Measuring Price Discrimination and Steering on E-commerce Web Sites

By Anikó Hannák, Gary Soeller, David M. J. Lazer, Alan Mislove, Christo Wilson. 2014

347 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Marketing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

In Marketing

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Social data are being leveraged to make inferences about how much to pay for a product (Hannak et al., 2014), about the likelihood of being a terrorist or about users health (Yom-Tov, 2016) and employability (Rosenblat et al., 2014).”
Discrimination through Optimization (2019, background): “tion; thus, most researchers treat them as “black boxes” and observe their reactions to di˛erent inputs. Among most notable results, researchers have shown price discrimination in online retail sites [40], gender discrimination in job sites [15, 41], stereotypical gender roles re-enforced by online ”
What Makes a Dark Pattern... Dark?: Design Attributes, Normative Considerations, and Measurement Methods (2021, background): “know why they are shown a limited set of choices [23].”
Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit (2015, background): “3), the field of web transparency [1,3,6,17,18,26,27,29,47,48] is closest to our work; indeed, our team contributed two scalable and generic system designs [26, 27].”
Online Tracking: A 1-million-site Measurement and Analysis (2016, background): “Many other practices that raise privacy or ethical concerns have been studied: price discrimination, where a site shows different prices to different consumers for the same product [19, 63]; steering, a gentler form of price discrimination where a product search shows differently-priced results for ”
Peeking Beneath the Hood of Uber (2015, methodology): “[14] determined the features used by Google Search to personalize content, while [13,20,21] measure price discrimination and steering on e-commerce sites.”
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media (2017, background): “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.”
A Human-Centered Approach to Algorithmic Services: Considerations for Fair and Motivating Smart Community Service Management that Allocates Donations to Non-Profit Organizations (2017, background): “Recent research has shown that machine learning algorithms can make unfair decisions due to the unintended consequences of choices made in the development process or biases in the training data set [15, 34].”
Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr (2017, background): “Successful prior audits have looked at personalization on search engines [30, 35], localization of online maps [54], social network news-feeds [25], online price discrimination [31, 43, 44], dynamic pricing in e-commerce [15], and the targeting of online advertisements [29, 38].”
Potential for Discrimination in Online Targeted Advertising (2018, background): “…in online services ranging from discriminatory pricing on e-commerce and travel sites like Staples (Mikians et al., 2012) and Hotels.com (Hannák et al., 2014) to discriminatory prioritization of service requests and oﬀer-ings from certain users over others in crowdsourcing and social…”
Auditing radicalization pathways on YouTube (2019, background): “Previous large scale algorithmic auditing include measuring discrimination on AirBnB [14], personalization onweb search [19] and price discrimination on e-commerce web sites [20].”
The fallacy of inscrutability (2018, background): “city to patrol [36], and in e-commerce platforms [35].”