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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.
1. Introduction: Feminist Machine Learning
2. Why Assemblage? Diagrammatics of Machine Learning
I. Algorithmic Agency: Probing the Epistemic Operations of Machine Learning
3. Linear Regression: From Regression to the Mean to Relation Machines
4. k-Nearest Neighbours: Homophily and the Making of Difference
5. Decision Trees: Arboreal Organization of Knowledge
6. Tying the Knots: Algorithms as Operational Diagrams
II. Learning Otherwise: Critical and Speculative Design Interventions
7. Diffracting Power: Critical Machine Learning Artefact Design
8. Activating Concepts: Redrawing Machine Learning Design Diagrams
9. Speculating Models, Inventing Algorithms: Experimental Diagrams
10. Towards New Materialist Informatics as a Critical Technical Practice