We will analyze the epistemological and ontological groundings of data-based learning algorithms as well as reflect how the logic of data-driven algorithms and the increasing automation of decision-making corresponds to our societal condition.
Data-based learning algorithms do not only open up new dimensions of knowledge production but shape increasingly more highly relevant societal decision-making processes. Based on correlation-based search heuristics, these algorithms are used to improve translation programs, search engines, predictive policing strategies or so-called ‚adaptive‚ intelligent' protheses. They are highly relevant in realms ranging from financing, administration and technoscience, law enforcement and counter-insurgency. These algorithms do not only embody sociotechnical practices of human and non-human actors but also transport invisible and unquestioned values, norms and preferences.
In our panel, we want to analyze the epistemological and ontological groundings of data-based learning algorithms - how, for example, they are dependent on the provided / selected data material, on implemented classifications, categorizations and problem-solving strategies. At the same time, we want to reflect how the epistemologic logic of data-driven algorithms and the increasing automation of decision-making corresponds to our societal condition: We want to ask whether correlation does not only feed into the datafication of our world but also serves a contemporary sociocultural and biopolitical logic of risk management or whether the increasing automation of decision-making reflects post-democratic developments in the Global North.