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Peeking Beneath the Hood of Uber

Published: 28 October 2015 Publication History

Abstract

Recently, Uber has emerged as a leader in the "sharing economy". Uber is a "ride sharing" service that matches willing drivers with customers looking for rides. However, unlike other open marketplaces (e.g., AirBnB), Uber is a black-box: they do not provide data about supply or demand, and prices are set dynamically by an opaque "surge pricing" algorithm. The lack of transparency has led to concerns about whether Uber artificially manipulate prices, and whether dynamic prices are fair to customers and drivers. In order to understand the impact of surge pricing on passengers and drivers, we present the first in-depth investigation of Uber. We gathered four weeks of data from Uber by emulating 43 copies of the Uber smartphone app and distributing them throughout downtown San Francisco (SF) and midtown Manhattan. Using our dataset, we are able to characterize the dynamics of Uber in SF and Manhattan, as well as identify key implementation details of Uber's surge price algorithm. Our observations about Uber's surge price algorithm raise important questions about the fairness and transparency of this system.

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Published In

cover image ACM Conferences
IMC '15: Proceedings of the 2015 Internet Measurement Conference
October 2015
550 pages
ISBN:9781450338486
DOI:10.1145/2815675
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 28 October 2015

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Author Tags

  1. algorithm auditing
  2. sharing economy
  3. surge pricing
  4. uber

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  • Research-article

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  • NSF

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IMC '15
Sponsor:
IMC '15: Internet Measurement Conference
October 28 - 30, 2015
Tokyo, Japan

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IMC '15 Paper Acceptance Rate 31 of 96 submissions, 32%;
Overall Acceptance Rate 277 of 1,083 submissions, 26%

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IMC '24
ACM Internet Measurement Conference
November 4 - 6, 2024
Madrid , AA , Spain

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  • (2024)The ``Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based BiasesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642669(1-18)Online publication date: 11-May-2024
  • (2024)AI auditing: The Broken Bus on the Road to AI Accountability2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)10.1109/SaTML59370.2024.00037(612-643)Online publication date: 9-Apr-2024
  • (2024)Seeking in Ride-on-Demand Service: A Reinforcement Learning Model With Dynamic Price PredictionIEEE Internet of Things Journal10.1109/JIOT.2024.340711911:18(29890-29910)Online publication date: 15-Sep-2024
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