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Annika Marie Schoene, a research scientist at our Institute, will present her virtual Expeditions in Experiential AI Seminar "Responsible AI for Suicide Prevention" on Wednesday, June 12, 2024.



Suicide remains one of the leading causes of death for people aged under 34 worldwide. While the numbers have started to decline in some countries, they continue to rise in the USA. Pre-existing mental health conditions are often cited as the main contributing factor to suicide risk. However, recent research has found that non-clinical factors, such as a person’s social, economic, political and physical circumstances (known as social determinants of health) are also significant contributors and are a driving force behind adverse health outcomes and inequalities. At the same time, recent years have seen a rise in the development of AI tools for detecting suicidal ideation and intent for suicide prevention. These technologies are wide ranging, from language models used in chatbot applications for therapy or triaging to clinical models that are used to predict suicide risk.

In this talk, Annika will give an overview of how AI has been used in suicide research in the past. More concretely, she will highlight current approaches to extracting social determinants of health for suicide using NLP. She will also explain how these approaches can be used to better understand non-clinical factors that drive mental health inequalities. Finally, she will discuss ethical concerns and considerations that should be taken into account when using AI for suicide prevention. 



Annika Marie Schoene is a research scientist and member of the Responsible AI Practice at the Institute for Experiential AI at Northeastern University. Annika splits her time between Boston and Charlotte, NC. Prior to joining Northeastern University, she completed her post-doc at the University of Manchester in the National Centre for Text Mining (NaCTeM). There, she worked on natural language processing for mental health and Named Entity Recognition for textual social media data and health science. She has a doctorate in natural language processing from the University of Hull in the United Kingdom. As a graduate student, she worked on accurate classification of emotional sentiment in textual social media data, and detecting suicide notes and suicidal ideation on social media.

  • Kate Bednar
  • Zhuoran Huang

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