A striking theory has taken the internet by storm—are ride-hailing apps charging iPhone users more than Android users for the same rides? Recent tests conducted by The Times of India (TOI) suggest this might not just be a conspiracy theory. Cab fares for identical routes in Chennai, checked simultaneously on iPhone and Android devices, revealed consistently higher prices for iOS users.While the pattern appeared more pronounced for shorter, single rides, this disparity isn’t conclusive evidence of bias. As TOI noted, the same searches on different days could yield varying results. However, the findings have reignited concerns about the not-so-clear pricing algorithms used by ride-hailing platforms.Meanwhile, Uber, responding to TOI’s queries, denied any policy of personalizing trip prices based on the user's phone type. Instead, the company attributed discrepancies to dynamic pricing factors such as real-time demand, estimated travel time, and distance. Ola did not respond to TOI's request for comment.What experts sayC Ambigapathy, managing director of Chennai-based ride-hailing platform Fastrack, claimed it’s technically feasible to tweak fares based on a user’s hardware. “It is child's play for companies to tweak fares based on hardware details while hiding behind the ‘dynamic pricing algorithm’ explanation,” he told TOI.Similarly, P Ravikumar, a former senior director at the Centre for Development of Advanced Computing (C-DAC), explained how machine learning frameworks like Google Cloud AI and Azure ML enable aggregators to incorporate variables such as device type, usage frequency, and search patterns to dynamically adjust pricing. TOI's report said that the claims have not been independently verified. An Intelligent Transport System expert involved in framing the Union government’s aggregator policy pointed out that fare surges aren’t limited to phone models. “These platforms rely on user behavior patterns to adjust pricing dynamically,” he told TOI. Frequent users and those who repeatedly check fares are often charged more, he added.Ambigapathy also argued that companies leverage past user data to predict behavior. “Once they identify a regular user, they inflate fares, confident that the user will eventually book,” he said. Meanwhile, Ravikumar called for greater transparency, stating, "If factors like estimated time, distance, and ride modes are consistent, users should not face discrimination based on their device."(With inputs from TOI)
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