Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements
Conrad Borchers; Dalia Sara Gala; Benjamin Gilburt; Eduard Oravkin; Wilfried Bounsi; Yuki M. Asano; Hannah Rose Kirk · 2022 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
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
Excerpt shown for reference under fair use — read the full paper at the publisher.
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Metadata source: arXiv