# Examining the Presence of Gender Bias in Customer Reviews Using Word Embedding

## TL;DR

This paper investigates whether customer reviews contain gender bias that algorithms learn and propagate, using word embeddings on large datasets, and discusses the implications for consumers and firms.

## Contribution

It demonstrates that algorithms trained on reviews learn gender stereotypes and analyzes the nature and impact of this bias.

## Key findings

- Algorithms learn gender bias from reviews.
- Bias can be positive for males or negative against females.
- Implications for consumer choice and ethical considerations.

## Abstract

Humans have entered the age of algorithms. Each minute, algorithms shape countless preferences from suggesting a product to a potential life partner. In the marketplace algorithms are trained to learn consumer preferences from customer reviews because user-generated reviews are considered the voice of customers and a valuable source of information to firms. Insights mined from reviews play an indispensable role in several business activities ranging from product recommendation, targeted advertising, promotions, segmentation etc. In this research, we question whether reviews might hold stereotypic gender bias that algorithms learn and propagate Utilizing data from millions of observations and a word embedding approach, GloVe, we show that algorithms designed to learn from human language output also learn gender bias. We also examine why such biases occur: whether the bias is caused because of a negative bias against females or a positive bias for males. We examine the impact of gender bias in reviews on choice and conclude with policy implications for female consumers, especially when they are unaware of the bias, and the ethical implications for firms.