Paper short abstract:

This paper looks at the principles of knowledge production and legitimization through data-based algorithms in machine learning. It asks what kind of conceptual models of learning and knowledge are proposed, and how these models can be re-evaluated from the perspective of feminist epistemologies.

Paper long abstract:

This paper will look at the principles of how knowledge is produced and legitimized through data-based algorithms in machine learning. We will interrogate what kind of conceptual models of 'learning' and 'knowledge' are proposed through algorithmic processes of machine learning, and how these models can be evaluated from the perspective of feminist epistemologies. Specifically, we will investigate how data-driven machine learning and resulting knowledge production is based on principles of abstraction, categorisation and correlation.

As technological as well as discursive principles these concepts served as foundational in pursuit of modern science and production of objective, truthful scientific knowledge. However, feminist epistemologies and critiques of science have pointed out that such knowledge production is closely related to unequal power dynamics that exist in a given society, and argued for a more embedded, embodied and situated perspectives on ways of knowing and legitimization of truth claims. Taking these aforementioned critiques into account, we will ask how data-driven algorithmic knowledge production in machine learning can be re-read in ways that account for the power dynamics that are re/produced through the models of 'knowledge' and 'learning' that are at the core of such knowledge production.