Part I: Exposing Myths and Barriers

This section invites chapters that critically examine the systemic, structural, and cultural barriers women face in AI. Despite the promise of neutral or objective technology, AI systems often reinforce long-standing patterns of exclusion and inequity. Contributors are encouraged to challenge dominant narratives—such as the idea that AI is unbiased, purely technical, or equally accessible to all—and instead reveal the deeper social, historical, and epistemological forces that shape AI development and deployment. Chapters might explore the gender gap in AI adoption, the compounded impact of intersectional identity, or the myths that obscure power dynamics in design and data.

• Intersectionality: how race, class, ability, and gender shape experiences with AI
• The gender adoption gap: why women use AI less—and the costs of that divide
• Bias in AI systems and the myth of technological neutrality
• How AI impacts women differently across global, racial, or Indigenous contexts

Part II: Resistance and Leadership in Action

This section focuses on women leading change—within organizations, institutions, and communities—to make AI more ethical, inclusive, and human-centered. Contributors are invited to share real-world examples of how women are resisting the default, challenging exclusionary norms, and building alternatives. These might include case studies of organizational policies that promote equity in AI use, examples of women leaders using AI in creative and values-aligned ways, or analyses of how internal culture shifts and allyship enable more diverse leadership in technical environments. The emphasis here is on replicable insight: what’s working, what’s emerging, and what others can adapt.

• Organizational policies and practices that increase AI adoption among women
• Navigating the “AI boys’ club”: transforming tech culture from within
• How women leaders use AI to support creativity, inclusion, or mission-driven work
• Ethical implementation: governance models and frameworks led by women
• Interviews with women driving inclusive AI initiatives across sectors

Part III: Reimagining AI’s Future

The final section welcomes visionary, theoretical, and speculative explorations of what AI could become under inclusive and feminist leadership. AI systems reflect the values and assumptions of their creators—so what might AI look like if it were guided by the ethics of care, feminist epistemology, or principles of design justice? Contributors are encouraged to draw from feminist theory, critical tech studies, Indigenous worldviews, or philosophical inquiry to chart new directions. This section may also include speculative futures, utopian or dystopian visions, and proposals for governance structures that center equity, consent, and accountability. These chapters should stretch the boundaries of what AI can be, not just what it is.

• Feminist frameworks: HCI, ethics of care, data feminism, and design justice
• Philosophical questions about intelligence, epistemology, and AI futures
• Mentorship and education models that prepare the next generation
• Regulatory visions: feminist or community-led approaches to AI governance
• Speculative imaginaries: utopias and dystopias from a woman’s lens