LLMs and attentional injustice in philosophy

Abstract An attentional injustice occurs when some person or group is given disproportionate attention. In academic philosophy, it has been observed that members of various marginalised groups are cited less frequently than white men, both in academic papers and in university syllabi. This has downstream effects on gender/racial representation in Philosophy more generally. Because Large Language Models produce text due to prediction on the basis of training data, they are likely to replicate existing biases. As a result, without precautionary measures, LLM use is likely to exacerbate attentional injustice in philosophy and contribute to the continuing of current demographic distortions.