Digital curation is defined as the act of using consumer data and algorithms to provide personalized, automatic, and machine-driven selections of online content. Algorithm-based curation allows firms to provide content that is personalized to each individual consumer on popular search engines such as Google and social media sites like Pinterest. While research suggests that more personalized content increases intended consumer outcomes, there is a darker side to the use of algorithms in personalization, namely the perpetuation of societal biases. “Algorithmic bias” describes systematic errors in a computer system that creates unfair or unethical outcomes, such as favoring one group of consumers over another. This research explores the consumer’s perception of algorithmic bias in online curation across three studies. The findings suggest that self-congruence and social identity are key drivers of consumer satisfaction in personalization, and that customization should also be considered by firms as a bias mitigation strategy.