data cyborgs - constructing datafied relations
How can data be reappropriated to shape a better world?
• 3. April - 5. June 2025 (Updated!)
• Online!
• Ten-weeks, Thursdays, 6-8PM CET
• Small class of participants
Artist / Student (Full Time)
€450*
Freelancer
€490*
Professional
€550*
Generous Supporter Ticket
€590*
course
description
The oppressive structures of modernity rely on the construction of three abstract divisions– mind/body, individual/collective, and civilization/nature– in each case, imposing a relation of dominance of the former over the latter. The body reduced to an object, freedom equated with individualism, and nature rendered savage and exploited.
According to Togolese anthropologist and architect, Sénamé Koffi Agbodjinou, surveillance techno-capitalism optimizes for and accelerates the deepening of these schisms, moving fast and breaking things towards culmination in the Smart City, a factory for data extraction, where the idea of the optimized self is sold for the price of privacy and agency, a society driven by algorithmic governmentality.
Scientific epistemology relies on using data to build mathematical models of the world—mechanisms for understanding processes, extracting actionable knowledge, and making predictions. This way of knowing has been granted supremacy over all others, legitimizing the distilled, abstractified, and decontextualized practice known as Data “Science.”
Within that practice is a tool, Machine Learning, a way of building mathematical models that requires no theory–no clear statements of assumptions or hypotheses; it is a central pillar of the post-truth AI revolution. The distribution of the power to use data and statistical modeling to understand or shape the world is heavily lopsided; advanced “technosolutions” reinforce the divisions at the root of oppression. That power needs to be redistributed.
This course will explore how a new balance can be struck, empowering participants to understand both the misuse of data and models and how they can instead be harnessed for acts of resistance and liberation. We will reframe the design and implementation of data science projects, combining the framework of value-sensitive design with eco-feminist principles and drawing from Simone de Beauvoir, Donna Haraway, James Bridle, Silvia Federici and others.
Situated within this reframing, students will acquire practical knowledge that will empower them to recognize and resist data as oppression and contribute to the construction of new datafied relations that aim not to divide but to unify.
This course has three main goals
1) Bring forth the limitations, oversimplifications, and hidden assumptions of data science, grounded in concrete examples that reveal the sociotechnical context and numerical encoding of power structures.
2) Use that knowledge to reframe data science as data storytelling and worldbuilding, teaching theoretical concepts through three case studies, each reflecting an act of building a data-mediated bridge across the divides created by oppressive power structures.
3) Give participants practical knowledge and tools through demos and exercises in a collaborative learning environment, working towards the co-creation of new data stories by prototyping projects that aim to enact liberating change.
keywords: AI, data science, machine learning, Python, cyborgs, value-sensitive design.
The main content of the course will follow the three main stages of the data science pipeline: Data Collection, Modeling and Deployment. We will situate the theory in case studies that highlight both artistic and activist projects that highlight how an understanding of data science can create new, liberating data relations. Each class will also aim to teach a practical skill through demonstrations and exercises followed by a week of putting these ideas into hands-on practice.
Participants will develop a sociotechnically situated framework for understanding data science and prototype their own data-driven projects, which will be presented in the final weeks of this ten-week course.
Case Studies
Individual/Collective - VFRAME: Developed and maintained by artist, software engineer and researcher Adam Harvey, VFRAME is a computer vision toolkit designed for human rights researchers. Using AI-powered object detection algorithms, the kit includes tools for the detection of war crimes and privacy-protection in protest images and video.
Mind/Body - Mental Health and Breathing: In my own research at the Bern University of Applied Sciences, I collaborate with clinical psychologists to develop models that help identify and track indicators for mental health issues based on personal narratives. Together with artist Coco Sollfrank, we connect well-being to biometric breathing data, drawing from the research of neurologist and pranayama practitioner Dr. Ulrich Ott.
Civilization/Nature - The Waldrapp Project: The northern bald ibis (DE: Waldrapp) is a migratory bird that was hunted to extinction in Europe in the 17th century. With the birds’ population reduced to a few critically endangered groups in the Middle East and Northern Africa, conservationists use geo-tracking as part of an effort to rewild the birds in Europe. Artist Gordan Savičić and media theorist Felix Stalder collaborate extensively with the conservation group, mapping the interconnected infrastructures required to protect the birds and create new data-mediated relations between humans and the birds.
In this course you will be introduced to
basic Python (learn by doing approach)
web scraping
synthetic data generation
machine learning
visualization, and other tools for interpreting data and models
Anvil for quickly prototyping Python webapps
course
outline
Each lecture class will be followed by a week of hands-on practice to allow more time to engage with the concepts and materials and to draw from the knowledge of the instructor and fellow participants.
Weeks 1-2: Getting to Know Each Other + Course Intro
The first session is about getting to know each other and learning about everyone’s background and expectations. What brought you to this course? What is it that you hope to learn and practice? We will share ideas, examples, and resources. We’ll also introduce important concepts in data science, present an alternative cyborg-inspired approach to the design and implementation of data projects, summarize the case studies, and give a general overview of the course and the tools we’ll be using. In the second week, students will present and discuss their own examples of data used to reinforce the three divisions of oppression and data used to reconcile those divisions, planting seeds for the final presentations in Weeks 9-10.
Week 3-4: Data
We will discuss data collection as well as the notion of data being situated, thinking critically about how sociotechnical context affects data, the gap between data and the aspects of the world it aims to represent and the potential benefits and harms of datafied perspectives. Practical skills: Web scraping and synthetic data generation.
Week 5-6: Modeling
In statistical modeling, raw data is inputted into a mathematical model, processed and converted into an output–a prediction, decision, information extracted from the data etc. We view the input data as an analog to sensory data and discuss James Bridle’s notion of the Umwelt, the internalized worldview of the model, which is inherited not just from the data, but from design
decisions and other more hidden aspects of the development context. Practical skills: Training machine learning (ML) models.
Week 7-8: Deployment
Once deployed, the finished model will contribute towards shaping the world in the image of its Umwelt, but the way the model shapes the world is determined by how its output is transformed into real-world action, be it fully automated or mediated through human decision-making. In the latter case, the model converts the data into a story. A call-to-action. Deployment concerns the environment in which the model will act, how information is transferred from the model, and how that information becomes action. We will discuss human-model-interfaces and human-model-decision-making. Practical skills: Anvil, visualization and tools for explaining the output of ML models.
Week 9-10: Presentations
Students present a chosen data-driven project. This could be an existing project or a planned one. Students will describe the data, modeling, deployment workflow, and any preliminary outcomes with the help of tools and perspectives acquired throughout the course. Where to go from here? A focused discussion on the insights gained during the course, in addition to getting involved and sharing resources suited to class participants’ goals.
who is this
class for?
Anyone with a desire to explore data science concepts in the context of data activism/artivism. People looking to understand the algorithms that govern much of modern society. Artists, journalists or activists searching for ways to use data to explore an issue or further a cause. No prior experience necessary.
about online classes
Classes are 'live' meaning that you can directly interact with the instructor as well as with the other participants from around the world. Classes will also be recorded for playback in case you are unable to attend for any reason. For specific questions, please email us and we'll get back to you as soon as we can.
about
scholarships
We are offering a limited number of reduced fee scholarships for this online class for those facing financial hardships. These allow participants to pay a reduced fee and are reserved for women, BIPOC, and LGBTQ+ who otherwise would be unable to attend. To be considered for one of these scholarships, please use this form.
To apply for a reduced fee scholarship, you must fill in the form no later than one week before the course begins. We will not accept any class sign-ups or scholarship applications after this date, as our regular sign-ups will determine the amount of scholarships we can accommodate. We will notify you shortly thereafter if your application has been approved.
We are a small organisation with no outside funding and like many, we are also in survival mode. We depend on tuition fees for reimbursing class instructers, space fees, and operational costs. We ask you to consider this when applying for a reduced fee scholarship. <3
meet the
instructor
Alexandre Puttick
Data Scientist, Writer, Artist, and Educator
A Data Scientist, Writer, Artist, and Educator based in Biel, Switzerland, Alexandre Puttick is currently a post-doctoral researcher and lecturer at the Bern University of Applied Sciences (BFH) in the Applied Machine Intelligence group, working with Prof. Dr. Mascha Kurpicz-Briki. Their research focuses on fairness in AI systems and AI applications for mental health, funded by the Swiss National Science Foundation (SNSF) and an EU Horizon grant.
Recently, they completed a three-year project called Latent Spaces at the Zurich University of the Arts (ZHdK), collaborating with Prof. Dr. Felix Stalder, !Mediengruppe Bitnik, Coco Sollfrank, Shusha Niederberger, and Gordon Savatic. Latent Spaces is an artistic research project exploring ambiguities in Big Data and Data Science, also funded by the SNSF.
As a writer, Alexandre specializes in short fiction, journalism, and educational material. They are a member of Zookunft, an interdisciplinary art collective based in Zurich, where they engage in visual art, creative writing, music, dance, and performance. Additionally, they are part of the planning committee for the 8th Kaleido Retreat in Switzerland, which offers workshops and lectures at the intersection of art, science, and activism.