Peer-reviewed article by Luke Stark and Anna Lauren Hoffmann.
Published in Journal of Cultural Analytics.
A growing list of high-profile controversies involving the social impacts of artificial intelligence systems (AI), digital data collection and algorithmic analysis have forced difficult conversations around the ethics of data-intensive digital technologies and so-called “big data” research. These incidents are directly relevant to newly coalescing cultures of “data science,” an emergent field which seeks both to interpret and capitalize on the creation, collection, and processing of knowledge through large collections of digital data, often in conjunction with particular techniques like machine learning (ML). The long list of recent public controversies, as Brian Beaton observes, lays bare data science’s extant lack of direction regarding professional ethics or values.