Data is a crucial component of machine learning. The field is reliant on data to train, validate,
and test models. With increased technical capabilities, machine learning research has …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Machine learning (ML) depends on data to train and verify models. Very often, organizations
outsource processes related to data work (ie, generating and annotating data and …

HCI engages with data science through many topics and themes. Researchers have
addressed biased dataset problems, arguing that bad data can cause innocent software to …

Building systems that are good for society in the face of complex societal effects requires a
dynamic approach. Recent approaches to machine learning (ML) documentation have …

The rise of datathons, also known as data or data science hackathons, has provided a
platform to collaborate, learn, and innovate quickly. Despite their significant potential …

Human-centric computer vision (HCCV) data curation practices often neglect privacy and
bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed …

Democratisation of machine learning (ML) has been an important theme in the research
community for the last several years with notable progress made by the model-building …

Artificial Intelligence (AI), particularly through the advent of large-scale generative AI (GenAI)
models such as Large Language Models (LLMs), has become a transformative element in …

Abstract Nowadays, Artificial Intelligence (AI) is present in all sectors of the economy.
Consequently, both data-the raw material used to build AI systems-and AI have an …