Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements
This addresses bias in AI-generated job ads, which can impact hiring fairness, but is incremental as it builds on existing debiasing research.
The study tackled bias in GPT-3-generated job advertisements by evaluating zero-shot generation and debiasing methods, finding that fine-tuning on unbiased real ads improved realism and reduced bias, while prompt-engineering did not.
The growing capability and availability of generative language models has enabled a wide range of new downstream tasks. Academic research has identified, quantified and mitigated biases present in language models but is rarely tailored to downstream tasks where wider impact on individuals and society can be felt. In this work, we leverage one popular generative language model, GPT-3, with the goal of writing unbiased and realistic job advertisements. We first assess the bias and realism of zero-shot generated advertisements and compare them to real-world advertisements. We then evaluate prompt-engineering and fine-tuning as debiasing methods. We find that prompt-engineering with diversity-encouraging prompts gives no significant improvement to bias, nor realism. Conversely, fine-tuning, especially on unbiased real advertisements, can improve realism and reduce bias.