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Stratyfy Joins Groundbreaking Machine Learning Research
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Stratyfy
“This research offers an opportunity to make meaningful strides towards removing unconscious bias in decisions that have practical, everyday impacts. Stratyfy welcomes the chance to present our unique approach, and is honored to be part of this important work.” NEW YORK (PRWEB) May 25, 2021 Stratyfy is pleased to announce its participation in critical research focused on improving the transparency and fairness of machine learning in the credit industry.
This unprecedented research will seek to address issues surrounding transparency and fairness that have made many lenders hesitant to adopt machine learning underwriting models at scale. The research will be led by FinRegLab, the industry leading innovation center in collaboration with researchers from the Stanford University Graduate School of Business (GSB) and key financial,
Negative stereotypes about men and women creep into performance reviews, research finds.
A take-charge attitude at work typically earns men positive performance reviews, but for women, assertiveness only gets them so far. Although workplace evaluations are supposed to be merit-based, the study finds that gender bias too often influences how supervisors rate employees, resulting in women having to meet a higher bar than their male colleagues to advance professionally.
Published in the
American Sociological Review, the paper pinpoints how and when managers’ beliefs about gender enter their evaluations of workers.
“Where we find the bigger biases are in evaluations of people’s personalities, their future potential, and on the mentions of exceptionalism,” says study coauthor Shelley J. Correll, professor of organizational behavior (by courtesy) at Stanford University Graduate School of Business. “So if we want to get rid of biases, we need to look at the areas where biases