The application of Artificial Intelligence (AI) across a wide range of domains comes with both
high expectations of its benefits and dire predictions of misuse. While AI systems have …

Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks
ranging from online comment toxicity to misinformation detection to medical diagnosis …

What does it mean for a generative AI model to be explainable? The emergent discipline of
explainable AI (XAI) has made great strides in hel** people understand discriminative …

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 …

Abstract Most current Artificial Intelligence applications are based on supervised Machine
Learning (ML), which ultimately grounds on data annotated by small teams of experts or …

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 …

AI tools are increasingly deployed in community contexts. However, datasets used to
evaluate AI are typically created by developers and annotators outside a given community …

Data is fundamental to AI/ML models. This paper investigates the work practices concerning
data annotation as performed in the industry, in India. Previous human-centred …

As Artificial Intelligence (AI) continues to advance rapidly, it becomes increasingly important
to consider AI's ethical and societal implications. In this paper, we present a bottom-up …

While artificial intelligence (AI) holds promise for supporting healthcare providers and
improving the accuracy of medical diagnoses, a lack of transparency in the composition of …