Feminist theory gives us essential tools for understanding AI that other frameworks miss:

Multiple strands, one commitment: Liberal, socialist, radical, intersectional, and technofeminist perspectives each illuminate different dimensions of AI’s relationship to gender. No single strand is complete.

The myth of objectivity: AI systems are not neutral. They reflect the standpoints of the people who build them. Feminist standpoint epistemology makes us more rigorous by insisting we acknowledge this.

Built by whom, for whom: The demographic homogeneity of AI development teams is not a diversity problem alone, it is a structural cause of AI systems that systematically fail women and gender minorities.

Patriarchal assumptions are encoded at scale: From voice assistants to hiring algorithms to healthcare diagnostics, AI systems reproduce and amplify gendered assumptions that have real consequences for real people.

The invisible labour must be seen: Data labellers, content moderators, and care workers are the hidden foundation of AI systems. Their invisibility is a feminist issue and a labour justice issue simultaneously.

Virginia Eubanks shows us the cost: Automated systems deployed in contexts of poverty and vulnerability amplify existing inequalities. The people bearing the cost are overwhelmingly women.

Feminist AI is possible: Care ethics, feminist design principles, intersectional impact assessment, and labour justice provide a concrete foundation for AI that takes gender seriously, not as a correction but as a design commitment.