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

• URL: http://arxiv.org/abs/2405.02711v1
• Date: Sat, 4 May 2024 16:53:19 GMT
• Title: The Role of AI in Peer Support for Young People: A Study of Preferences for Human- and AI-Generated Responses
• Authors: Jordyn Young, Laala M Jawara, Diep N Nguyen, Brian Daly, Jina Huh-Yoo, Afsaneh Razi,
• Abstract summary: 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. 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. We discuss the role of training in online peer support exchange and its implications for supporting young people's well-being.
• Score: 16.35125470386213
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Towards Understanding Emotions for Engaged Mental Health Conversations [1.3654846342364306]
  We are developing a system to perform passive emotion-sensing using a combination of keystroke dynamics and sentiment analysis. The analysis of short text messages and keyboard typing patterns can provide emotion information that may be used to support both clients and responders.
  arXiv  Detail & Related papers  (2024-06-17T01:27:15Z)
• Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
  Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal. We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
  arXiv  Detail & Related papers  (2024-04-17T02:57:42Z)
• Toward Safe Evolution of Artificial Intelligence (AI) based Conversational Agents to Support Adolescent Mental and Sexual Health Knowledge Discovery [0.22530496464901104]
  We discuss the current landscape and opportunities for Conversation Agents (CAs) to support adolescents' mental and sexual health knowledge discovery. We call for a discourse on how to set guardrails for the safe evolution of AI-based CAs for adolescents.
  arXiv  Detail & Related papers  (2024-04-03T19:18:25Z)
• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
  According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2022-07-03T11:33:52Z)
• Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support [10.743204843534512]
  We develop Hailey, an AI-in-the-loop agent that provides just-in-time feedback to help participants who provide support (peer supporters) respond more empathically to those seeking help (support seekers) We show that our Human-AI collaboration approach leads to a 19.60% increase in conversational empathy between peers overall. We find a larger 38.88% increase in empathy within the subsample of peer supporters who self-identify as experiencing difficulty providing support.
  arXiv  Detail & Related papers  (2022-03-28T23:37:08Z)
• AI agents for facilitating social interactions and wellbeing [0.0]
  We provide an overview of the mediative role of AI-augmented agents for social interactions. We discuss opportunities and challenges of the relational approach with wellbeing AI to promote wellbeing in our societies.
  arXiv  Detail & Related papers  (2022-02-26T04:05:23Z)
• Neural Approaches to Conversational Information Retrieval [94.77863916314979]
  A conversational information retrieval (CIR) system is an information retrieval (IR) system with a conversational interface. Recent progress in deep learning has brought tremendous improvements in natural language processing (NLP) and conversational AI. This book surveys recent advances in CIR, focusing on neural approaches that have been developed in the last few years.
  arXiv  Detail & Related papers  (2022-01-13T19:04:59Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus [13.12257465328948]
  We present the Human-Robot Dialogue Learning (HuRDL) Corpus - a novel dialogue corpus collected in an online interactive virtual environment. We describe the corpus data and a corresponding annotation scheme to offer insight into the form and content of questions that humans ask to facilitate learning in a situated environment.
  arXiv  Detail & Related papers  (2021-06-11T16:58:22Z)
• The Short Anthropological Guide to the Study of Ethical AI [91.3755431537592]
  Short guide serves as both an introduction to AI ethics and social science and anthropological perspectives on the development of AI. Aims to provide those unfamiliar with the field with an insight into the societal impact of AI systems and how, in turn, these systems can lead us to rethink how our world operates.
  arXiv  Detail & Related papers  (2020-10-07T12:25:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.