An Empirical Study on Online Price Differentiation

Hupperich, Thomas; Tatang, Dennis; Wilkop, Nicolai; Holz, Thorsten

doi:10.1145/3176258.3176338

Cited by 21 publications

(11 citation statements)

References 18 publications

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“…And while price discrimination is often legal, laws in the United States prohibit similar kinds of opportunistic price changes [50], making algorithmic price discrimination an important behavior to capture and understand through algorithm audits. To this end, Mikians et al [111] conducted the earliest audit in our review, with similar follow-up studies conducted by Mikians et al [113], Hannak et al [74], Chen et al [27], and Hupperich et al [81].…”

Section: Discrimination In Pricingmentioning

confidence: 62%

Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits

Bandy

2021

Preprint

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While algorithm audits are growing rapidly in commonality and public importance, relatively little scholarly work has gone toward synthesizing prior work and strategizing future research in the area. This systematic literature review aims to do just that, following PRISMA guidelines in a review of over 500 English articles that yielded 62 algorithm audit studies. The studies are synthesized and organized primarily by behavior (discrimination, distortion, exploitation, and misjudgement), with codes also provided for domain (e.g. search, vision, advertising, etc.), organization (e.g. Google, Facebook, Amazon, etc.), and audit method (e.g. sock puppet, direct scrape, crowdsourcing, etc.). The review shows how previous audit studies have exposed public-facing algorithms exhibiting problematic behavior, such as search algorithms culpable of distortion and advertising algorithms culpable of discrimination. Based on the studies reviewed, it also suggests some behaviors (e.g. discrimination on the basis of intersectional identities), domains (e.g. advertising algorithms), methods (e.g. code auditing), and organizations (e.g. Twitter, TikTok, LinkedIn) that call for future audit attention. The paper concludes by offering the common ingredients of successful audits, and discussing algorithm auditing in the context of broader research working toward algorithmic justice.

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Section: Discrimination In Pricingmentioning

confidence: 62%

Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits

Bandy

2021

Preprint

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“…Botta and Klaus [ 14 ] qualitatively proposed that algorithmic price discrimination is different from offline differential pricing and is related to the collection of consumer information, which is a unique feature of the digital economy. With the wide application of big data and the gradual deepening of algorithm technology, the e-commerce company can price discriminate against consumers with great precision [ 4 ], and these were confirmed empirically [ 4 , 15 ]. The pricing ecosystem of the online platform is a dynamic pricing system [ 15 ].…”

Section: Related Literaturementioning

confidence: 99%

“…With the wide application of big data and the gradual deepening of algorithm technology, the e-commerce company can price discriminate against consumers with great precision [ 4 ], and these were confirmed empirically [ 4 , 15 ]. The pricing ecosystem of the online platform is a dynamic pricing system [ 15 ]. Algorithmic price discrimination [ 16 ], artificial intelligence techniques, and digital system fingerprints [ 15 ] enable the e-commerce company to have the ability of price discrimination.…”

Section: Related Literaturementioning

confidence: 99%

“…The pricing ecosystem of the online platform is a dynamic pricing system [ 15 ]. Algorithmic price discrimination [ 16 ], artificial intelligence techniques, and digital system fingerprints [ 15 ] enable the e-commerce company to have the ability of price discrimination. Price discrimination is not only widespread in the field of commodity sales, and there are also discrimination and price difference by using customer information in the field of online car-hailing [ 8 ] and the field of advertising recommendation [ 7 , 17 ].…”

Section: Related Literaturementioning

confidence: 99%

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Supervision Strategy Analysis on Price Discrimination of E-Commerce Company in the Context of Big Data Based on Four-Party Evolutionary Game

Xiao

2022

Computational Intelligence and Neuroscience

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This paper focuses on the phenomenon of “big data killing” implied in e-commerce and discusses how to take the government as the lead to coordinately supervise the price discrimination behavior of e-commerce companies towards loyal customers. First, the four-party evolutionary game model of the government regulatory department, e-commerce platform, e-commerce company, and consumer is built. Second, the stability of the strategy choice of each game subject is analyzed. On this basis, the evolutionary stable strategy in the system based on First Law of Lyapunov is explored. Finally, the influences of key elements on system evolution are simulated and analyzed by MATLAB2021. Results demonstrate that (1) the government supervision mechanism can effectively supervise the price discrimination of e-commerce company based on big data to loyal customers; (2) when the government chooses the strict supervision strategy, reducing the information supervision cost of the e-commerce platform and the strict supervision cost of the government enable the government and the e-commerce platform to coordinate supervision and make the e-commerce company incline to choose the nondifferential pricing strategy; (3) when the government chooses the loose supervision strategy, reducing the information supervision cost of the e-commerce platform and increasing the probability of consumer discovering differential pricing and the penalties for differential pricing of e-commerce company enable the e-commerce platform and consumer to coordinate supervision, and make the e-commerce company incline to choose the nondifferential pricing strategy. The results of this study can provide theoretical guidance for the government and companies to make beneficial strategic decisions in the development of e-commerce.

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“…Though many countries have laws against direct discrimination, it is still a common occurrence due to the difficulty of proving discrimination in individual cases, especially algorithmic ones [118]. In domains such as hiring [89,90], advertising [22,29], and pricing [55,72], direct forms of discrimination, algorithmically mediated or not, are relatively common. For sectors like advertising, discriminatory practices are often justified by claims that differential treatment results in more helpful services, which may in fact be true.…”

Section: Encroachments On Individual Privacymentioning

confidence: 99%

Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness

Andrus¹,

Villeneuve²

2022

Preprint

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Most proposed algorithmic fairness techniques require access to demographic data in order to make performance comparisons and standardizations across groups, however this data is largely unavailable in practice, hindering the widespread adoption of algorithmic fairness. Through this paper, we consider calls to collect more data on demographics to enable algorithmic fairness and challenge the notion that discrimination can be overcome with smart enough technical methods and sufficient data. We show how these techniques largely ignore broader questions of data governance and systemic oppression when categorizing individuals for the purpose of fairer algorithmic processing. In this work, we explore under what conditions demographic data should be collected and used to enable algorithmic fairness methods by characterizing a range of social risks to individuals and communities. For the risks to individuals we consider the unique privacy risks of sensitive attributes, the possible harms of miscategorization and misrepresentation, and the use of sensitive data beyond data subjects' expectations. Looking more broadly, the risks to entire groups and communities include the expansion of surveillance infrastructure in the name of fairness, misrepresenting and mischaracterizing what it means to be part of a demographic group, and ceding the ability to define what constitutes biased or unfair treatment. We argue that, by confronting these questions before and during the collection of demographic data, algorithmic fairness methods are more likely to actually mitigate harmful treatment disparities without reinforcing systems of oppression. Towards this end, we assess privacy-focused methods of data collection and use and participatory data governance structures as proposals for more responsibly collecting demographic data.CCS Concepts: • Security and privacy → Social aspects of security and privacy; Privacy protections; Economics of security and privacy; • Social and professional topics → User characteristics; Gender; Sexual orientation.

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An Empirical Study on Online Price Differentiation

Cited by 21 publications

References 18 publications

Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits

Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits

Supervision Strategy Analysis on Price Discrimination of E-Commerce Company in the Context of Big Data Based on Four-Party Evolutionary Game

Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness

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