Tag: Fairness
All the articles with the tag "Fairness".
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Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning
This paper introduces the Objective Fairness Index (OFI), a legally grounded metric for evaluating bias in machine learning by comparing marginal benefits across groups, demonstrating its ability to detect algorithmic bias in applications like COMPAS and Folktable's Adult Employment dataset where traditional Disparate Impact fails.
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Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M
本文通过基于提示的方法初步研究了大型语言模型(LLMs)对MovieLens-1M推荐数据集的记忆程度,发现所有测试模型均表现出一定记忆,且记忆程度与推荐性能和模型规模正相关,同时揭示了流行度偏见问题。
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A closer look at how large language models trust humans: patterns and biases
本研究通过模拟实验首次揭示大型语言模型对人类的隐性信任模式,显示其类似于人类受可信度维度影响,但存在模型异质性和人口统计学偏差。
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An Empirical Study of Evaluating Long-form Question Answering
本文实证研究了长形式问题回答的自动评估指标,证明了基于LLM的指标在准确性和稳定性上的优势,同时分析了其偏差和改进策略。