting up structures that make it feasible and effective.
The second limitation is prediction. This is best illustrated with the Collinridge dilemma: in the early phases of new technology, when a technology and its social embedding are still malleable, there is uncertainty about the social effects of that technology. In later phases, social effects may be clear but then often the technology has become so well entrenched in society that it is hard to overcome negative social effects. (This summary is taken from an excellent van de Poel article on the ethics of experimental technology.)
Many solutions disregard the Collingridge dilemma and try to predict and prevent adverse effects of new systems at design-time. One example of this approach would be value-sensitive design. Our focus in stead is on use-time. Considering the fact that smart public infrastructure tends to be developed on an ongoing basis, the question becomes how to make citizens a partner in this process. And even more specifically we are interested in how this can be made part of the design of the “touchpoints” people actually encounter in the streets, as well as their backstage processes.
Why do we focus on these physical objects? Because this is where people actually meet the infrastructural systems, of which large parts recede from view. These are the places where they become aware of their presence. They are the proverbial tip of the iceberg.
The use of automated decision-making in infrastructure reduces people’s agency. For this reason, resources for agency need to be designed back into these systems. Frequently the answer to this question is premised on a transparency ideal. This may be a prerequisite for agency, but it is not sufficient. Transparency may help you become aware of what is going on, but it will not necessarily help you to act on that knowledge. This is why we propose a shift from transparency to contestability. (We can highly recommend Ananny and Crawford’s article for more on why transparency is insufficient.)
To clarify what we mean by contestability, consider the following three examples: When you see the lights on your router blink in the middle of the night when no-one in your household is using the internet you can act on this knowledge by yanking out the device’s power cord. You may never use the emergency brake in a train but its presence does give you a sense of control. And finally, the cash register receipt provides you with a view into both the procedure and the outcome of the supermarket checkout procedure and it offers a resource with which you can dispute them if something appears to be wrong.
Image credits: Aangiftedoen, source unknown for remainder
None of these examples is a perfect illustration of contestability but they hint at something more than transparency, or perhaps even something wholly separate from it. We’ve been investigating what their equivalents would be in the context of smart public infrastructure.
To illustrate this point further let us come back to the smart EV charging project we mentioned earlier. In Amsterdam, public EV charging stations are becoming “smart” which in this case means they automatically adapt the speed of charging to a number of factors. These include grid capacity, and the availability of solar energy. Additional factors can be added in future, one of which under consideration is to give priority to shared cars over privately owned cars. We are involved with an ongoing effort to consider how such charging stations can be redesigned so that people understand what’s going on behind the scenes and can act on this understanding. The motivation for this is that if not designed carefully, the opacity of smart EV charging infrastructure may be detrimental to social acceptance of the technology. (A first outcome of these efforts is the Transparent Charging Station designed by The Incredible Machine. A follow-up project is ongoing.)
We have identified a number of different ways in which people may object to smart EV charging. They are listed in the table below. These types of objections can lead us to feature requirements for making the system contestable.
Because the list is preliminary, we asked the audience if they could imagine additional objections, if those examples represented new categories, and if they would require additional features for people to be able to act on them. One particularly interesting suggestion that emerged was to give local communities control over the policies enacted by the charge points in their vicinity. That’s something to further consider the implications of.
And that’s where we left it. So to summarise:
Algorithmic systems are becoming part of public infrastructure.
Smart public infrastructure raises new ethical concerns.
Many solutions to ethical concerns are premised on a transparency ideal, but do not address the issue of diminished agency.
There are different categories of objections people may have to an algorithmic system’s workings.
Making a system contestable means creating resources for people to object, opening up a space for the exploration of meaningful alternatives to its current implementation.
