Today, many industrial processes are undergoing digital transformation, which often
requires the integration of well-understood domain models and state-of-the-art machine …

Recent large language models (LLMs) have advanced the quality of open-ended
conversations with chatbots. Although LLM-driven chatbots have the potential to support …

Artificial intelligence (AI) and machine learning (ML) are quickly becoming pervasive in ways
that impact the lives of all humans across the globe. In an effort to make otherwise” black …

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 …

Recently, the AI/ML research community has indicated an urgent need to establish
Responsible AI (RAI) values and practices as part of the AI/ML lifecycle. Several …

Field workers, like farmers and radiologists, play a crucial role in dataset collection for AI
models in low-resource settings. However, we know little about how field workers' expertise …

The recent release of very large language models such as PaLM and GPT-4 has made an
unprecedented impact in the popular media and public consciousness, giving rise to a …

The importance of meeting data quality standards in the context of Machine Learning (ML)
development pipelines is explored in this study. It delves deep into why good data is crucial …

The topic of algorithmic fairness is of increasing importance to the Human–Computer
Interaction research community following accumulating concerns regarding the use and …

The fitness of the systems in which Machine Learning (ML) is used depends greatly on good-
quality data. Specifications on what makes a good-quality dataset have traditionally been …