Data-oriented applications, their users, and even the law require data of high quality.
Research has divided the rather vague notion of data quality into various dimensions, such …

Cancer therapy is on the brink of a significant transformation with the inclusion of patient-
derived organoids (PDOs) in drug development. These three-dimensional cell cultures …

In this paper, we introduce CTFL, a fair, robust, and interpretable framework designed to
estimate clients' contributions to federated learning, aiming to incentivize high-quality data …

The proliferation of machine learning (ML) applications has given rise to a new and popular
data marketplace paradigm. These marketplaces facilitate ML model requesters in obtaining …

Data is the new oil of the 21st century. The growing trend of trading data for greater welfare
has led to the emergence of data markets. A data market is any mechanism whereby the …

Large language models (LLMs) are trained on a vast amount of human-written data, but data
providers often remain uncredited. In response to this issue, data valuation (or data …

Driven by the growing field of data intelligence, data market emerges as a promising
paradigm for data exchange, enabling the full utilization of data. Data pricing is an essential …

The immense success of ML systems relies heavily on large-scale, high-quality data. The
high demand for data has led to many paradigms that involve selling, exchanging, and …

Systemic property dispossession from minority groups has often been carried out in the
name of technological progress. In this paper, we identify evidence that the current paradigm …

Research in Responsible AI has developed a range of principles and practices to ensure
that machine learning systems are used in a manner that is ethical and aligned with human …