Martins, Yann Patrick
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Making Arguments with Data: Resisting Appropriation and Assumption of Access / Reason in Machine Learning Training Processes
This article presents an approach to practicing ethics when working with large datasets and designing data representations. Inspired by feminist critique of technoscience and recent problematizations of digital literacy, we argue that machine learning models can be navigated in a multi-narrative manner when access to training data is well articulated and understood. We programmed and used web-based interfaces to sort, organize, and explore a community-run digital archive of radio signals. An additional perspective on the question of working with datasets is offered from the experience of teaching image synthesis with freely accessible online tools. We hold that the main challenge to social transformations related to digital technologies comes from lingering forms of colonialism and extractive relationships that easily move in and out of the digital domain. To counter both the unfounded narratives of techno-optimismand the universalizing critique of technology, we discuss an approachto data and networks that enables a situated critique of datafication and correlationism from within.
Making Arguments with Data
Whether we are discussing measures in order to "flatten the curve" in a pandemic or what to wear given the most recent weather forecast, we base arguments on patterns observed in data. This article presents an approach to practicing ethics when working with large datasets and designing data representations. We programmed and used web-based interfaces to sort, organize, and explore a community-run archive of radio signals. Inspired by feminist critique of technoscience and recent problematizations of digital literacy, we argue that one can navigate machine learning models in a multi-narrative manner. We hold that the main challenge to sovereignty comes from lingering forms of colonialism and extractive relationships that easily move in and out of the digital domain. Countering both narratives of techno-optimism and the universalizing critique of technology, we discuss an approach to data and networks that enables a situated critique of datafication and correlationism from within.