Feminist theory is not a single unified framework. It is a family of related but distinct intellectual traditions, each with different diagnoses of gender-based oppression and different prescriptions for addressing it. Understanding these distinctions matters for applying feminist theory to AI, because different strands of feminism reveal different dimensions of the problem.

Liberal feminism focuses on formal equality: women should have the same rights, opportunities, and representation as men. Applied to AI, liberal feminism asks whether AI systems treat men and women equally. Do hiring algorithms disadvantage women? Do credit scoring systems produce different outcomes for women? Do voice recognition systems perform equally well on male and female voices? These are important questions. But liberal feminism’s focus on equality within existing structures limits its ability to question those structures themselves.

Socialist and Marxist feminism examines the intersection of gender and class, arguing that women’s oppression cannot be understood apart from economic relations. It draws attention to the labour women perform, paid and unpaid, visible and invisible, and how AI systems relate to that labour. Who does the work that makes AI possible? Who is displaced by AI automation? Whose labour is devalued by AI systems?

Radical feminism argues that patriarchy, male domination, is the primary form of oppression, more fundamental than class or race. Applied to AI, radical feminism asks about the power relations embedded in AI design: who has authority, whose perspective is treated as default, whose needs are centred.

Intersectional feminism, developed by Kimberlé Crenshaw and elaborated by many scholars, insists that gender cannot be understood apart from race, class, sexuality, disability, and other axes of identity. An AI system that produces equitable outcomes for white professional women may simultaneously harm Black working-class women. Single-axis analysis misses this.

Technofeminism, developed by scholars like Judy Wajcman, examines the relationship between gender and technology specifically, arguing that technologies are not neutral tools but are shaped by the gendered contexts in which they are designed and deployed. AI is not an exception.

Key scholars whose work is essential to this course: bell hooks on intersectionality and power; Donna Haraway on the cyborg and situated knowledge; Safiya Umoja Noble on algorithmic oppression; Kate Crawford on AI and power; Virginia Eubanks on automated inequality.

Reflection question: Which strand of feminist theory resonates most with challenges you see in your own professional context? What does it illuminate that other frameworks miss?