Recent discussions at the intersection of journalism, HCI, and human-centered computing ask how technologies can help create reader-oriented news experiences. The current paper takes up this initiative by focusing on immigrant readers, a group who reports significant difficulties engaging with mainstream news yet has received limited attention in prior research. We report findings from our co-design research with eleven immigrant readers living in the United States and seven journalists working in the same region, aiming to enhance the news experience of the former. Data collected from all participants revealed an “unaddressed-or-unaccountable” paradox that challenges value alignment across immigrant readers and journalists. This paradox points to four metaphors regarding how conversational AI agents can be designed to assist news reading. Each metaphor requires conversational AI, journalists, and immigrant readers to coordinate their shared responsibilities in a distinct manner. These findings provide insights into reader-oriented news experiences with AI in the loop.
ACM CHI Conference on Human Factors in Computing Systems