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Ride-sharing represents a sustainable transportation paradigm that is beneficial to human, society, and environment. Common ride-sharing pricing approaches determine prices for riders through optimizing one or more figures of merit, e.g., the profit or revenue. However, they overlook an important issue— <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fairness</i>—which, when perceived by the riders, can critically affect their degree of satisfaction. In this work, we take the initiative to consider an intuitive and appropriate notion of individual fairness called fairness-in-hindsight for riders in ride-sharing pricing. We study the problem of online fair pricing of shared rides (which allow multiple riders to share one ride) with an aim to maximize the profit of the ride-sharing operator/platform. We design an online fair ride-sharing pricing algorithm called OnFairRP, which comprises phases of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning, transition</i>, and exploitation. We prove that OnFairRP has a sub-linear regret bound and can guarantee the fairness between riders. Our extensive performance evaluations using real-world data traces of ride-sharing demonstrate the advantages of OnFairRP over benchmarking schemes including commonly used methods with or without fairness guarantee.