The potential of artificial intelligence (AI) to reduce health care disparities and inequities is
recognized, but it can also exacerbate these issues if not implemented in an equitable …

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 …

Machine learning models are commonly used to detect toxicity in online conversations.
These models are trained on datasets annotated by human raters. We explore how raters' …

The interpretation of data is fundamental to machine learning. This paper investigates
practices of image data annotation as performed in industrial contexts. We define data …

Human annotations play a crucial role in machine learning (ML) research and development.
However, the ethical considerations around the processes and decisions that go into …

HCI engages with data science through many topics and themes. Researchers have
addressed biased dataset problems, arguing that bad data can cause innocent software to …

Algorithmic systems have infiltrated many aspects of our society, mundane to high-stakes,
and can lead to algorithmic harms known as representational and allocative. In this paper …

Scholars have recently drawn attention to a range of controversial issues posed by the use
of computer vision for automatically generating descriptions of people in images. Despite …

How are gender, class, and race imagined in relation to automation and augmentation in
popular books on the future of work? This paper problematises intersectional inequality …