2024-09-28, 16:40–17:10 (Europe/Zurich), Werk
Dirty data refers to data that is somewhat faulty and requires to be removed in data preprocessing. In 1980s, non-white women’s body size data was categorized as dirty data, now, in the age of GPT, when “scale-up” first is the golden rule, when researchers/engineers scraping the internet to get as much free data as possible, what is considered as dirty data and how are they removed from massive training materials?
In this presentation, we will look into historical dirty data examples and several open (or reproduced thus open) extraction-based, human-not-in-the-loop datasets and gathers their heuristic-based methods for filtering out dirty data. A heuristic is a practical method that, not guaranteed to be optimal, perfect, or rational, but is “good enough” solving the problem at hand. This publication questions whether a narrative of “cleaning” can emerge from technical papers, reflecting on these silent, anonymous yet upheld estimations and not-guaranteed rationalities in current sociotechnical artifacts, as well as for whom these estimations are good-enough, as it will soon be part our technological infrastructures.
jiawen exists as a user most of the time in her life, having little agency as a standard user, both technology and politic wise, she seeks for otherwise possibilities queering the given identity of a user, her wish is to be an analogkäse in the digital figurations and drip sizzling fat on the cables and ports.
Apart from that, jiawen is a researcher-designer based in Bremen, Germany, her research focus on technology as memory and desire, with contaminated history but appearing pure, sterilized, decontextualized and dehistoricized which reduces rather than relating. She looks into technology’s micro-(counter)history, materiality, poetics and politics. She sees l̸͓̪̜͕̮̆̎̓e̷͍̲͕̫̦͌a̵̡̝͈̗̐k̴̢͖̮̞̣̄͛̓͛̐ as an instance of space-time continuum and a definite part of the digital reality, and leaking as a method to survive together.