Feminist Machine Learning

Towards New Materialist Informatics

AvGoda Klumbytė

Häftad, Engelska, 2026

Del i serien Dis-positions: Troubling Methods and Theory in STS

689 kr

Kommande

Beskrivning

Available open access digitally under CC-BY-NC-ND licence.Machine learning shapes what we see, know and decide, yet the processes through which it operates often remain obscure. This bold and original book brings feminist theories of knowledge into direct dialogue with algorithmic systems design, revealing how machine learning systems encode power, difference and historical bias into their mathematical operations. Moving from critical analysis to creative intervention, it explores three widely used algorithms to show how design choices shape outcomes and embed social assumptions, before proposing radical new design strategies rooted in appropriation and experimentation.The result is a compelling call for a transdisciplinary critical technical practice - one that places feminist and new materialist thinking at the heart of how we build intelligent systems.

Produktinformation

Utgivningsdatum:2026-11-01
Mått:156 x 234 x undefined mm
Format:Häftad
Språk:Engelska
Serie:Dis-positions: Troubling Methods and Theory in STS
Antal sidor:224
Förlag:Bristol University Press
ISBN:9781529256857

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Mer om författaren

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

Innehållsförteckning

Introduction1. Why Assemblage? Approaching Machine Learning DiagrammaticallyPart 1: Algorithmic Agency: Probing the Epistemic Operations of Machine Learning2. Linear Regression: From Regression to the Mean to Relation Machines3. K-Nearest Neighbours: Homophily and the Making of Difference4. Decision Trees: Arboreal Organisation of KnowledgePart 2: Learning Otherwise: Critical and Speculative Design Interventions5. Diffracting Power: Critical Machine Learning Artefact Design6. Activating Concepts: Redrawing Machine Learning Design Diagrams7. Speculating Models, Inventing Algorithms: Experimental DiagramsConclusion: Towards New Materialist Informatics as a Critical Technical PracticeAppendix: “Critical Tools for Machine Learning” Workshop Framework and ExercisesReferences