Handling Gender Biases in E-Commerce Product Specifications

Abstract

Fair computing has emerged as a key area of artificial intelligence (AI), and especially machine learning (ML). Identification and mitigation of several types of biases, spanning over data and machine learning models, has attracted both research and regulatory attention. In this work, we explore the presence and degree of gender bias in product descriptions featured on e-commerce websites. Using the knowledge obtained in analysis, we recommend methods to debias the product description, using a product feature level text selection scheme, sourced by customer reviews. Our work is the first of its kind, that establishes a baseline for enhancing the cross-gender acceptability of product descriptions, and proposes a framework for e-retailers to provide such gender-neutral product descriptions.