Trauma does not fit neatly into structured data formats, leading to algorithmic dismissal of hesitant or disjointed testimonies, thereby reinforcing systemic silencing.

Asking ChatGPT-4o: Please read the paper “Tech, Trauma and Testimony: The limits of AI in GBV Justice” and tell me what it says. List the most surprising, controversial and valuable statements made and conclude with recommendations for all relevant stakeholders.

ChatGPT-4o’s answer:
Summary of the Paper "Tech, Trauma and Testimony: The Limits of AI in GBV Justice"

Key Messages:

1. Incompatibility of AI and Trauma Narratives:

   • AI systems struggle to interpret the non-linear, fragmented, and emotionally complex nature of trauma testimonies. Survivors often recount their experiences in inconsistent ways due to PTSD, cultural differences, or memory gaps, which AI models can misinterpret as unreliable or non-credible.

   • Trauma does not fit neatly into structured data formats, leading to algorithmic dismissal of hesitant or disjointed testimonies, thereby reinforcing systemic silencing.

2. Patriarchal and Colonial Data Bias:

   • AI trained on historical legal, medical, and bureaucratic data inherits deeply embedded patriarchal and colonial biases, marginalizing survivors further. For instance, past legal standards often required "proof" of resistance in rape cases, a standard AI can replicate, effectively filtering out complex, nonlinear trauma narratives.

   • Examples include the UK Home Office's credibility assessment tools, which penalize asylum seekers for incoherent or inconsistent testimonies, a common feature of trauma narratives.

3. Automation of Institutional Control:

   • Rather than empowering survivors, AI often becomes a tool for institutional control, dictating whose testimonies are preserved and whose are dismissed, reinforcing power imbalances rather than dismantling them.

   • The reliance on structured, Western-centric data formats further marginalizes survivors from non-Western cultures, whose testimonies may be culturally distinct and linguistically challenging for AI to process.

4. Danger of “Neutral” AI Narratives:

   • The belief that AI can provide "neutral" or "objective" assessments of credibility is a dangerous myth, as the data it learns from is historically biased against women. For example, AI medical diagnostics have been found to underdiagnose women’s pain due to biased training data, a pattern likely to repeat in GBV justice contexts.

5. Memory Preservation Risks:

   • AI-driven memory preservation can strip survivors of control over their own narratives, transforming deeply personal accounts into data points for institutional use, often without meaningful survivor consent or control.

Most Surprising, Controversial, and Valuable Statements:

• Surprising: The paper argues that AI cannot merely replicate human biases but can amplify them through scalable, algorithmic exclusion, effectively institutionalizing misogyny and colonialism in digital form.

• Controversial: It directly challenges the idea of AI as a solution for justice, calling for a "feminist burning down" of techno-utopianism in favor of survivor-centered approaches, rejecting the notion that technological efficiency equates to fairness.

• Valuable: The emphasis on the need for "feminist epistemology" and "radical transparency" in AI systems, demanding that these technologies be reimagined, redesigned, or abandoned if they cannot serve survivors effectively.

Recommendations for Stakeholders:

1. For AI Developers and Tech Companies:

   • Abandon the myth of "neutral" data and integrate counter-archives that include marginalized voices and trauma-informed datasets.

   • Implement radical transparency, making AI systems challengeable and open to public scrutiny.

2. For Governments and NGOs:

   • Avoid relying on AI for credibility assessments in asylum and GBV cases until systems can account for trauma’s non-linear nature.

   • Prioritize survivor agency, allowing them to contest algorithmic decisions and control their narratives.

3. For Legal and Human Rights Organizations:

   • Recognize the limits of AI in justice contexts and emphasize human oversight over automated decision-making.

   • Develop guidelines that reject rigid credibility metrics, focusing on context and survivor perspectives.

4. For Feminist Advocates and Researchers:

   • Push for AI systems to be designed around feminist principles, ensuring that survivors are not just data points but agents of their own narratives.

   • Advocate for the dismantling of patriarchal legal standards that AI systems tend to replicate.