Joan Giner-Miguelez

• Doctor of Engineering
• Research at Barcelona Supercomputing Center

Recent public regulatory initiatives and relevant voices in the ML community have identified the need to document datasets according to several dimensions to ensure the fairness and trustworthiness of machine learning systems. In this sense, the data-sharing practices in the scientific field have been quickly evolving in the last years, with more a...

The interest and concerns about diversity in software development have soared in recent years. Reporting diversity-related aspects of software projects can increase user trust and help regulators evaluate potential adoption. Furthermore, recent directives around AI are beginning to require diversity information in the development of AI products, in...

To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides, data-sharing practices in many scientific domains have evolved in recent years for reproducibility purposes. In this se...

Data is critical to advancing AI technologies, yet its quality and documentation remain significant challenges, leading to adverse downstream effects (e.g., potential biases) in AI applications. This paper addresses these issues by introducing Croissant-RAI, a machine-readable metadata format designed to enhance the discoverability, interoperabilit...

To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides, data-sharing practices in many scientific domains have evolved in recent years for reproducibility purposes. In this se...

Datasets play a central role in the training and evaluation of machine learning (ML) models. But they are also the root cause of many undesired model behaviors, such as biased predictions. To overcome this situation, the ML community is proposing a data-centric cultural shift, where data issues are given the attention they deserve, for instance, pr...

Datasets play a central role in the training and evaluation of machine learning (ML) models. But they are also the root cause of many undesired model behaviors, such as biased predictions. To overcome this situation, the ML community is proposing a data-centric cultural shift where data issues are given the attention they deserve, and more standard...

Content Management Systems (CMSs) are the most popular tool when it comes to create and publish content across the web. Recently, CMSs have evolved, becoming headless. Content served by a headless CMS aims to be consumed by other applications and services through REST APIs rather than by human users through a web browser. This evolution has enable...