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Systematic gaps in AI evaluation reporting are exposed, revealing inconsistencies that hinder reliable comparisons across thousands of models and benchmarks.
Despite growing interest, queer NLP research remains largely reactive, highlighting biases instead of building proactive solutions, leaving significant opportunities for stakeholder-driven and intersectional approaches.
