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This position paper analyzes the skewed distribution of diversity efforts within NLP, finding a concentration in fairness-related areas driven by specific incentives and biases. The authors investigate researcher demographics across NLP subfields to reveal disparities and propose recommendations for broader inclusivity. Their analysis highlights the need to disrupt feedback loops that perpetuate these imbalances and address geographical and linguistic barriers to participation.
NLP's diversity efforts are hyper-focused on fairness, leaving other subfields behind due to systemic biases that marginalize researchers and reinforce existing power structures.
This position paper argues that recent progress with diversity in NLP is disproportionately concentrated on a small number of areas surrounding fairness. We further argue that this is the result of a number of incentives, biases, and barriers which come together to disenfranchise marginalized researchers in non-fairness fields, or to move them into fairness-related fields. We substantiate our claims with an investigation into the demographics of NLP researchers by subfield, using our research to support a number of recommendations for ensuring that all areas within NLP can become more inclusive and equitable. In particular, we highlight the importance of breaking down feedback loops that reinforce disparities, and the need to address geographical and linguistic barriers that hinder participation in NLP research.