  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.
