Increasingly, companies are solving the problems of hiring backlogs using artificial intelligence. AI, however, has been an imperfect solution for hiring in the past, to say the least. Just as with human candidate selection, it can be subject to biases that lead to unfair hiring practices and good, well-deserving candidates unfortunately getting passed over.
That's why Melbourne, Australia-based Sapia.ai, a provider of workplace AI solutions, recently launched its new generative AI model called SAIGE. The company says it was designed to transform how organizations globally select, manage and optimize their workforce, and overcome the problems of bias.
SAIGE, which leverages large language model (LLM) capabilities, evaluates candidates using a blind chat interview, providing comprehensive scoring along with a set of job-related competencies, including transparent explanations for its assessments. Compared to resume parsers that evaluate a historical record encoded with various biases, SAIGE allows organizations to have a conversation with every candidate and assess their potential to be a good fit for the role, according to the company.
Unlike solutions built on existing general-purpose LLMs like GPT-4 or PaLM, SAIGE’s foundation is a proprietary LLM fine-tuned on a massive dataset of over 3.5 million unique interviews spanning 15 million responses across industries, job families and geographies. This training data, combined with oversight from industrial and organizational psychology experts and empirical research from Sapia.ai research division Sapia.ai Labs, provides SAIGE with significant assessment capabilities governed by robust ethical policies to mitigate human biases that may impact candidate evaluations.
“We have been on a mission to humanize the way people are evaluated, and SAIGE is the next step in this journey. As much as ChatGPT was a revolution in the way we interact with AI, SAIGE is a quantum leap in the way people will be assessed and coached,” said Sapia.ai founder and CEO Barb Hyman. “Structured interviews have been proven to lead to better hiring outcomes and reduce bias in the selection process. Now with SAIGE, organizations have a way to consistently interview and evaluate at scale using a rubric set by experts, and also generate detailed interview notes. What we are really excited about is the potential SAIGE has on providing coaching to hiring managers and new employees on setting them up for success.”
Edited by
Alex Passett
