Related papers: Measuring Lay Reactions to Personal Data Markets
We study mechanism design in environments where agents have private preferences and private information about a common payoff-relevant state. In such settings with multi-dimensional types, standard mechanisms fail to implement efficient…
Data markets are emerging as key mechanisms for trading personal and organizational data. Traditional data pricing studies -- such as query-based or arbitrage-free pricing models -- mainly emphasize price consistency and profit maximization…
Open data are held to contribute to a wide variety of social and political goals, including strengthening transparency, public participation and democratic accountability, promoting economic growth and innovation, and enabling greater…
Online marketplace designers frequently run A/B tests to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect estimates obtained through Bernoulli…
We study the problem of collaborative machine learning markets where multiple parties can achieve improved performance on their machine learning tasks by combining their training data. We discuss desired properties for these machine…
Individuals are often influenced by the behavior of others, for instance because they wish to obtain the benefits of coordinated actions or infer otherwise inaccessible information. In such situations this social influence decreases the ex…
Matching markets are of particular interest in computer science and economics literature as they are often used to model real-world phenomena where we aim to equitably distribute a limited amount of resources to multiple agents and…
As AI agents increasingly act on behalf of human stakeholders in economic settings, understanding their behavior in complex market environments becomes critical. This article examines how Large Language Models coordinate on markets that are…
We report from a study performed in ten European countries, where we asked about attitudes and behaviour towards data sharing behaviour. We looked into the differences between members of age groups. We find that there are more similarities…
While users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this "privacy paradox" is that when an individual shares her data, it is not just her…
Perception of privacy is a contested concept, which is also evolving along with the rapid proliferation and expansion of technological advancements. Information systems (IS) applications incorporate various sensing infrastructures,…
Machine learning algorithms are increasingly employed to price or value homes for sale, properties for rent, rides for hire, and various other goods and services. Machine learning-based prices are typically generated by complex algorithms…
This paper proposes a regression market for wind agents to monetize data traded among themselves for wind power forecasting. Existing literature on data markets often treats data disclosure as a binary choice or modulates the data quality…
Social aspects of software projects become increasingly important for research and practice. Different approaches analyze the sentiment of a development team, ranging from simply asking the team to so-called sentiment analysis on text-based…
Online platforms increasingly offer "paid" ad-free subscriptions as an alternative to the traditional "free" ad-based model. The transition to ad-free models ostensibly removes advertising as a key justification for data processing under…
This paper explores the design of a balanced data-sharing marketplace for entities with heterogeneous datasets and machine learning models that they seek to refine using data from other agents. The goal of the marketplace is to encourage…
Modern privacy regulations, such as the General Data Protection Regulation (GDPR), address privacy in software systems in a technologically agnostic way by mentioning general "technical measures" for data privacy compliance rather than…
Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancement of the field by highlighting shortcomings and proposing…
Online platforms collect rich information about participants and then share some of this information back with them to improve market outcomes. In this paper we study the following information disclosure problem in two-sided markets: If a…
Researchers find weaknesses in current strategies for protecting privacy in large datasets. Many anonymized datasets are reidentifiable, and norms for offering data subjects notice and consent over emphasize individual responsibility. Based…