The AI Paradox: SSH Scholars Warn of Erasure in Algorithmic Academic Review

As generative AI becomes a staple in academic writing and peer review, a global coalition of SSH scholars is raising the alarm about the potential 'algorithmic erasure' of nuanced human thought. A white paper released this week argues that current AI-driven peer-review systems are biased toward standardized language and conventional viewpoints, often flagging innovative or culturally specific SSH research as 'outliers' or 'low-quality.' This is particularly concerning for PhD students who are exploring niche theories or utilizing indigenous methodologies that do not conform to Western-centric data patterns used to train these AI models. The report warns that if universities and journals continue to outsource the first stage of review to AI, we risk a 'homogenization of knowledge' where only safe, predictable research is published. Doctoral candidates are finding themselves in a difficult position, feeling pressured to modify their research language to be 'AI-friendly' rather than intellectually rigorous. The coalition is calling for a mandatory 'Human-in-the-Loop' policy for all SSH academic evaluations to ensure that the depth, irony, and cultural context essential to the humanities are not lost in digital translation.