Goda Klumbytė is Postdoctoral Researcher in Participatory IT Design at the University of Kassel and in Human–Computer Interaction at the University of Salzburg.

• 1.Introduction: Feminist Machine Learning2.Why Assemblage? Diagrammatics of Machine LearningI.Algorithmic Agency: Probing the Epistemic Operations of Machine Learning3.Linear Regression: From Regression to the Mean to Relation Machines4.k-Nearest Neighbours: Homophily and the Making of Difference5.Decision Trees: Arboreal Organization of Knowledge6.Tying the Knots: Algorithms as Operational DiagramsII.Learning Otherwise: Critical and Speculative Design Interventions 7.Diffracting Power: Critical Machine Learning Artefact Design8.Activating Concepts: Redrawing Machine Learning Design Diagrams9.Speculating Models, Inventing Algorithms: Experimental Diagrams10.Towards New Materialist Informatics as a Critical Technical Practice

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Goda Klumbytė is Postdoctoral Researcher in Participatory IT Design at the University of Kassel and in Human–Computer Interaction at the University of Salzburg.

• 1.Introduction: Feminist Machine Learning2.Why Assemblage? Diagrammatics of Machine LearningI.Algorithmic Agency: Probing the Epistemic Operations of Machine Learning3.Linear Regression: From Regression to the Mean to Relation Machines4.k-Nearest Neighbours: Homophily and the Making of Difference5.Decision Trees: Arboreal Organization of Knowledge6.Tying the Knots: Algorithms as Operational DiagramsII.Learning Otherwise: Critical and Speculative Design Interventions 7.Diffracting Power: Critical Machine Learning Artefact Design8.Activating Concepts: Redrawing Machine Learning Design Diagrams9.Speculating Models, Inventing Algorithms: Experimental Diagrams10.Towards New Materialist Informatics as a Critical Technical Practice

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Goda Klumbytė is Postdoctoral Researcher in Participatory IT Design at the University of Kassel and in Human–Computer Interaction at the University of Salzburg.

• 1.Introduction: Feminist Machine Learning2.Why Assemblage? Diagrammatics of Machine LearningI.Algorithmic Agency: Probing the Epistemic Operations of Machine Learning3.Linear Regression: From Regression to the Mean to Relation Machines4.k-Nearest Neighbours: Homophily and the Making of Difference5.Decision Trees: Arboreal Organization of Knowledge6.Tying the Knots: Algorithms as Operational DiagramsII.Learning Otherwise: Critical and Speculative Design Interventions 7.Diffracting Power: Critical Machine Learning Artefact Design8.Activating Concepts: Redrawing Machine Learning Design Diagrams9.Speculating Models, Inventing Algorithms: Experimental Diagrams10.Towards New Materialist Informatics as a Critical Technical Practice

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