Hello, I am Anaëlle Beignon, and I am going to present the result of my masters’ thesis project in partnership with “Pôle Supérieur de Design”
at Villefontaine and Université Lyon 1. I collaborated with Emeline Brulé,
Jean-Baptiste Joatton and Aurélien Tabard throughout the project.
Our pictorial “Tricky Design Probes” is at the intersection of a speculative approach of design tools and design education.
During my thesis project, I created four probes aimed at designers.
I will focus on the first one for this presentation, but you will find the other three examples in the pictorial.
Each of the probe takes a different form and targets different tools and methods in the design process.
Here is the one I will tell you about in this presentation, it is a Computer vision based analytics to understand gendered behaviors in cities.
This one is a design workshop for creating a service for women’s safety in mobility.
This one is a workbook to investigate women’s fears in the streets regarding men’s behaviours.
And the last one is a night trip plan alone in the city that I have done to confront fears of unsafety.
We created these probes in echo to growing concerns in the design community about the ways design tools and processes can be used.
We are all familiar with common design tools and methods widely spread amongst the design community such as brainstormings, customer journeys,
design sprints, design toolkits, and of course post it notes.
But actually, a growing body of literature criticizes these mainstream design tools and processes, how they are deployed and conceived.
For example, Iskander questions how current design tools tend to aim for consensual solutions, as they become generic to apply to different contexts. 16
In a recent article, Mattern criticizes the emblematic post-it note and other traditional tools for participatory design because they would contribute to faking
a democratic process through the aesthetics of the design workshop.
And actually making decisions in the design process that suit every stakeholder might be impossible, for example Bennett emphasizes that some tensions can’t be resolved,
and rather need to be discussed in order to create meaningful social change.
Different design approaches already exist to seek for dissensus and contestation such as Carl DiSalvo’s Adversarial Design, which our work especially draws on,
as well as the body of work from Malmö on agonism in Design.
Critical and speculative design involve creating design pieces that foster reflection on beliefs, values and problem framing, or problem finding as Dunne and Raby propose,
as opposed to a design for the industry which would be constrained by market-driven limitations.
Designing from the margins addresses the democratization of the design process to marginalized populations that lack of participation
and therefore of representation in design processes.
By proposing a critical design approach that looks at the design processes and tools, we open a space of contestation within the design community.
So our critical design probes target designers.
During my thesis project, I chose to focus on design tools in the context of urban technologies, and I particularly wanted to open
a space of contestation regarding gender inequalities in public places.
The probe I will tell you about now explores design tools that imply automated analytics, and large-scale data-collection.
This year has been tough for designers who wanted to conduct fieldwork because of the coronavirus outbreak. The next probe explores remote fieldwork tools,
that we considered through the lens of gender for this project.
There are already many different possibilities to access easily people’s lives remotely. You can find for free and online different public or private surveillance cameras
that are precious tools for studying for example how do people travel in cities, or observing cultural differences between different countries.
Let’s pretend now that you want to observe the street’s occupation disparities according to gender and to different locations in the world and at different moments of the day and night.
These open access cameras would be precious resources that you could take advantage of for collecting a large data bank
of gendered behaviours that you could work with in the context of urban planning projects for instance.
A first step would be to collect live feeds from online surveillance cameras from different places around the world.
There are numerous websites on which you can observe people in public spaces from public cameras, but also in their intimacy through hacked connected objects
without them even knowing it. As surveillance is getting ubiquitous, the exploitation of surveillance devices could become a new popular way
to observe our end-users. Why not?
To classify the gender of the people in the pictures, one could create and train a computer vision algorithm.
Such an algorithm could be trained to recognize video features corresponding to gender attitudes.
Here we propose a set of criteria with a weighting going from -100 to +100. Every person classified would get a score according to which criteria they meet or not.
For example, riding a bike brings 20 points, while pushing a stroller takes of 30 points.
If the final score is negative, the person is a woman, while if the score is positive, the person is a man.
The criteria could be broadly categorised as ‘physiological’ (for example height) and ‘cultural’
for example a person who uses a stroller. The algorithm aims to detect the gender
of the people on the pictures without making “mistakes”.
We chose to experiment the development of such an algorithm. We collected different screenshots from public surveillance cameras,
and developed three versions of an algorithm for gender classification.
The algorithm was trained by human beings, which means that they had to look at each photo
and make the math for each person on it to determine a score.
So you can understand better the process of creation of the algorithm, I am going to show 3 different iterations that resulted from the training.
I started by creating a first version of the algorithm which corresponds to my own criteria for recognizing one’s gender, that you can see here.
This first step triggered personal reflection upon my own personal stereotypes. For example, I questioned the fact that my strongest criterion was baldness
which I strongly associated with masculine traits, while I strongly associated strollers to women.
Then I trained the algorithm on a first set of pictures until the weight of the criteria were perfectly balanced to detect the gender of people
according to my personal gender stereotypes. I only made minor modifications, such as adding the pink color
in the algorithm because I observed that the people I was identifying as women on the pictures were often wearing pink.
Once I was done with my second version of the algorithm, I gave it to two other designers and I asked them to train
the initial algorithm on other sets of pictures. After training their own weights, they came out with their own version of it,
that you can see here. They changed a lot of things, because they have a different vision of gender than mine,
but also because they had pictures from different cameras than mine.
The purpose of the creation of this algorithm was never to be operational for helping a design project. The implication of designers in the creation
of the algorithm aimed to engage critical discussions about the use of analytics in the design process and their impact on gendered stereotypes.
I will now go through the 3 steps which enabled different stages of reflection.
First, we chose to question the integration of mass-surveillance devices in design tools,
this is why we chose to divert the use of existing public surveillance cameras.
This step enabled us to gather the screenshots that participants could work with,
while being in a condition that might question their own ethical limitations..
Then, we asked participants to build and train together the algorithm.
They had to follow the training process of an algorithm by iterating different version of their algorithm on paper.
This might evoke their own ways in which they construct or deconstruct stereotypes, which they had to discuss together because the training of the algorithm was a group work.
In the third stage of the probe’s deployment, we asked participants to count people recognized as men and women by their algorithm.
Even if the final classification is binary, the linear scale from negative to positive suggests The the categorisation is not so clear.
The creation of these algorithms revealed some of the stereotypes involved on a daily basis to define gender identity, and led us and participants
to discuss the ambivalence of gender stereotypes. Because they may describe accurately a significant proportion of the population, But they also reduce people to a few arbitrary and superficial criteria.
The probe also questioned the ways in which analytics are used on an everyday basis by the profession all around the world,
and raises the question of consent of the people being analyzed.
This probe worked well at different levels and for different audiences.
Indeed, the designer of the probe -myself- had to reflect upon subtle gender issues before deploying the probe.
The participants had to call my algorithm into question in the first place, before being in a situation that they had to agree together on one algorithm.
The last type of audience would be other designers who didn’t participate in the probe.
You, watching this video for example.
From our experience with the different tricky design probes we have tested, we found that it was important to design believable design tools,
but that will progressively engage designers in crossing boundaries of what should be acceptable.
A Tricky design probe, is composed of 5 main elements. First you will need to choose a theme to question,
for instance in our previous example it was gender and surveillance.
Then you choose a design method or tool, in our example it was algorithms.
The tool will be associated with a Trick, such as simplification.
After trying the tool yourself or asking other designers to use it, the trick is revealed and the designers are encouraged to reflect upon the design tools they use.
We met different issues while designing the probes. First, just as any critical or speculative design approach, there is a matter of credibility.
The probe needs to enable the audience to suspend judgement until the probes reach the ethical limits of the design practice to enable a confrontation.
Finding the right balance here between credibility and trickery is important because not enough trickery can lead the audience
to think the probe is an actual design tool, while too much trickery could lead the audience get disinterested of the topic
because it would not refer to what they experience in their design practice.
So, to conclude, our Tricky design Probes proved their capacity to move critical design
and critical considerations to the design process itself and to involve designers in these critical reflexions targeted at their own practice.
The process of creating a probe is as well in itself a way to reflect upon design tools and methods for the designer.
But maybe non-designers could be involved as well in this critical reflections regarding design processes, as the audience, but as well as the creators of the tricky design probes.