On Measuring Bias in Online Information

Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems.Comment: 6 pages, 1 figur

Third Party Tracking in the Mobile Ecosystem

Third party tracking allows companies to identify users and track their behaviour across multiple digital services. This paper presents an empirical study of the prevalence of third-party trackers on 959,000 apps from the US and UK Google Play stores. We find that most apps contain third party tracking, and the distribution of trackers is long-tailed with several highly dominant trackers accounting for a large portion of the coverage. The extent of tracking also differs between categories of apps; in particular, news apps and apps targeted at children appear to be amongst the worst in terms of the number of third party trackers associated with them. Third party tracking is also revealed to be a highly trans-national phenomenon, with many trackers operating in jurisdictions outside the EU. Based on these findings, we draw out some significant legal compliance challenges facing the tracking industry.Comment: Corrected missing company info (Linkedin owned by Microsoft). Figures for Microsoft and Linkedin re-calculated and added to Table

Online user behavioural modeling with applications to price steering

Price steering is the practice of “changing the order of search results to highlight specific products” and products prices. In this paper, we show an initial investigation to quantify the price steering level in search results shown to different kind of users on Google Shopping. We mimic the category of affluent users. Affluent users visit websites offering expensive services, search for luxury goods and always click on the most costly items results at Google Shopping. The goal is checking if users trained in specific ways get different search results, based on the price of the products in the results. Evaluation is based on well known metrics to measure page results differences and similarities. Experiments are automised, rendering large-scale investigations feasible. Results of our experiments, based on a preliminary experimental setting, show that users trained on some particular topics are not always influenced by previous search and click activities. However, different trained users actually achieve different search results, thus paving the way for further investigation

Web Transparency for Complex Targeting: Algorithms, Limits, and Tradeoffs

International audienceBig Data promises important societal progress but exacerbates the need for due process and accountability. Companies and institutions can now discriminate between users at an individual level using collected data or past behavior. Worse, today they can do so in near perfect opacity. The nascent field of web transparency aims to develop the tools and methods necessary to reveal how information is used, however today it lacks robust tools that let users and investigators identify targeting using multiple inputs. Here, we formalize for the first time the problem of detecting and identifying targeting on combinations of inputs and provide the first algorithm that is asymptotically exact. This algorithm is designed to serve as a theoretical foundational block to build future scalable and robust web transparency tools. It offers three key properties. First, our algorithm is service agnostic and applies to a variety of settings under a broad set of assumptions. Second, our algorithm's analysis delineates a theoretical detection limit that characterizes which forms of targeting can be distinguished from noise and which cannot. Third, our algorithm establishes fundamental tradeoffs that lead the way to new metrics for the science of web transparency. Understanding the tradeoff between effective targeting and targeting concealment lets us determine under which conditions predatory targeting can be made unprofitable by transparency tools

Investigating Personalized Price Discrimination of Textile-, Electronics- and General Stores in German Online Retail

Developers of pricing strategies in e-commerce businesses see a wide range of opportunities for deploying online price discrimination techniques given their ability to track consumers’ online identity and behavior. In theory, an increasing use of personal data enables organizations to show every single consumer their own personalized price, which is determined by the consumer’s characteristics, e.g. age, gender, surfing history, or location. This paper aims to explore the existence of online price discrimination activities within the German ecommerce market using a three-method approach. First, inquiring the online retailers via email and investigating their public documents; second, surveying students; and third, using a software crawler to simulate surfing activity. Our results do not provide any evidence of individualized price discrimination, which, we argue, is due to economic and political reasons, not technical reasons

Consumer Privacy and Product Steering versus Price Discrimination in the Online Market

The following master thesis aims to study consumer privacy, where I focus on price discrimination and steering in the online market. I look closer at whether consumers will benefit from revealing their preferences or not to the seller. The consumer will need to consider the benefits of more accurate recommendations and possible consequences of higher product prices. My topic question is as follows: Will consumers benefit from voluntarily disclosing their information to the seller? I present two models by Hidir and Vellodi (2021) and Ichihashi (2020), to help find answers to the topic question. The models study the price implications of consumers’ privacy and welfare in the online market. Hidir and Vellodi (2021) focus on price discrimination and introduce incentive-compatible market segmentation. To ensure trade over relevant products, Hidir and Vellodi (2021) state that the consumer needs to partially reveal their information with pooling segments wide enough to keep the prices low and narrow enough to get trade with relevant products. Ichihashi (2020), with a focus on steering, studies a multi-product seller either with a commitment or no-commitment pricing regimes. A consumer discloses information to the seller, which learns the consumer’s preferences, sets prices, and makes product recommendations.MasteroppgaveECON391MASV-SØKPROF-SØ

ALGORITMOS DE PRECIFICAÇÃO E DIREITO CONCORRENCIAL

This article intends to answer if the if the use of price algorithms by market agents may result in antitrust practices. In this effort, it revises the literature about the matter, in other to elaborate a case study of the hypothesis theoretically formulated, taking Brazilian legal context as a reference. It starts by reinstating the conceptual delineations associated to price algorithms. After that, the work contextualizes the use of price algorithms in platform markets, recovering some theoretical contributions from studies related to two-sided markets. Subsequently, the work formulates two hypotheses in which the use of price algorithms may result in antitrust practices, grounding them on previous works from antitrust authorities. Finally, the work uses the jurisprudential systematization of the article 36 from the Brazilian Antitrust Statute to verify if the practices previously identified may be understood as antitrust illicit conducts accordingly to Brazilian Law. The study concludes that price algorithms may lead to antitrust practices in limited and specific conditions.El artículo pretende responder en qué medida el uso de algoritmos de precios por parte de los agentes del mercado puede derivar en la práctica de delitos anticompetitivos. Par este fin, se realiza una revisión bibliográfica sobre el tema, con el fin de, a partir de los escenarios diseñados en un plan hipotético, elaborar un estudio de caso, teniendo como base el contexto jurídico brasileño, de las hipótesis formuladas teóricamente. El trabajo comienza recuperando los contornos conceptuales asociados con los algoritmos de precios, en la teoría de la regulación algorítmica. En seguida, los algoritmos precios son contextualizados en el escenario de mercado en plataforma, con contribuciones teóricas de la literatura asociada con el análisis de mercado de dos lados. Luego, con base en los informes de las autoridades de competencia, se formulan dos hipótesis en las cuales los algoritmos de precios pueden tener repercusiones competitivas. Finalmente, se somete las hipótesis para el análisis, basadas en la sistematización doctrinal del artículo 36 de la Ley. 12.529 / 2011 y las características estructurales de los delitos contrarios a la competencia. Se concluye que los algoritmos de fijación de precios pueden conducir a la práctica de delitos contra el comercio en condiciones específicas y limitadas.O artigo objetiva responder em que medida a utilização de algoritmos de precificação por agentes de mercado pode resultar na prática de ilícitos anticoncorrenciais. Para tanto, realiza-se a revisão de literatura sobre o tema, para, a partir dos cenários desenhados em plano hipotético elaborar estudo de caso, tendo o contexto jurídico brasileiro por base, das hipóteses formuladas teoricamente. Inicia-se o trabalho pela recuperação dos contornos conceituais associados aos algoritmos de precificação, enquadrados dentro da teoria da regulação algorítmica. Em seguida, contextualiza-se os algoritmos de precificação dentro do cenário dos mercados em plataforma, com aportes teóricos da literatura associada à análise dos mercados de dois lados. Em seguida, com base nos relatórios de autoridades concorrenciais, formula-se duas hipóteses em que os algoritmos de precificação podem ter repercussões concorrenciais. Por fim, submete-se às hipóteses à análise jurídica, tomando com base a sistematização doutrinária do artigo 36 da Lei. 12.529/2011 e as características estruturais dos ilícitos anticoncorrenciais. Conclui-se que os algoritmos de precificação podem levar à prática de ilícitos anticonrrenciais, observadas condições específicas