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.

人工智能在社交媒体中的使用对年轻人的自我表达、人际关系和身体健康等问题提供了帮助，但在涉及敏感话题如自杀时，年轻人更青睐于人类回应。该研究还探讨了在线同伴支持交流的培训作用以及对支持年轻人福祉的意义。

人工智能在青少年互助中的作用：对人工智能生成和人工生成应答的偏好研究

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

We conducted an in-person ethnography in India and the US to investigate how
young people (18-24) trusted online content, with a focus on generative AI
(GenAI). We had four key findings about how young people use GenAI and
determine what to trust online. First, when online, we found participants
fluidly shifted between mindsets and emotional states, which we term
"information modes." Second, these information modes shaped how and why
participants trust GenAI and how they applied literacy skills. In the modes
where they spent most of their time, they eschewed literacy skills. Third, with
the advent of GenAI, participants imported existing trust heuristics from
familiar online contexts into their interactions with GenAI. Fourth, although
study participants had reservations about GenAI, they saw it as a requisite
tool to adopt to keep up with the times. Participants valued efficiency above
all else, and used GenAI to further their goals quickly at the expense of
accuracy. Our findings suggest that young people spend the majority of their
time online not concerned with truth because they are seeking only to pass the
time. As a result, literacy interventions should be designed to intervene at
the right time, to match users' distinct information modes, and to work with
their existing fact-checking practices.

年轻人在线上如何信任内容，尤其是生成式人工智能（GenAI）的使用以及信任形成的关键发现。

新背景，旧启示：生成式人工智能时代中印度和美国年轻人对在线内容的信任

New contexts, old heuristics: How young people in India and the US trust  online content in the age of generative AI

Local authorities in England, such as Leicestershire County Council (LCC),
provide Early Help services that can be offered at any point in a young
person's life when they experience difficulties that cannot be supported by
universal services alone, such as schools. This paper investigates the
utilisation of machine learning (ML) to assist experts in identifying families
that may need to be referred for Early Help assessment and support. LCC
provided an anonymised dataset comprising 14360 records of young people under
the age of 18. The dataset was pre-processed, machine learning models were
build, and experiments were conducted to validate and test the performance of
the models. Bias mitigation techniques were applied to improve the fairness of
these models. During testing, while the models demonstrated the capability to
identify young people requiring intervention or early help, they also produced
a significant number of false positives, especially when constructed with
imbalanced data, incorrectly identifying individuals who most likely did not
need an Early Help referral. This paper empirically explores the suitability of
data-driven ML models for identifying young people who may require Early Help
services and discusses their appropriateness and limitations for this task.

本文利用机器学习技术对提高英格兰的早期干预服务的家庭辨识能力进行了实证研究，虽然该模型可以确定需要干预和早期干预的年轻人，但也产生了大量错误的预测，特别是在不平衡数据构建下。