Summary of Faiir: Building Toward a Conversational Ai Agent Assistant For Youth Mental Health Service Provision, by Stephen Obadinma et al.
FAIIR: Building Toward A Conversational AI Agent Assistant for Youth Mental Health Service Provision
by Stephen Obadinma, Alia Lachana, Maia Norman, Jocelyn Rankin, Joanna Yu, Xiaodan Zhu, Darren Mastropaolo, Deval Pandya, Roxana Sultan, Elham Dolatabadi
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents FAIIR (Frontline Assistant: Issue Identification and Recommendation), a tool aimed at reducing the cognitive burden on Crisis Responders (CRs) who engage in conversations for youth mental health support. An ensemble of transformer models, fine-tuned on 780,000 conversations, is used to identify issues and streamline post-conversation tasks. The authors evaluate FAIIR’s performance on retrospective and prospective conversations, achieving an average AUCROC of 94%, F1-score of 64%, and recall score of 81%. They also demonstrate the tool’s robustness during silent testing. CRs’ responses show an overall agreement of 90.9% with FAIIR’s predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a tool to help people who talk to kids in crisis get better at understanding what’s going on and making things happen. The tool uses special computer models that learn from lots of conversations about youth mental health. It helps the people talking to kids identify problems and do tasks after the conversation. The researchers tested the tool and found it worked really well, even when they weren’t watching. People who talked to kids agreed with the tool’s ideas most of the time. |
Keywords
» Artificial intelligence » F1 score » Recall » Transformer
