Bias and Fairness in Data Marketplaces
In the modern age of digitization, data is everywhere. The need to efficiently sell and purchase data has led to an emergence of platforms that act as intermediaries between data providers and data consumers, but at the same time offer functionality for processing the data offered (e.g., cleansing, anonymizing, aggregating, etc.), typically before it is being sold. These platforms are referred to in the literature as data marketplaces.
A problem that emerges when designing a marketplace for the commercialization of data and data-driven models is the one referring to bias and fairness. That is, using information technology to materialize such a platform without a thorough and principled design can propagate or even aggravate existing prejudicial bias in data exchange (e.g., buyer discrimination) and data-related services (e.g., biased model estimations).
This thesis involves the investigation of bias and fairness issues in the context of data marketplaces. In this sense, the central questions this thesis addresses are: (1) How to define bias and fairness in data marketplaces? (2) How to make data providers and buyers aware of bias-related issues in datasets? (3) How bias and fairness issues impact data pricing schemes?
Moreover, of particular interest in this context is the effect of recent developments, such as crypto-based solutions, blockchains and smart contracts (e.g., IOTA, Datum).