自动情绪分析的潜在陷:酷儿恐惧偏见的例子
Eddie L Ungless1, Björn Ross1, Vaishak Belle1
1The University of Edinburgh, Scotland, UK.
概括
自动化情绪分析工具对边缘化群体,特别是酷儿身份有偏见. 即使是流行的模型也表现出偏见,强调需要仔细选择工具,以确保自然语言处理中的公正结果.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
- 社会语言学 社会语言学
背景情况:
- 自动化情绪分析被广泛用于各种领域的趋势检测.
- 自然语言处理 (NLP) 技术,包括情绪分析,可以无意中延续社会偏见.
- 现有的研究已经确定了关于性别,种族和残疾的情感分析中的偏见.
研究的目的:
- 调查流行情绪分析工具中的偏见,特别是关于酷尔身份的偏见.
- 在情感分析中,通过检查更广泛的边缘化群体来扩大现有研究.
- 为选择情绪分析工具提供指导,以减轻偏见.
主要方法:
- 评估六种流行的情绪分析工具.
- 测试与各种酷儿身份相关的句子的工具.
- 对工具响应进行比较分析,以确定有偏见的输出.
主要成果:
- 在经过测试的工具中发现了对几个边缘化酷儿身份的偏见的证据.
- 两个著名的模型 (谷歌,亚马逊) 显示了偏见,尽管表面上显然是微不足道的努力.
- 在评估的情绪分析工具中,偏见的程度和性质各不相同.
结论:
- 情绪分析工具并不总是公正的,并且可能使边缘化社区处于不利地位.
- 表面的脱皮方法可能不足以消除人工智能模型中的偏差.
- 选择情绪分析工具需要仔细考虑潜在的偏见,以确保可靠和公平的结果.
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