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Patterns of Inclusion: How Gender Matters for Automation, Artificial Intelligence and the Future of Work
Citation. Kelan, E. (2024). Patterns of Inclusion: How Gender Matters for Automation, Artificial Intelligence and the Future of Work. Routledge. https://doi.org/10.4324/9781003427100
Summary. This open-access monograph analyzes how gender and inclusion are constructed in accounts of digitalization, automation, and AI at work. It traces narratives about automation versus augmentation, ostensibly uniquely human socio-emotional skills, the promise of fixing algorithmic bias, and the visibility of labor and assumptions embedded in AI production. It argues that AI can reproduce social inequalities while also making institutional patterns of exclusion more legible, so inclusion cannot be treated as a purely technical correction.
Key claims
- Future-of-work narratives can frame automation as a contest between humans and machines while obscuring the gendered assumptions embedded in which occupations and workers are imagined as threatened or augmented.
- Treating socio-emotional skills as a distinctively human advantage can reproduce gendered valuations of care and relational work rather than neutrally identifying work that technology cannot automate.
- Algorithmic bias is not necessarily remediable through explainability or technical fixes alone because AI systems learn from and can repeat inequalities already structured into social and organizational settings.
- AI-supported hiring and other systems may reveal patterned exclusion at scale, but visibility of those patterns does not itself determine a just organizational response.
- AI production and deployment can conceal the labor, classifications, and social assumptions on which ostensibly autonomous systems depend.
Evidence & limitations
- The book is a qualitative, interpretive synthesis of the author’s research on gender, digitalization, and work; its analytic contribution is to identify patterns and mechanisms rather than estimate causal effects or prevalence.
- It is a Routledge monograph published online in 2024 under CC BY 4.0; its general arguments need context-specific empirical validation before being used to predict outcomes for a particular workforce or AI system.