Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Background The reporting of machine learning (ML) prognostic and diagnostic modeling
studies is often inadequate, making it difficult to understand and replicate such studies. To …

Evaluating the performance of machine learning models on diverse and underrepresented
subgroups is essential for ensuring fairness and reliability in real-world applications …

Data quality is crucial for robust machine learning algorithms, with the recent interest in data-
centric AI emphasizing the importance of training data characterization. However, current …

Synthetic data serves as an alternative in training machine learning models, particularly
when real-world data is limited or inaccessible. However, ensuring that synthetic data …

Abstract ydata-profiling is an open-source Python package for advanced exploratory data
analysis that enables users to generate data profiling reports in a simple, fast, and efficient …

Problem statement: Standardisation of AI fairness rules and benchmarks is challenging
because AI fairness and other ethical requirements depend on multiple factors, such as …

Due to huge investments by both the public and private sectors, artificial intelligence (AI) has
made tremendous progress in solving multiple real-world problems such as disease …

Machine Learning (ML) in low-data settings remains an underappreciated yet crucial
problem. This challenge is pronounced in low-to-middle income countries where access to …

Identification and appropriate handling of inconsistencies in data at deployment time is
crucial to reliably use machine learning models. While recent data-centric methods are able …