AI governance frameworks and policies addressing technology-facilitated gender-based violence are being developed largely in parallel, with minimal connection between the two agendas, leaving women, queer, and gender-diverse people vulnerable to AI-enabled harms. A new research report from the Association for Progressive Communications (APC) reveals that despite rapid AI deployment, those most affected by technology-facilitated gender-based violence (TFGBV) are often excluded from the policies meant to protect them.

Why Are AI Governance and Gender Violence Protections Operating in Separate Silos?

The disconnect is striking. AI governance bodies and international organizations addressing gender-based violence are sometimes even the same multilateral institutions, yet they rarely communicate with each other. This gap has real consequences. As AI enables new forms of abuse, existing governance frameworks struggle to keep pace. Deepfakes, automated harassment campaigns, gendered disinformation, and surveillance-enabled abuse are among the most urgent and under-regulated consequences of AI deployment, according to the APC research.

"There are now these great many AI governance frameworks and this growing body of international and regional norms on TFGBV, but they rarely or barely speak to each other. Who bears the cost of that gap and how do we break it?" explained Daniela Schnidrig, digital rights and policy consultant specialist and author of the report.

Daniela Schnidrig, Digital Rights and Policy Consultant Specialist

The problem extends beyond institutional coordination. AI doesn't create entirely new forms of violence; instead, it dramatically increases the scale, speed, and persistence of existing harms. Coordinated harassment campaigns or gender disinformation that once required organized human effort can now run at volumes and with persistence that was previously impossible or prohibitively expensive.

How Does AI-Enabled Gender Violence Affect Women's Participation in Society?

The consequences of TFGBV reach far beyond individual experiences of abuse. The economic and material impacts are substantial. People leave their jobs, withdraw from public life, and relocate because of threats to their reputation. This creates what experts call a "chilling effect" that silences vulnerable voices from civic spaces, academic discourse, and policy debates.

This absence itself becomes a structural harm. When women, queer, and gender-diverse people are driven out of public discourse due to the cost of participation, their voices disappear from the very rooms where AI governance decisions are made. As Schnidrig noted, "Voices that are missing from the public debate then end up being these same voices that are missing from the rooms where AI is being governed".

As Schnidrig

The recent UN Global Dialogue on AI Governance illustrated this problem. Gender remained largely peripheral to the conversation, with stakeholders raising it only as an afterthought rather than as a mainstreamed concern. Technology-facilitated gender-based violence was frequently absorbed into broader discussions about AI security rather than treated as a specific, urgent issue requiring dedicated attention.

What Are the Critical Gaps in Current AI Governance Frameworks?

The research maps three intersectional areas where gaps exist. These structural vulnerabilities create opportunities for harm to flourish unchecked:

• Structural Gendered Risks in AI: The gender digital divide, data extraction without consent, and the lack of meaningful participation from women and marginalized communities in AI development and deployment.
• TFGBV Harms Enabled or Amplified by AI: Gendered disinformation, bias and stereotypes embedded in AI systems, and the automation of harassment at unprecedented scale.
• Governance Gaps and Challenges: Most AI frameworks barely consider TFGBV, and where gender is addressed, it appears as broad principles like fairness and non-discrimination rather than concrete, enforceable measures.

A critical distinction emerged from the research: the difference between principles and implementation. It's relatively easy to include gender equality language in policy preambles, but ensuring gender considerations survive into the operative provisions requires specific mechanisms. These include gender-responsive risk assessments, human rights impact evaluations, audits, disaggregated data requirements, budget allocations, and detailed implementation plans.

Many TFGBV instruments predate current AI technologies and have not been updated to address emerging threats like deepfakes, algorithmic amplification, or other AI-enabled forms of abuse becoming increasingly common. This regulatory lag leaves vulnerable populations exposed to harms that existing legal frameworks were never designed to address.

How to Bridge the Gap Between AI Governance and Gender Violence Protection

The APC research proposes concrete action across three areas to address these institutional silos and protect vulnerable communities:

• AI-Specific Measures: Implement gender-responsive risk and human rights impact assessments throughout the entire AI lifecycle, from development through deployment and monitoring.
• TFGBV-Specific Reforms: Explicitly recognize technology-facilitated gender-based violence as a category of AI risk, update definitions of digital violence, and strengthen reporting and redress mechanisms for survivors.
• Crosscutting Structural Changes: Require intersectional data collection, build regulatory capacity, and ensure meaningful participation of women, gender-diverse people, and civil society in AI governance processes.

Breaking down institutional silos is at the heart of these recommendations. Different instruments, from gender policies and ministries to AI strategies and governance mechanisms, need mechanisms to communicate with each other. Representation alone is insufficient; a gender-balanced panel does not necessarily produce a gender-responsive discussion. True mainstreaming requires asking across every dimension of AI governance: who benefits, who bears the risks, whose labor sustains the system, whose knowledge is represented, who participates in decisions, and who has access to remedy when harm occurs.

While governments have the primary responsibility for adopting and enforcing legal, policy, and regulatory measures to ensure private sector actors identify and mitigate TFGBV risks, multilateral bodies can support this through norm-setting and coordination. Civil society plays a crucial role in monitoring and documenting impacts, holding both governments and technology companies accountable.

The research underscores an uncomfortable reality: as AI systems become more powerful and pervasive, the governance frameworks meant to protect vulnerable populations remain fragmented and incomplete. Closing this gap is not merely a technical or policy challenge; it is a matter of fundamental rights and democratic participation.