Peeking Beneath the Hood of Uber

By Le Chen, Alan Mislove, Christo Wilson. ACM/SIGCOMM Internet Measurement Conference, 2015

305 citations recorded by the source corpus.

Subfields: Automotive Engineering, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/d4d076c8a44e578250e6c7239b95f228fd0bc2a2

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Citations in the corpus

Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products (2019, background): “In several implemented audit studies, vendor names are kept anonymous [6] or the scope is scaled down to a single named target [24, 28, 37].”
The Bouncer Problem: Challenges to Remote Explainability (2019, methodology): “their explainability, as enforced by new protocols. A second line of research may be the collaboration of users observations for spotting the attack in an automated way.Indeed, as done by Chen et al. [11] for understanding the impact of Uber surge pricing on passengers and drivers (by deploying 43 U”
Algorithmic management in a work context (2021, background): “In platform-mediated gig work, considerable attention has been levied to “unboxing” the algorithms, trying to ascertain how the different algorithmic systems function (Chen et al., 2015; Van Doorn, 2020).”
Addressing Age-Related Bias in Sentiment Analysis (2018, background): “However, given that many algorithms are proprietary, researchers have also attempted to decipher algorithms by interpreting output while varying inputs [15,22] –”
Quantifying the Invisible Labor in Crowd Work (2021, background): “This dynamic emerged after Uber changed its pricing algorithm and did not provide transparency on how it functioned [12].”
The Effect of Population and "Structural" Biases on Social Media-based Algorithms: A Case Study in Geolocation Inference Across the Urban-Rural Spectrum (2017, background): “Additionally, much of the algorithmic accountability literature has focused on detecting algorithmic bias when faced with a black-box system (e.g. [7,35,60,63]).”
Dynamic pricing and matching in ride‐hailing platforms (2019, unknown): “Such driver response to earnings can occur over a short time horizon (Chen et al. 2015).”
Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr (2017, result): “Other studies present quantitative results from observational studies of workers [47, 14].”
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media (2017, unknown): “Therefore, building on previous studies that have adopted this “black box” view for an algorithmic system while auditing it [4, 9, 14, 15, 20], we characterized the bias of the ranking algorithm in Twitter’s search platform, without knowing its internals.”
The Future of Work in the ‘Sharing Economy’. Market Efficiency and Equitable Opportunities or Unfair Precarisation? (2016, unknown): “, 2015; Rosenblat & Stark, 2015) and quantitative (Chen et al., 2015) analyses have focussed on control and surveillance by algorithm in Uber.”
Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns (2019, background): “Studies in the area of algorithm audits have provided essential insights into the technical workings of AI-powered, black-boxed systems, showing problematic implications, for example, in terms of bias (Chen et al., 2015; Sandvig et al., 2014; Venkatadri et al., 2018).”