As artificial intelligence (AI) transitions from research to deployment, creating the appropriate
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …

Ranking, recommendation, and retrieval systems are widely used in online platforms and
other societal systems, including e-commerce, media-streaming, admissions, gig platforms …

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 …

Computer vision models have known performance disparities across attributes such as
gender and skin tone. This means during tasks such as classification and detection, model …

Research in machine learning (ML) has argued that models trained on incomplete or biased
datasets can lead to discriminatory outputs. In this commentary, we propose moving the …

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 …

Computer Vision (CV) has achieved remarkable results, outperforming humans in several
tasks. Nonetheless, it may result in significant discrimination if not handled properly. Indeed …

Diversity in datasets is a key component to building responsible AI/ML. Despite this
recognition, we know little about the diversity among the annotators involved in data …

Artificial Intelligence (AI) has seamlessly integrated into numerous scientific domains,
catalysing unparalleled enhancements across a broad spectrum of tasks; however, its …

Data is central to the development and evaluation of machine learning (ML) models.
However, the use of problematic or inappropriate datasets can result in harms when the …