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Behind the Interface: Layers of Bias Hidden in Machine Learning

Behind the interface examines the technocultural dimensions of working with (text) data, with the understanding that computing infrastructure and practices are not neutral but emerge from complicated historical lineages that often remain hidden to the user. By peering behind the interface at the circumstances, biases and assumptions surrounding the layers of decision-making involved in developing technologies, we encourage you to consider how structures of inequality become hard-coded into the tools and conventions of data science and how we can work towards opening up new sites of resistance and critique.

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