BIAS DETECTION MODEL

Genderlens

Audit text in real time to detect, flag, and neutralize gender-coded language to prevent implicit bias

Try out the latest prototype

WHY LANGUAGE MATTERS

76%

of high-performing women receive negative feedback, compared with 2% of high-performing men.

7×

Women are more likely to receive feedback directed at personality rather than concrete work output.

78%

Women are more likely to be labeled “emotional” in written performance reviews.

0.83%

of male-dominated job advertisements consists of masculine-coded language.

PURPOSE

Each word impacts inclusion

Through GenderLens Zarisana aims to enable users to detect gender coded language and analyze emails, job descriptions, performance reviews, etc in order to prevent discimination from implicit bias.

Zarisana