Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings. With advances in Natural Language Processing strategies, it is now possible to design automated systems to assess suicide risk. However, such systems may generate uncertain predictions, leading to severe consequences. We hence reformulate suicide risk assessment as a selective prioritized prediction problem over the Columbia Suicide Severity Risk Scale (C-SSRS). We propose SASI, a risk-averse and self-aware transformer-based hierarchical attention classifier, augmented to refrain from making uncertain predictions. We show that SASI is able to refrain from 83% of incorrect predictions on real-world Reddit data. Furthermore, we discuss the qualitative, practical, and ethical aspects of SASI for suicide risk assessment as a human-in-the-loop framework.
Preprint version Association for Computational Linguistics 2022 (ACL 2022), 2022.
© The Authors, 2022
Sawhney, R., Neerkaje, A. T., & Gaur, M. (2022). A Risk-averse mechanism for suicidality assessment on social media [Preprint].