Towards WinoQueer: Developing a Benchmark for Anti-Queer Bias in Large Language Models
This addresses bias detection and mitigation for queer and trans people in AI systems, representing incremental work by adapting existing benchmark methods to a new domain.
The paper tackled the problem of anti-queer bias in large language models like BERT by introducing the WinoQueer benchmark and found that BERT exhibits significant homophobic bias, which can be mostly mitigated through finetuning on LGBTQ+ community-written data.
This paper presents exploratory work on whether and to what extent biases against queer and trans people are encoded in large language models (LLMs) such as BERT. We also propose a method for reducing these biases in downstream tasks: finetuning the models on data written by and/or about queer people. To measure anti-queer bias, we introduce a new benchmark dataset, WinoQueer, modeled after other bias-detection benchmarks but addressing homophobic and transphobic biases. We found that BERT shows significant homophobic bias, but this bias can be mostly mitigated by finetuning BERT on a natural language corpus written by members of the LGBTQ+ community.