交谈式人工智能在纠正思想理论和自治偏见方面的有效性:比较分析
Marcin Rządeczka1,2, Anna Sterna3, Julia Stolińska1
1IDEAS NCBR, Warsaw, Poland.
JMIR mental health
|February 7, 2025
概括
一般用途的人工智能模型在识别和纠正心理健康相互作用中的认知偏见方面表现优于治疗聊天机器人. 这些先进的模型在识别用户影响和偏见方面表现出更大的适应性和准确性.
科学领域:
- 人与计算机的交互
- 人工智能在心理健康中的作用
- 认知科学 认知科学
背景情况:
- 交谈式人工智能越来越多地用于心理健康干预,需要评估其在解决认知偏见和影响识别方面的有效性.
- 认知偏见可以通过加强人类-人工智能交互中的不适应性思维模式,使抑郁和焦虑等心理健康状况恶化.
研究的目的:
- 将治疗聊天机器人 (Wysa,Youper) 与通用语言模型 (GPT-3.5,GPT-4,Gemini Pro) 的有效性进行比较.
- 评估他们识别和纠正认知偏见以及识别用户交互中的影响的能力.
主要方法:
- 利用模拟用户-机器人交互的构建案例场景来评估偏差纠正.
- 评估了特定的认知偏见:思想理论 (人类形态,过度信任,归因) 和自主性 (控制错觉,基本归因错误,正义世界假设).
- 认知科学家和临床心理学家对答案的准确性,治疗质量和遵守认知行为治疗原则进行了评分.
主要成果:
- 一般用途模型在纠正认知偏见方面表现优于治疗聊天机器人,特别是过度信任,基本归因错误和公正世界假设.
- GPT-4获得最高分,而Wysa获得最低分;通用机器人表现出卓越的准确性和适应性.
- 一般用途的聊天机器人还在情感识别方面表现出色,更快地适应情感细微差别,在67%的测试偏差中表现优于治疗机器人.
结论:
- 目前的治疗聊天机器人在解决认知偏见和有效模拟同情心方面存在局限性.
- 增强的人工智能系统需要分析和纠正偏见,作为人类认知不可或缺的一部分,以获得精确性和模拟的同情心.
- 未来的研究必须改进模拟情绪智能,解决伦理问题,并确保安全,有效的AI心理健康支持.
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