Battling Bias in Large Language Models

Photo by Clint Adair on Unsplash

These days, AI is everywhere. It may not resemble the sci-fi versions of it, fed to us through popular culture, but its potential is growing every year, and it is going to impact every industry and every business, from the products we use to the work we do and the way we drive.

Since data is one of the key ingredients to any AI-powered application, one of the major concerns surrounding GPT-3 is the chance of it replicating the human biases present in the training data.

OpenAI’s Playground depicting a GPT-3 completion for a prompt containing the word ‘Muslims’

More recently (June 10, 2021), OpenAI published a study in which they claim to have mitigated bias in GPT-3 (Solaiman and Dennison). To do so, they created a values-targeted dataset called Process for Adapting Language Models to Society (PALMS) that consists of carefully curated question-answer pairs targeting sensitive topics.

Despite the undeniable difficulties in detecting, isolating, and mitigating biases, it can’t be this easy for a model to throw sexist and racial slurs when presented with seemingly neutral prompts.

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We are a low-code AI platform powering the digital transformation of businesses: customer care and beyond || www.automaise.com

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Automaise

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We are a low-code AI platform powering the digital transformation of businesses: customer care and beyond || www.automaise.com

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