How can we begin to understand as citizens the growing multi-database platforms embedded in cities that are ever-changing culturally, politically, and physically? And how might we experience the possibility of ‘openness’ and fluidity of the networked city while making this knowledge accessible to all levels of an inquiring society?
Steps to a series of data walks
A well-known building, a restaurant, a landscape, and a public figure.
The journey is an 80-minute excursion through different spots on the Ars Electronica site.
In a group of ten maximum, we will go on a walk in the surroundings of Ars Electronica; guided by a leader that will give instructions on the data collection to make; take careful notes about what happens, and produce a series of experimental inventories or archives and a final cosmogram.
Each spot introduces approaches to accounting data in the city realm: from public memory, metaphors, senses, and ecologies, and ways to bridge our inter/dependences with our environment.
Spot 1 - Collect examples of “algorithm” histories. We first look into the Urban History of an Algorithm, it’s not only math that drives algorithms, but also concepts and “institutional and administrative conditions.” What people call “AI” is actually a long historical process of crystallizing collective behavior, personal data, and individual labor into privatized algorithms that are used for the automation of complex tasks: from driving to translation, from object recognition to music composition.Spot 2 - Collect examples of “data” uncertainties.
Uncertainty in data may appear in many forms and may be described in many ways. In order to identify examples, we will attend to the different ways that uncertainty is described and build a list of different keywords, taxonomies, and starting points.
Here we apply a taxonomy of uncertainty using two categories: “user-recognized” and “machine-generated”. User-recognized uncertainty is how uncertainty is perceived and categorized in digital texts by users and the relations to previous work in visualization and the humanities. On the other hand, “machine-generated” uncertainty covers the annotation done by algorithms and the uncertainty that might occur.
Spot 3 - Collect examples of “data” extraction. Make an inventory of all of the different “actors” which are involved in the production of extraction in a technical landscape. What do we consider the life cycle of this technical landscape? Where does it begin and where does it end? Who is involved in each step of the process?
This spot also will open a space for discussing data and planetary thinking in matters of environmental and climate change.
Spot 4 - Collect examples of the olfactory/ tactile/ sonic dimensions How can collecting sensory aspects break binaries in data collection? Here with the support of several guiding materials, we look into the sensorial part that is embedded in data collection.
Make an inventory of how the trouble is produced. From the different materials gathered, each will make a selection of one case to focus on or a smaller number of cases to compare. What has gone wrong? What is a nice discovery? How are algorithmic gaps produced?
It situates a reflection on algorithms, not as a finite sequence, but on the unforeseen arrangements of algorithms, affects, meanings, structures of power, and so on. What an algorithm does to bodies and what, and how much doing assemble in the environment? Here we may look not just “within” the algorithm as a set of computational steps, but also “across” the situation affected by it. Could be the result of an engineering error but also the result of a deliberate intervention to alter results or because of the multivalence of a search term. Make an inventory of all of the different “actors” who are involved in the production of uncertainty, glitches, and errors.
Mapping out the different elements involved. Once an inventory of different elements is produced, we will explore the relations between them by making a visual diagram. People can use post-it notes or graphical software to show the relations between the various elements, as well as annotations about how they relate to each other.
Create an account of “algorithm gaps” Once you have created a diagram, we may then use this as the basis of an account of urban auscultation in the form of a story, narrative, image, film clip, or another account. In particular, following the approaches mentioned above, we may delve into less familiar perspectives and other aspects of how gaps and sensory aspects are produc