人工智能时代的残疾伦理和教育:在聊天GPT和双子座中识别能力偏差
Jacob T Urbina1, Peter D Vu1, Michael V Nguyen1
1Department of Physical Medicine and Rehabilitation, McGovern Medical School, UTHealth Houston, Houston, TX.
Archives of physical medicine and rehabilitation
|August 31, 2024
概括
像ChatGPT和Gemini这样的生成人工智能聊天机器人显示能力偏见,描述残疾人不那么有利. 这凸显了在医疗保健及其他领域需要道德的人工智能开发的需要.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 关于残疾人的研究.
背景情况:
- 生成型人工智能 (AI) 大型语言模型 (LLM) 越来越多地用于各种应用,包括医疗保健.
- 这些人工智能模型中的潜在偏差,特别是关于残疾的偏差,需要进行彻底的调查,以确保公平的结果.
- 了解人工智能的能力偏差对于负责任的AI部署至关重要.
研究的目的:
- 识别和量化领先的生成性AI聊天机器人的能力偏差,特别是OpenAI的ChatGPT和谷歌的Gemini.
- 分析人工智能生成的残疾人和没有残疾人的描述的语言特征.
主要方法:
- 使用仅限于ChatGPT和Gemini的调查浏览器配置文件进行了一项观察性研究.
- 每个聊天机器人生成了300个描述:60个用于没有特定功能状态的人,30个用于残疾人,残疾患者和残疾运动员.
- 生成的文本被解析成单词,并进行语言分析,以寻找有利和限制性质.
主要成果:
- 聊天GPT和双子座都表现出显著的能力偏差,低估了他们生成的描述中的残疾.
- 语言分析显示,与没有残疾的人相比,残疾人的描述始终含有较少的有利品质和更多的限制.
- 这种偏见在对普通人,患者和残疾运动员的描述中很明显.
结论:
- 生成性AI聊天机器人表现出可量化的能力偏见,经常排除或误解残疾人.
- 这些人工智能聊天机器人在医疗系统中的伦理应用必须承认并解决这种固有的局限性.
- 进一步的研究和开发是必要的,以创造更公平和包容的人工智能技术.
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