Debiasing GPT-3 Job Ads

We hear about the impeccable achievements of GPT-3 models, but such large generative models come with their bias. On the show today, Conrad Borchers, a Ph.D. student in Human-Computer Interaction, joins us to discuss the bias in GPT-3 for job ads and how such large models can be de-biased. Listen to learn more!

Guest

Conrad Borchers: Conrad is a first-year PhD student at the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University, School of Computer Science. His research interests span the theme of leveraging data science to improve educational processes. They include the study of institutional and teacher use of social media, course workload and course selection in higher education, and the roles of teacher attention and student motivation in the efficacy of educational technologies.