论文标题
在机器学习模型中识别脆弱组的解释性
Explainability for identification of vulnerable groups in machine learning models
论文作者
论文摘要
如果预测模型确定了脆弱的个人或群体，则该模型的使用可能成为道德问题。但是我们能知道这是一个模型吗？机器学习公平作为一个领域的重点是通过机器学习方法对信息处理的个人和群体的公正处理。尽管已经非常关注缓解受保护群体的歧视，但脆弱的群体并未受到相同的关注。与受保护的群体不同，这些群体可以被视为总是脆弱的，一个脆弱的群体在一种情况下可能是脆弱的，而在另一种情况下却不是脆弱的。这引发了有关如何以及何时保护机器学习中脆弱的个人和群体的新挑战。相比之下，来自可解释的人工智能（XAI）的方法确实考虑了更多的上下文问题，并关注回答“为什么做出这一决定？”的问题。现有的公平性和现有的解释性方法都不允许我们确定预测模型是否确定了漏洞。我们讨论了这个问题，并提出了在这方面分析预测模型的方法。
If a prediction model identifies vulnerable individuals or groups, the use of that model may become an ethical issue. But can we know that this is what a model does? Machine learning fairness as a field is focused on the just treatment of individuals and groups under information processing with machine learning methods. While considerable attention has been given to mitigating discrimination of protected groups, vulnerable groups have not received the same attention. Unlike protected groups, which can be regarded as always vulnerable, a vulnerable group may be vulnerable in one context but not in another. This raises new challenges on how and when to protect vulnerable individuals and groups under machine learning. Methods from explainable artificial intelligence (XAI), in contrast, do consider more contextual issues and are concerned with answering the question "why was this decision made?". Neither existing fairness nor existing explainability methods allow us to ascertain if a prediction model identifies vulnerability. We discuss this problem and propose approaches for analysing prediction models in this respect.
