Feminist Machine Learning

Towards New Materialist Informatics
1. Auflage 2026
ISBN: 978-1-5292-5685-7
Verlag: Bristol University Press

Buch, Englisch, 224 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Dis-positions: Troubling Methods and Theory in STS

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.

Autoren/Hrsg.

Klumbyte, Goda

Fachgebiete

• Geisteswissenschaften Philosophie Erkenntnistheorie
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein Soziale und ethische Aspekte der EDV
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziale Gruppen/Soziale Themen Ethische Themen & Debatten: Wissenschaft, Technologie, Medizin
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Feminismus, Feministische Theorie

Inhaltsverzeichnis

Introduction

1. Why Assemblage? Approaching Machine Learning Diagrammatically

Part 1: Algorithmic Agency: Probing the Epistemic Operations of Machine Learning

2. Linear Regression: From Regression to the Mean to Relation Machines

3. K-Nearest Neighbours: Homophily and the Making of Difference

4. Decision Trees: Arboreal Organisation of Knowledge

Part 2: Learning Otherwise: Critical and Speculative Design Interventions

5. Diffracting Power: Critical Machine Learning Artefact Design

6. Activating Concepts: Redrawing Machine Learning Design Diagrams

7. Speculating Models, Inventing Algorithms: Experimental Diagrams

Conclusion: Towards New Materialist Informatics as a Critical Technical Practice

Appendix: “Critical Tools for Machine Learning” Workshop Framework and Exercises

References

Über Autor(innen)

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

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