Problem Statement

The problem we are focusing on is minimizing subjective biases in written language. Subconscious subjective biases are often embedded in everyday communications, even when a writer aims to be neutral. In a society that is more and more diverse and intersectional, the cultural dynamic has to adjust to prevent biases, marginalization and micro-aggressions in language. In other words, there is an imperative in our society to minimize bias, and even while many agree with this sentiment, there aren’t current tools available to automatically check one’s own biases in their writing. In the field of NLP, there aren’t any pre-existing great bias measurement tools. Existing solutions focus on toxic language or hate speech or on measuring bias in models directly. While subjective biases are more nuanced and difficult to detect, there is an opportunity to address them efficiently through fine-tuning pre-existing language models. Our product aims to bridge the gap between current solutions and the nuance of these subjective biases to help writers become more objective in their communications. As such, our primary target users are HRBPs and communication specialists seeking a bias mitigation tool. The market opportunity is in this sector is vast as current existing solutions are centered primarily around less nuanced biases, and there is a lack of user-facing products.