With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

In this work, we survey a breadth of literature that has revealed the limitations of
predominant practices for dataset collection and use in the field of machine learning. We …

This paper presents the overview of the AuTexTification shared task as part of the IberLEF
2023 Workshop in Iberian Languages Evaluation Forum, within the framework of the SEPLN …

The recent emergence and adoption of Machine Learning technology, and specifically of
Large Language Models, has drawn attention to the need for systematic and transparent …

Machine learning datasets have elicited concerns about privacy, bias, and unethical
applications, leading to the retraction of prominent datasets such as DukeMTMC, MS-Celeb …

Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

With the growing reliance on artificial intelligence (AI) for many different applications, the
sharing of code, data, and models is important to ensure the replicability and …

Machine learning (ML) approaches have demonstrated promising results in a wide range of
healthcare applications. Data plays a crucial role in developing ML-based healthcare …

Foundation models (FMs), such as Large Language Models (LLMs), have revolutionized
software development by enabling new use cases and business models. We refer to …

Datasets are central to training machine learning (ML) models. The ML community has
recently made significant improvements to data stewardship and documentation practices …