CHENNAI: It’s a theory that has been doing the rounds on social media, but is it true? Some commuters have noticed a curious disparity in cab fares displayed simultaneously on Android devices and iPhones for identical rides, which has left them wondering if pricing algorithms on ride-hailing apps are programmed to charge Apple users higher.
TOI used an iPhone and Android device simultaneously to search for rides to the same destinations from three locations in Chennai. In each case, the fare displayed was higher on iOS (see graphic).
A caveat – this is by no means conclusive evidence. The same searches on another day may throw up different results. Also, the pattern seemed to be limited to single rides and more pronounced over relatively shorter distances. For the record, Uber said it did not have a policy of personalising trip pricing based on the prospective rider’s phone. It attributed disparities, if any, to factors such as estimated time, distance, and real-time demand for cabs in a specific area. Ola did not respond to queries from TOI.
Once firms identify regular user, they inflate fares: Expert
Experts suggest the disparities stem from how ride-hailing apps access hardware data to which users are required to give consent while installing an app.
C Ambigapathy, managing director of ride-hailing platform Fastrack in Chennai, said the central server could easily generate fare estimates tailored to the user’s device. “It is child’s play for companies to tweak fares based on hardware details while hiding behind the ‘dynamic pricing algorithm’ explanation,” he said.
P Ravikumar, former senior director of Centre for Development of Advanced Computing (C-DAC) in Thiruvananthapuram, said aggregators were known to use rapid development tools such as machine learning frameworks (Google Cloud AI, and Azure ML) to refine pricing algorithms. These tools can incorporate variables like device type, app usage frequency, and search patterns to dynamically adjust fares.
TOI was unable to independently verify whether that is indeed the case.
An Intelligent Transport System expert involved in framing the Union govt’s aggregator policy said fare surges weren’t limited to differences between phone models. He pointed out that this also applies to frequent app users and those who repeatedly check fares on the same device. “These platforms rely on user behaviour patterns to adjust pricing dynamically,” the expert said.
Ambigapathy pointed out that companies leverage past data to gauge user loyalty and trust. “Once they identify a regular user, they inflate fares, confident that the user will eventually book, even if they wait for prices to drop, although they never do.”
Ravikumar said it was time for companies to be transparent about their pricing models. “If factors like estimated time, distance, and ride modes are consistent, users should not face discrimination based on their device.”