Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Recent progress in artificial intelligence and machine learning has led to the growth of
research in every aspect of life including the health care domain. However, privacy risks and …

We propose a general approach for differentially private synthetic data generation, that
consists of three steps:(1) select a collection of low-dimensional marginals,(2) measure …

Synthetic datasets are gradually emerging as solutions for data sharing. Multiple synthetic
data generators have been introduced in the last decade fueled by advancement in machine …

Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of
healthcare data for secondary purposes. It is considered a safe approach for the sharing of …

This essay joins recent scholarship in arguing that FAccT's fundamental framing of the
potential to achieve the normative conditions for justice through bettering the design of …

Background Synthetic tabular data generation is a potentially valuable technology with great
promise for data augmentation and privacy preservation. However, prior to adoption, an …

The exponential growth of collected, processed, and shared microdata has given rise to
concerns about individuals' privacy. As a result, laws and regulations have emerged to …

In the near future, systems, that use Artificial Intelligence (AI) methods, such as machine
learning, are required to be certified or audited for fairness if used in ethically sensitive fields …

This article discusses the technology of city digital twins (CDTs) and its potential applications
in the policymaking context. The article analyzes the history of the development of the …