In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …

It is well understood from literature that the performance of a machine learning (ML) model is
upper bounded by the quality of the data. While researchers and practitioners have focused …

AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …

Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

This article attempts to bridge the gap between widely discussed ethical principles of Human-
centered AI (HCAI) and practical steps for effective governance. Since HCAI systems are …

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …

This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

This paper argues that the data warehouse architecture as we know it today will wither in the
coming years and be replaced by a new architectural pattern, the Lakehouse, which will (i) …

The introduction of machine learning (ML) components in software projects has created the
need for software engineers to collaborate with data scientists and other specialists. While …