Thought I’d post a quick update on my PhD. Since my previous post almost five months have passed. I’ve been developing my plan further, for which you’ll find an updated description below. I’ve also put together my very first conference paper, co-authored with my supervisor Gerd Kortuem. It’s a case study of the MX3D smart bridge for Designing Interactive Systems 2019. We’ll see if it gets accepted. But in any case, writing something has been hugely educational. And once I finally figured out what the hell I was doing, it was sort of fun as well. Still kind of a trip to be paid to do this kind of work. Looking ahead, I am setting goals for this year and the nearer term as well. It’s all very rough still but it will likely involve research through design as a method and maybe object oriented ontology as a theory. All of which will serve to operationalise and evaluate the usefulness of the “contestability” concept in the context of smart city infrastructure. To be continued—and I welcome all your thoughts!
Designing Smart City Infrastructure for Contestability
The use of information technology in cities increasingly subjects citizens to automated data collection, algorithmic decision making and remote control of physical space. Citizens tend to find these systems and their outcomes hard to understand and predict . Moreover, the opacity of smart urban systems precludes full citizenship and obstructs people’s ‘right to the city’ .
A commonly proposed solution is to improve citizens understanding of systems by making them more open and transparent . For example, GDPR prescribes people’s right to explanation of automated decisions they have been subjected to. For another example, the city of Amsterdam offers a publicly accessible register of urban sensors, and is committed to opening up all the data they collect.
However, it is not clear that openness and transparency in and of itself will yield the desired improvements in understanding and governing of smart city infrastructures . We would like to suggest that for a system to perceived as accountable, people must be able to contest its workings—from the data it collects, to the decisions it makes, all the way through to how those decisions are acted on in the world.
The leading research question for this PhD therefore is how to design smart city infrastructure—urban systems augmented with internet-connected sensing, processing and actuating capabilities—for contestability : the extent to which a system supports the ability of those subjected to it to oppose its workings as wrong or mistaken.
Burrell, Jenna. “How the machine ‘thinks’: Understanding opacity in machine learning algorithms.” Big Data & Society 3.1 (2016): 2053951715622512.
Kitchin, Rob, Paolo Cardullo, and Cesare Di Feliciantonio. “Citizenship, Justice and the Right to the Smart City.” (2018).
Abdul, Ashraf, et al. “Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda.” Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 2018.
Ananny, Mike, and Kate Crawford. “Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability.” New Media & Society 20.3 (2018): 973–989.
Hirsch, Tad, et al. “Designing contestability: Interaction design, machine learning, and mental health.” Proceedings of the 2017 Conference on Designing Interactive Systems. ACM, 2017.
Goodreads tells me I’ve read 48 books in 2018. I set myself the goal of 36 so it looks like I beat it handily. But included in that count are quite a few roleplaying game books and comics. If I discard those I’m left with 28 titles. Still a decent amount but nothing particularly remarkable. Below are a few lists and some notes to go with them.
Most of the non-fiction is somewhere on the intersection of design, technology and Left politics. A lot of this reading was driven by my desire to develop some kind of mental framework for the work we were doing with Tech Solidarity NL. More recently—since I started my PhD—I’ve mostly been reading textbooks on research methodology. Hidden from this list is the academic papers I’ve started consuming as part of this new job. I should figure out a way of sharing some of that here or elsewhere as well.
I took a break from technology and indulged in a deep dive into the history of the thirty year’s war with a massive non-fiction treatment as well as a classic picaresque set in the same time period. While reading these I was transitioning into my new role as a father of twin boys. Somewhat related was a brief history of The Netherlands, which I’ve started recommending to foreigners who are struggling to understand our idiosyncratic little nation and go beyond superficialities.
Then there’s the fiction, which in the beginning of the year consisted of highbrow weird and historical novels but then ventured into classic fantasy and (utopian) sci-fi territory. Again, mostly because of a justifiable desire for some escapism in the sleep deprived evenings and nights.
Having mentioned the arrival of our boys a few times it should come as no surprise that I also read a couple of parenting books. These were more than enough for me and really to be honest I think parenting is a thing best learned through practice. Especially if you’re raising two babies at once.
So that’s it. I’ve set myself the modest goal of 24 books for this year because I’m quite sure most of my reading will be papers and such. Here’s to a year of what I expect will be many more late night and early morning reading sessions of escapist weird fiction.
I’d like to talk about the future of our design practice and what I think we should focus our attention on. It is all related to this idea of complexity and opening up black boxes. We’re going to take the scenic route, though. So bear with me.
Two years ago I spent about half a year in Singapore.
While there I worked as product strategist and designer at a startup called ARTO, an art recommendation service. It shows you a random sample of artworks, you tell it which ones you like, and it will then start recommending pieces it thinks you like. In case you were wondering: yes, swiping left and right was involved.
We had this interesting problem of ingesting art from many different sources (mostly online galleries) with metadata of wildly varying levels of quality. So, using metadata to figure out which art to show was a bit of a non-starter. It should come as no surprise then, that we started looking into machine learning—image processing in particular.
And so I found myself working with my engineering colleagues on an art recommendation stream which was driven at least in part by machine learning. And I quickly realised we had a problem. In terms of how we worked together on this part of the product, it felt like we had taken a bunch of steps back in time. Back to a way of collaborating that was less integrated and less responsive.
That’s because we have all these nice tools and techniques for designing traditional software products. But software is deterministic. Machine learning is fundamentally different in nature: it is probabilistic.
It was hard for me to take the lead in the design of this part of the product for two reasons. First of all, it was challenging to get a first-hand feel of the machine learning feature before it was implemented.
And second of all, it was hard for me to communicate or visualise the intended behaviour of the machine learning feature to the rest of the team.
So when I came back to the Netherlands I decided to dig into this problem of design for machine learning. Turns out I opened up quite the can of worms for myself. But that’s okay.
There are two reasons I care about this:
The first is that I think we need more design-led innovation in the machine learning space. At the moment it is engineering-dominated, which doesn’t necessarily lead to useful outcomes. But if you want to take the lead in the design of machine learning applications, you need a firm handle on the nature of the technology.
The second reason why I think we need to educate ourselves as designers on the nature of machine learning is that we need to take responsibility for the impact the technology has on the lives of people. There is a lot of talk about ethics in the design industry at the moment. Which I consider a positive sign. But I also see a reluctance to really grapple with what ethics is and what the relationship between technology and society is. We seem to want easy answers, which is understandable because we are all very busy people. But having spent some time digging into this stuff myself I am here to tell you: There are no easy answers. That isn’t a bug, it’s a feature. And we should embrace it.
At the end of 2016 I attended ThingsCon here in Amsterdam and I was introduced by Ianus Keller to TU Delft PhD researcher Péter Kun. It turns out we were both interested in machine learning. So with encouragement from Ianus we decided to put together a workshop that would enable industrial design master students to tangle with it in a hands-on manner.
About a year later now, this has grown into a thing we call Prototyping the Useless Butler. During the workshop, you use machine learning algorithms to train a model that takes inputs from a network-connected arduino’s sensors and drives that same arduino’s actuators. In effect, you can create interactive behaviour without writing a single line of code. And you get a first hand feel for how common applications of machine learning work. Things like regression, classification and dynamic time warping.
The thing that makes this workshop tick is an open source software application called Wekinator. Which was created by Rebecca Fiebrink. It was originally aimed at performing artists so that they could build interactive instruments without writing code. But it takes inputs from anything and sends outputs to anything. So we appropriated it towards our own ends.
The thinking behind this workshop is that for us designers to be able to think creatively about applications of machine learning, we need a granular understanding of the nature of the technology. The thing with designers is, we can’t really learn about such things from books. A lot of design knowledge is tacit, it emerges from our physical engagement with the world. This is why things like sketching and prototyping are such essential parts of our way of working. And so with useless butler we aim to create an environment in which you as a designer can gain tacit knowledge about the workings of machine learning.
Simply put, for a lot of us, machine learning is a black box. With Useless Butler, we open the black box a bit and let you peer inside. This should improve the odds of design-led innovation happening in the machine learning space. And it should also help with ethics. But it’s definitely not enough. Knowledge about the technology isn’t the only issue here. There are more black boxes to open.
Which brings me back to that other black box: ethics. Like I already mentioned there is a lot of talk in the tech industry about how we should “be more ethical”. But things are often reduced to this notion that designers should do no harm. As if ethics is a problem to be fixed in stead of a thing to be practiced.
So I started to talk about this to people I know in academia and more than once this thing called Value Sensitive Design was mentioned. It should be no surprise to anyone that scholars have been chewing on this stuff for quite a while. One of the earliest references I came across, an essay by Batya Friedman in Interactions is from 1996! This is a lesson to all of us I think. Pay more attention to what the academics are talking about.
So, at the end of last year I dove into this topic. Our host Iskander Smit, Rob Maijers and myself coordinate a grassroots community for tech workers called Tech Solidarity NL. We want to build technology that serves the needs of the many, not the few. Value Sensitive Design seemed like a good thing to dig into and so we did.
I’m not going to dive into the details here. There’s a report on the Tech Solidarity NL website if you’re interested. But I will highlight a few things that value sensitive design asks us to consider that I think help us unpack what it means to practice ethical design.
First of all, values. Here’s how it is commonly defined in the literature:
“A value refers to what a person or group of people consider important in life.”
I like it because it’s common sense, right? But it also makes clear that there can never be one monolithic definition of what ‘good’ is in all cases. As we designers like to say: “it depends” and when it comes to values things are no different.
“Person or group” implies there can be various stakeholders. Value sensitive design distinguishes between direct and indirect stakeholders. The former have direct contact with the technology, the latter don’t but are affected by it nonetheless. Value sensitive design means taking both into account. So this blows up the conventional notion of a single user to design for.
Various stakeholder groups can have competing values and so to design for them means to arrive at some sort of trade-off between values. This is a crucial point. There is no such thing as a perfect or objectively best solution to ethical conundrums. Not in the design of technology and not anywhere else.
Value sensitive design encourages you to map stakeholders and their values. These will be different for every design project. Another approach is to use lists like the one pictured here as an analytical tool to think about how a design impacts various values.
Furthermore, during your design process you might not only think about the short-term impact of a technology, but also think about how it will affect things in the long run.
And similarly, you might think about the effects of a technology not only when a few people are using it, but also when it becomes wildly successful and everybody uses it.
There are tools out there that can help you think through these things. But so far much of the work in this area is happening on the academic side. I think there is an opportunity for us to create tools and case studies that will help us educate ourselves on this stuff.
There’s a lot more to say on this but I’m going to stop here. The point is, as with the nature of the technologies we work with, it helps to dig deeper into the nature of the relationship between technology and society. Yes, it complicates things. But that is exactly the point.
Privileging simple and scalable solutions over those adapted to local needs is socially, economically and ecologically unsustainable. So I hope you will join me in embracing complexity.
Today is the first official work day of my new doctoral researcher position at Delft University of Technology. After more than two years of laying the ground work, I’m starting out on a new challenge.
I remember sitting outside a Jewel coffee bar in Singapore1 and going over the various options for whatever would be next after shutting down Hubbub. I knew I wanted to delve into the impact of machine learning and data science on interaction design. And largely through process of elimination I felt the best place for me to do so would be inside of academia.
Back in the Netherlands, with help from Ianus Keller, I started making inroads at TU Delft, my first choice for this kind of work. I had visited it on and off over the years, coaching students and doing guest lectures. I’d felt at home right away.
There were quite a few twists and turns along the way but now here we are. Starting this month I am a doctoral candidate at Delft University of Technology’s faculty of Industrial Design Engineering.
Below is a first rough abstract of the research. But in the months to come this is likely to change substantially as I start hammering out a proper research plan. I plan to post the occasional update on my work here, so if you’re interested your best bet is probably to do the old RSS thing. There’s social media too, of course. And I might set up a newsletter at some point. We’ll see.
If any of this resonates, do get in touch. I’d love to start a conversation with as many people as possible about this stuff.
Intelligibility and Transparency of Smart Public Infrastructures: A Design Oriented Approach
This phd will explore how designers, technologists, and citizens can utilize rapid urban manufacturing and IoT technologies for designing urban space that expresses its intelligence from the intersection of people, places, activities and technology, not merely from the presence of cutting-edge technology. The key question is how smart public infrastructure, i.e. data-driven and algorithm-rich public infrastructures, can be understood by lay-people.
The design-oriented research will utilize a ‘research through design’ approach to develop a digital experience around the bridge and the surrounding urban space. During this extended design and making process the phd student will conduct empirical research to investigate design choices and their implications on (1) new forms of participatory data-informed design processes, (2) the technology-mediated experience of urban space, (3) the emerging relationship between residents and “their” bridge, and (4) new forms of data-informed, citizen led governance of public space.
At a recent Tech Solidarity NL meetup we dove into Value Sensitive Design. This approach had been on my radar for a while so when we concluded that for our community it would be useful to talk about how to practice ethical design and development of technology, I figured we should check it out.
Below, I have attempted to pull together the most salient points from what is a rather dense twenty-plus-slides deck. I hope it is of some use to those professional designers and developers who are looking for better ways of building technology that serves the interest of the many, not the few.
The departure point is the observation that “there is a need for an overarching theoretical and methodological framework with which to handle the value dimensions of design work.” In other words, something that accounts for what we already know about how to deal with values in design work in terms of theory and concepts, as well as methods and techniques.
This is of course not a new concern. For example, famed cyberneticist Norbert Wiener argued that technology could help make us better human beings, and create a more just society. But for it to do so, he argued, we have to take control of the technology.
We have to reject the “worshiping [of] the new gadgets which are our own creation as if they were our masters.” (Wiener 1953)
We can find many more similar arguments throughout the history of information technology. Recently such concerns have flared up in industry as well as society at large. (Not always for the right reasons in my opinion, but that is something we will set aside for now.)
To address these concerns, Value Sensitive Design was developed. It is “a theoretically grounded approach to the design of technology that accounts for human values in a principled and comprehensive manner throughout the design process.” It has been applied successfully for over 20 years.
But what is a value? In the literature it is defined as “what a person or group of people consider important in life.” I like this definition because it is easy to grasp but also underlines the slippery nature of values. Some things to keep in mind when talking about values:
In a narrow sense, the word “value” refers simply to the economic worth of an object. This is not the meaning employed by Value Sensitive Design.
Values should not be conflated with facts (the “fact/value distinction”) especially insofar as facts do not logically entail value.
“Is” does not imply “ought” (the naturalistic fallacy).
Values cannot be motivated only by an empirical account of the external world, but depend substantively on the interests and desires of human beings within a cultural milieu. (So contrary to what some right-wingers like to say: “Facts do care about your feelings.”)
Let’s dig into the way this all works. “Value Sensitive Design is an iterative methodology that integrates conceptual, empirical, and technical investigations.” So it distinguishes between three types of activities (“investigations”) and it prescribes cycling through these activities multiple times. Below are listed questions and notes that are relevant to each type of investigation. But in brief, this is how I understand them:
Defining the specific values at play in a project;
Observing, measuring, and documenting people’s behaviour and the context of use;
Analysing the ways in which a particular technology supports or hinders particular values.
Who are the direct and indirect stakeholders affected by the design at hand?
How are both classes of stakeholders affected?
What values are implicated?
How should we engage in trade-offs among competing values in the design, implementation, and use of information systems (e.g., autonomy vs. security, or anonymity vs. trust)?
Should moral values (e.g., a right to privacy) have greater weight than, or even trump, non-moral values (e.g., aesthetic preferences)?
How do stakeholders apprehend individual values in the interactive context?
How do they prioritise competing values in design trade-offs?
How do they prioritise individual values and usability considerations?
Are there differences between espoused practice (what people say) compared with actual practice (what people do)?
And, specifically focusing on organisations:
What are organisations’ motivations, methods of training and dissemination, reward structures, and economic incentives?
Not a list of questions here, but some notes:
Value Sensitive Design takes the position that technologies in general, and information and computer technologies in particular, have properties that make them more or less suitable for certain activities. A given technology more readily supports certain values while rendering other activities and values more difficult to realise.
Technical investigations involve the proactive design of systems to support values identified in the conceptual investigation.
Technical investigations focus on the technology itself. Empirical investigations focus on the individuals, groups, or larger social systems that configure, use, or are otherwise affected by the technology.
Value Sensitive Design enlarges the arena in which values arise to include not only the work place
Value Sensitive Design contributes a unique methodology that employs conceptual, empirical, and technical investigations, applied iteratively and integratively
Value Sensitive Design enlarges the scope of human values beyond those of cooperation (CSCW) and participation and democracy (Participatory Design) to include all values, especially those with moral import.
Value Sensitive Design distinguishes between usability and human values with ethical import.
Value Sensitive Design identifies and takes seriously two classes of stakeholders: direct and indirect.
Value Sensitive Design is an interactional theory
Value Sensitive Design builds from the psychological proposition that certain values are universally held, although how such values play out in a particular culture at a particular point in time can vary considerably
[ad 4] “By moral, we refer to issues that pertain to fairness, justice, human welfare and virtue, […] Value Sensitive Design also accounts for conventions (e.g., standardisation of protocols) and personal values”
[ad 5] “Usability refers to characteristics of a system that make it work in a functional sense, […] not all highly usable systems support ethical values”
[ad 6] “Often, indirect stakeholders are ignored in the design process.”
[ad 7] “values are viewed neither as inscribed into technology (an endogenous theory), nor as simply transmitted by social forces (an exogenous theory). […] the interactional position holds that while the features or properties that people design into technologies more readily support certain values and hinder others, the technology’s actual use depends on the goals of the people interacting with it. […] through human interaction, technology itself changes over time.”
[ad 8] “the more concretely (act-based) one conceptualises a value, the more one will be led to recognising cultural variation; conversely, the more abstractly one conceptualises a value, the more one will be led to recognising universals”
Value Sensitive Design doesn’t prescribe a particular process, which is fine by me, because I believe strongly in tailoring your process to the particular project at hand. Part of being a thoughtful designer is designing a project’s process as well. However, some guidance is offered for how to proceed in most cases. Here’s a list, plus some notes.
Start with a value, technology, or context of use
Identify direct and indirect stakeholders
Identify benefits and harms for each stakeholder group
Map benefits and harms onto corresponding values
Conduct a conceptual investigation of key values
Identify potential value conflicts
Integrate value considerations into one’s organisational structure
[ad 1] “We suggest starting with the aspect that is most central to your work and interests.”
[ad 2] “direct stakeholders are those individuals who interact directly with the technology or with the technology’s output. Indirect stakeholders are those individuals who are also impacted by the system, though they never interact directly with it. […] Within each of these two overarching categories of stakeholders, there may be several subgroups. […] A single individual may be a member of more than one stakeholder group or subgroup. […] An organisational power structure is often orthogonal to the distinction between direct and indirect stakeholders.”
[ad 3] “one rule of thumb in the conceptual investigation is to give priority to indirect stakeholders who are strongly affected, or to large groups that are somewhat affected […] Attend to issues of technical, cognitive, and physical competency. […] personas have a tendency to lead to stereotypes because they require a list of “socially coherent” attributes to be associated with the “imagined individual.” […] we have deviated from the typical use of personas that maps a single persona onto a single user group, to allow for a single persona to map onto to multiple stakeholder groups”
[ad 4] “In some cases, the corresponding values will be obvious, but not always.”
[ad 5] “the philosophical ontological literature can help provide criteria for what a value is, and thereby how to assess it empirically.”
[ad 6] “value conflicts should usually not be conceived of as “either/or” situations, but as constraints on the design space.”
[ad 7] “In the real world, of course, human values (especially those with ethical import) may collide with economic objectives, power, and other factors. However, even in such situations, Value Sensitive Design should be able to make positive contributions, by showing alternate designs that better support enduring human values.”
This table is a useful heuristic tool for values that might be considered. The authors note that it is not intended as a complete list of human values that might be implicated. Another more elaborate tool of a similar sort are the Envisioning Cards.
For the ethics nerds, it may be interesting to note that most of the values in this table hinge on the deontological and consequentialist moral orientations. In addition, the authors have chose several other values related to system design.
When doing the empirical investigations you’ll probably rely on stakeholder interviews quite heavily. Stakeholder interviews shouldn’t be a new thing to any design professional worth their salt. But the authors do offer some practical pointers to keep in mind.
First of all, keep the interview somewhat open-ended. This means conducting a semi-structured interview. This will allow you to ask the things you want to know, but also creates the opportunity for new and unexpected insights to emerge.
Laddering—repeatedly asking the question “Why?” can get you quite far.
The most important thing, before interviewing stakeholders, is to have a good understanding of the subject at hand. Demarcate it using criteria that can be explained to outsiders. Use descriptions of issues or tasks for participants to engage in, so that the subject of the investigation becomes more concrete.
Two things I find interesting here. First of all, we are encouraged to map the relationship between design trade-offs, value conflicts and stakeholder groups. The goal of this exercise is to be able to see how stakeholder groups are affected in different ways.
The second useful suggestion for technical investigations is to build flexibility into a product or service’s technical infrastructure. The reason for this is that over time, new values and value conflicts can emerge. As designers we are not always around anymore once a system is deployed so it is good practice to enable the stakeholders to adapt our design to their evolving needs. (I was very much reminded of the approach advocated by Stewart Brand in How Buildings Learn.)
When discussing matters of ethics in design with peers I often notice a reluctance to widen the scope of our practice to include these issues. Frequently, folks argue that since it is impossible to foresee all the potential consequences of design choices, we can’t possibly be held accountable for all the terrible things that can happen as a result of a new technology being introduced into society.
I think that’s a misunderstanding of what ethical design is about. We may not always be directly responsible for the consequences of our design (both good and bad). But we are responsible for what we choose to make part of our concerns as we practice design. This should include the values considered important by the people impacted by our designs.
In the 1996 article mentioned at the start of this post, Friedman concludes as follows:
“As with the traditional criteria of reliability, efficiency, and correctness, we do not require perfection in value-sensitive design, but a commitment. And progress.” (Friedman 1996)
I think that is an apt place to end it here as well.
Friedman, Batya, Peter Kahn, and Alan Borning. “Value sensitive design: Theory and methods.” University of Washington technical report (2002): 02–12.
Le Dantec, Christopher A., Erika Shehan Poole, and Susan P. Wyche. “Values as lived experience: evolving value sensitive design in support of value discovery.” Proceedings of the SIGCHI conference on human factors in computing systems.ACM, 2009.
Borning, Alan, and Michael Muller. “Next steps for value sensitive design.” Proceedings of the SIGCHI conference on human factors in computing systems.ACM, 2012.
Freidman, B., P. Kahn, and A. Borning. “Value sensitive design and information systems.” Human–computer interaction in management information systems: Foundations (2006): 348–372.
At the end of last year I was invited to speak at the PLAYTrack conference in Aarhus about the workplace change management games made by Hubbub. It turned out to be a great opportunity to reconnect with the play research community.
I was very much impressed by the program assembled by the organisers. People came from a wide range of disciplines and crucially, there was ample time to discuss and reflect on the materials presented. As I tweeted afterwards, this is a thing that most conference organisers get wrong.
Back in Utrecht after a wonderful time in Århus attending #PLAYTrack. The lectures were uniformly fascinating but the one thing this conference really got right was the ample time to reflect and discuss. Really elevates the experience to something more than the usual info dump.
The abstract for my talk is below, which covers most of what I talked about. I tried to give people a good sense of:
what the games consisted of,
what we were aiming to achieve,
how both the fiction and the player activities supported these goals,
how we made learning outcomes visible to our players and clients,
and finally how we went about designing and developing these games.
Both projects have solid write-ups over at the Hubbub website, so I’ll just point to those here: Code 4 and Ripple Effect.
In the final section of the talk I spent a bit of time reflecting on how I would approach projects like this today. After all, it has been seven years since we made Code 4, and four years since Ripple Effect. That’s ages ago and my perspective has definitely changes since we made these.
First of all, I would get even more serious about co-designing with players at every step. I would recruit representatives of players and invest them with real influence. In the projects we did, the primary vehicle for player influence was through playtesting. But this is necessarily limited. I also won’t pretend this is at all easy to do in a commercial context.
But, these games are ultimately about improving worker productivity. So how do we make it so that workers share in the real-world profits yielded by a successful culture change?
I know of the existence of participatory design but from my experience it is not a common approach in the industry. Why?
Value sensitive design
On a related note, I would get more serious about what values are supported by the system, in whose interest they are and where they come from. Early field research and workshops with audience do surface some values but values from customer representatives tend to dominate. Again, the commercial context we work in is a potential challenge.
I know of value sensitive design, but as with participatory design, it has yet to catch on in a big way in the industry. So again, why is that?
One thing I continue to be interested in is to reduce the complexity of a game system’s physical affordances (which includes its code), and to push even more of the substance of the game into those social allowances that make up the non-material aspects of the game. This allows for spontaneous renegotiation of the game by the players. This is disintermediation as a strategy. David Kanaga’s take on games as toys remains hugely inspirational in this regard, as does Bernard De Koven’s book The Well Played Game.
Gamefulness versus playfulness
Code 4 had more focus on satisfying the need for autonomy. Ripple Effect had more focus on competence, or in any case, it had less emphasis on autonomy. There was less room for ‘play’ around the core digital game. It seems to me that mastering a subjective simulation of a subject is not necessarily what a workplace game for culture change should be aiming for. So, less gameful design, more playful design.
Finally, the agency model does not enable us to stick around for the long haul. But workplace games might be better suited to a setup where things aren’t thought of as a one-off project but more of an ongoing process.
In How Buildings Learn, Stewart Brand talks about how architects should revisit buildings they’ve designed after they are built to learn about how people are actually using them. He also talks about how good buildings are buildings that its inhabitants can adapt to their needs. What does that look like in the context of a game for workplace culture change?
Playful Design for Workplace Change Management
Code 4 (2011, commissioned by the Tax Administration of the Netherlands) and Ripple Effect (2013, commissioned by Royal Dutch Shell) are both games for workplace change management designed and developed by Hubbub, a boutique playful design agency which operated from Utrecht, The Netherlands and Berlin, Germany between 2009 and 2015. These games are examples of how a goal-oriented serious game can be used to encourage playful appropriation of workplace infrastructure and social norms, resulting in an open-ended and creative exploration of new and innovative ways of working.
Serious game projects are usually commissioned to solve problems. Solving the problem of cultural change in a straightforward manner means viewing games as a way to persuade workers of a desired future state. They typically take videogame form, simulating the desired new way of working as determined by management. To play the game well, players need to master its system and by extension—it is assumed—learning happens.
These games can be be enjoyable experiences and an improvement on previous forms of workplace learning, but in our view they decrease the possibility space of potential workplace cultural change. They diminish worker agency, and they waste the creative and innovative potential of involving them in the invention of an improved workplace culture.
We instead choose to view workplace games as an opportunity to increase the space of possibility. We resist the temptation to bake the desired new way of working into the game’s physical and digital affordances. Instead, we leave how to play well up to the players. Since these games are team-based and collaborative, players need to negotiate their way of working around the game among themselves. In addition, because the games are distributed in time—running over a number of weeks—and are playable at player discretion during the workday, players are given license to appropriate workplace infrastructure and subvert social norms towards in-game ends.
We tried to make learning tangible in various ways. Because the games at the core are web applications to which players log on with individual accounts we were able to collect data on player behaviour. To guarantee privacy, employers did not have direct access to game databases and only received anonymised reports. We took responsibility for player learning by facilitating coaching sessions in which they could safely reflect on their game experiences. Rounding out these efforts, we conducted surveys to gain insight into the player experience from a more qualitative and subjective perspective.
These games offer a model for a reasonably democratic and ethical way of doing game-based workplace change management. However, we would like to see efforts that further democratise their design and development—involving workers at every step. We also worry about how games can be used to create the illusion of worker influence while at the same time software is deployed throughout the workplace to limit their agency.
Our examples may be inspiring but because of these developments we feel we can’t continue this type of work without seriously reconsidering our current processes, technology stacks and business practices—and ultimately whether we should be making games at all.
Returning to what is something of an annual tradition, these are the books I’ve read in 2017. I set myself the goal of getting to 36 and managed 38 in the end. They’re listed below with some commentary on particularly memorable or otherwise noteworthy reads. To make things a bit more user friendly I’ve gone with four broad buckets although as you’ll see within each the picks range across genres and subjects.
I always have one piece of fiction or narrative non-fiction going. I have a long-standing ‘project’ of reading cult classics. I can’t settle on a top pick for the first category so it’s going to have to be a tie between Lowry’s alcohol-drenched tale of lost love in pre-WWII Mexico, and Salter’s unmatched lyrical prose treatment of a young couple’s liaisons as imagined by a lecherous recluse in post-WWII France.
When I feel like something lighter I tend to seek out sci-fi written from before I was born. (Contemporary sci-fi more often than not disappoints me with its lack of imagination, or worse, nostalgia for futures past. I’m looking at you, Cline.) My top pick here would be the Strugatsky brothers, who blew me away with their weird tale of a world forever changed by the inexplicable visit by something truly alien.
I’ve also continued to seek out works by women, although I’ve been less strict with myself in this department than previous years. Here I’m ashamed to admit it took me this long to finally read anything by Woolf because Mrs Dalloway is every bit as good as they say it is. I recommend seeking out the annotated Penguin addition for additional insights into the many things she references.
I’ve also sometimes picked up a newer book because it popped up on my radar and I was just really excited about reading it. Most notably Dolan’s retelling of the Iliad in all its glorious, sad and gory detail, updated for today’s sensibilities.
Each time I read a narrative treatment of history or current affairs I feel like I should be doing more of it. All of these are recommended but Kapuściński towers over all with his heart-wrenching first-person account of the Iranian revolution.
A few books on design and technology here, although most of my ‘professional’ reading was confined to academic papers this year. I find those to be a more effective way of getting a handle on a particular subject. Books published on my métier are notoriously fluffy. I’ll point out Löwgren for a tough but rewarding read on how to do interaction design in a non-dogmatic but reflective way.
I got into leftist politics quite heavily this year and tried to educate myself a bit on contemporary anti-capitalist thinking. Fisher’s book is a most interesting and also amusing diagnosis of the current political and economic world system through a cultural lens. It’s a shame he’s no longer with us, I wonder what he would have made of recent events.
I decided to work my way through a bunch of roleplaying game books all ‘powered by the apocalypse’ – a family of games which I have been aware of for quite a while but haven’t had the opportunity to play myself. I like reading these because I find them oddly inspirational for professional purposes. But I will point to the original Apocalypse World as the one must-read as Baker remains one of the designers I am absolutely in awe of for the ways in which he manages to combine system and fiction in truly inventive ways.
The Perilous Wilds, Jason Lutes
Urban Shadows: Political Urban Fantasy Powered by the Apocalypse, Andrew Medeiros
Dungeon World, Sage LaTorra
Apocalypse World, D. Vincent Baker
I don’t usually read poetry for reasons similar to how I basically stopped reading comics earlier: I can’t seem to find a good way of discovering worthwhile things to read. The collection below was a gift, and a delightful one.
As always, I welcome suggestions for what to read next. I’m shooting for 36 again this year and plan to proceed roughly as I’ve been doing lately—just meander from book to book with a bias towards works that are non-anglo, at least as old as I am, and preferably weird or inventive.
We developed three exercises, one for each type of Wekinator output: regression, classification and dynamic time warping.
In contrast to the first version, we had two hours to run through the whole thing, in stead of a day… So we had to cut some corners, and doubled down on walking participants through a number of exercises so that they would come out of it with some readily applicable skills.
We dubbed the workshop ‘prototyping the useless butler’, with thanks to Philip van Allen for the suggestion to frame the exercises around building something non-productive so that the focus was shifted to play and exploration.
All of the code, the circuit diagram and slides are over on GitHub. But I’ll summarise things here.
We spent a very short amount of time introducing machine learning. We used Google’s Teachable Machine as an example and contrasted regular programming with using machine learning algorithms to train models. The point was to provide folks with just enough conceptual scaffolding so that the rest of the workshop would make sense.
We then introduced our ‘toolchain’ which consists of Wekinator, the Arduino MKR1000 module and the OSC protocol. The aim of this toolchain is to allow designers who work in the IoT space to get a feel for the material properties of machine learning through hands-on tinkering. We tried to create a toolchain with as few moving parts as possible, because each additional component would introduce another point of failure which might require debugging. This toolchain would enable designers to either use machine learning to rapidly prototype interactive behaviour with minimal or no programming. It can also be used to prototype products that expose interactive machine learning features to end users. (For a speculative example of one such product, see Bjørn Karmann’s Objectifier.)
Participants were then asked to set up all the required parts on their own workstation. A list can be found on the Useless Butler GitHub page.
We then proceeded to build the circuit. We provided all the components and showed a Fritzing diagram to help people along. The basic idea of this circuit, the eponymous useless butler, was to have a sufficiently rich set of inputs and outputs with which to play, that would suit all three types of Wekinator output. So we settled on a pair of photoresistors or LDRs as inputs and an RGBLED as output.
With the prerequisites installed and the circuit built we were ready to walk through the examples. For regression we mapped the continuous stream of readings from the two LDRs to three outputs, one each for the red, green and blue of the LED. For classification we put the state of both LDRs into one of four categories, each switching the RGBLED to a specific color (cyan, magenta, yellow or white). And finally, for dynamic time warping, we asked Wekinator to recognise one of three gestures and switch the RGBLED to one of three states (red, green or off).
When we reflected on the workshop afterwards, we agreed we now have a proven concept. Participants were able to get the toolchain up and running and could play around with iteratively training and evaluating their model until it behaved as intended.
However, there is still quite a bit of room for improvement. On a practical note, quite a bit of time was taken up by the building of the circuit, which isn’t the point of the workshop. One way of dealing with this is to bring those to a workshop pre-built. Doing so would enable us to get to the machine learning quicker and would open up time and space to also engage with the participants about the point of it all.
We’re keen on bringing this workshop to more settings in future. If we do, I’m sure we’ll find the opportunity to improve on things once more and I will report back here.
Many thanks to Iskander and the rest of the ThingsCon team for inviting us to the conference.
Earlier this year I coached Design for Interaction master students at Delft University of Technology in the course Research Methodology. The students organised three seminars for which I provided the claims and assigned reading. In the seminars they argued about my claims using the Toulmin Model of Argumentation. The readings served as sources for backing and evidence.
The claims and readings were all related to my nascent research project about machine learning. We delved into both designing for machine learning, and using machine learning as a design tool.
Below are the readings I assigned, with some notes on each, which should help you decide if you want to dive into them yourself.
The only non-academic piece in this list. This served the purpose of getting all students on the same page with regards to what machine learning is, its applications of machine learning in interaction design, and common challenges encountered. I still can’t think of any other single resource that is as good a starting point for the subject as this one.
Fiebrink’s Wekinator is groundbreaking, fun and inspiring so I had to include some of her writing in this list. This is mostly of interest for those looking into the use of machine learning for design and other creative and artistic endeavours. An important idea explored here is that tools that make use of (interactive, supervised) machine learning can be thought of as instruments. Using such a tool is like playing or performing, exploring a possibility space, engaging in a dialogue with the tool. For a tool to feel like an instrument requires a tight action-feedback loop.
A really good survey of how designers currently deal with machine learning. Key takeaways include that in most cases, the application of machine learning is still engineering-led as opposed to design-led, which hampers the creation of non-obvious machine learning applications. It also makes it hard for designers to consider ethical implications of design choices. A key reason for this is that at the moment, prototyping with machine learning is prohibitively cumbersome.
The second Fiebrink piece in this list, which is more of a deep dive into how people use Wekinator. As with the chapter listed above this is required reading for those working on design tools which make use of interactive machine learning. An important finding here is that users of intelligent design tools might have very different criteria for evaluating the ‘correctness’ of a trained model than engineers do. Such criteria are likely subjective and evaluation requires first-hand use of the model in real time.
Bostrom, Nick, and Eliezer Yudkowsky. 2014. “The Ethics of Artificial Intelligence.” In The Cambridge Handbook of Artificial Intelligence, edited by Keith Frankish and William M Ramsey, 316–34. Cambridge: Cambridge University Press. doi:10.1017/CBO9781139046855.020.
Bostrom is known for his somewhat crazy but thoughtprovoking book on superintelligence and although a large part of this chapter is about the ethics of general artificial intelligence (which at the very least is still a way out), the first section discusses the ethics of current “narrow” artificial intelligence. It makes for a good checklist of things designers should keep in mind when they create new applications of machine learning. Key insight: when a machine learning system takes on work with social dimensions—tasks previously performed by humans—the system inherits its social requirements.
Finally, a feet-in-the-mud exploration of what it actually means to design for machine learning with the tools most commonly used by designers today: drawings and diagrams of various sorts. In this case the focus is on using machine learning to make an interface adaptive. It includes an interesting discussion of how to balance the use of implicit and explicit user inputs for adaptation, and how to deal with inference errors. Once again the limitations of current sketching and prototyping tools is mentioned, and related to the need for designers to develop tacit knowledge about machine learning. Such tacit knowledge will only be gained when designers can work with machine learning in a hands-on manner.
I provided this to students so that they get some additional grounding in the various kinds of prototyping that are out there. It helps to prevent reductive notions of prototyping, and it makes for a nice complement to Buxton’s work on sketching.
Some of the papers refer to machine learning as a “design material” and this paper helps to understand what that idea means. Software is a material without qualities (it is extremely malleable, it can simulate nearly anything). Yet, it helps to consider it as a physical material in the metaphorical sense because we can then apply ways of design thinking and doing to software programming.
This is not exactly a now page, but I thought I would write up what I am doing at the moment since last reporting on my status in my end-of-year report.
The majority of my workdays are spent doing freelance design consulting. My primary gig has been through Eend at the Dutch Victim Support Foundation, where until very recently I was part of a team building online services. I helped out with product strategy, setting up a lean UX design process, and getting an integrated agile design and development team up and running. The first services are now shipping so it is time for me to move on, after 10 months of very gratifying work. I really enjoy working in the public sector and I hope to be doing more of it in future.
So yes, this means I am available and you can hire me to do strategy and design for software products and services. Just send me an email.
Shortly before the Dutch national elections of this year, Iskander and I gathered a group of fellow tech workers under the banner of “Tech Solidarity NL” to discuss the concerning lurch to the right in national politics and what our field can do about it. This has developed into a small but active community who gather monthly to educate ourselves and develop plans for collective action. I am getting a huge boost out of this. Figuring out how to be a leftist in this day and age is not easy. The only way to do it is to practice and for that reflection with peers is invaluable. Building and facilitating a group like this is hugely educational too. I have learned a lot about how a community is boot-strapped and nurtured.
And finally, the last major thing on my plate is a continuing effort to secure a PhD position for myself. I am getting great support from people at Delft University of Technology, in particular Gerd Kortuem. I am focusing on internet of things products that have features driven by machine learning. My ultimate aim is to develop prototyping tools for design and development teams that will help them create more innovative and more ethical solutions. The first step for this will be to conduct field research inside companies who are creating such products right now. So I am reaching out to people to see if I can secure a reasonable amount of potential collaborators for this, which will go a long way in proving the feasibility of my whole plan.
If you know of any companies that develop consumer-facing products that have a connected hardware component and make use of machine learning to drive features, do let me know.
That’s about it. Freelance UX consulting, leftist tech-worker organising and design-for-machine-learning research. Quite happy with that mix, really.