Conceptualizing Algorithmic Stigmatization

Andalibi, Nazanin; Pyle, Cassidy; Barta, Kristen; Xian, Lu; Jacobs, Abigail Z.; Ackerman, Mark S.

doi:10.1145/3544548.3580970

Cited by 6 publications

(2 citation statements)

References 150 publications

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“…AI conversational agents could be helpful in identifying users at risk of suicide and self-harm, but it is imperative that we not rely on AI alone to detect imminent risks. Furthermore, there are open questions about monitoring stigmatized topics like suicide, as it can perpetuate algorithmic stigmatization [5] for individuals struggling with mental health issues and create an environment where they feel constantly surveilled. Researchers should investigate the social implications of deploying AI in sensitive contexts such as mental health support based on four algorithmic stigma elements (i.e., labeling, stereotyping, separation, status loss/discrimination) [5] which include representational and/or allocative harms from the perspective of youth.…”

Section: Ai As Complimentary Response Based On the Context And Sensit...mentioning

confidence: 99%

“…The notion of education as a solution to the lack of expertise of support givers was validated by a later study where researchers [81] developed an AI system called 'Hailey' which helped develop more empathetic responses to support seekers in a mental health peer support platform. AI does have many limitations, such as its inability to understand human nuances [13], ethical risks of misclassification for sensitive topics [71], and potential algorithmic stigmatization and harms [5]. This has led to continued efforts to explore the potential of AI as a supplementary, rather than a primary, source of support for youth.…”

Section: Introductionmentioning

confidence: 99%

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The Role of AI in Peer Support for Young People: A Study of Preferences for Human- and AI-Generated Responses

Young,

Jawara,

Nguyen

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Generative Artificial Intelligence (AI) is integrated into everyday technology, including news, education, and social media. AI has further pervaded private conversations as conversational partners, auto-completion, and response suggestions. As social media becomes young people's main method of peer support exchange, we need to understand when and how AI can facilitate and assist in such exchanges in a beneficial, safe, and socially appropriate way. We asked 622 young people to complete an online survey and evaluate blinded human-and AI-generated responses to help-seeking messages. We found that participants preferred the AI-generated response to situations about relationships, self-expression, and physical health. However, when addressing a sensitive topic, like suicidal thoughts, young people preferred the human response. We also discuss the role of training in online peer support exchange and its implications for supporting young people's well-being. Disclaimer: This paper includes sensitive topics, including suicide ideation. Reader discretion is advised. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; Empirical studies in collaborative and social computing; Natural language interfaces; Social media; • Social and professional topics → Adolescents.

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Section: Ai As Complimentary Response Based On the Context And Sensit...mentioning

confidence: 99%