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.
翻译:如果预测模型确定脆弱个人或群体,那么使用该模型可能会成为一个道德问题。但是,我们能否知道这是个什么模式?作为一个领域的机器学习公平性侧重于以机器学习方法公正对待信息处理中的个人和群体。虽然已经相当重视减轻对受保护群体的歧视,但弱势群体没有得到同样的关注。与可以被视为总是脆弱的受保护群体不同,弱势群体在一种情况下可能是脆弱的,但在另一种情况下并非如此。这在如何和何时保护接受机器学习的脆弱个人和群体方面提出了新的挑战。相反,机器学习中如何和何时保护弱势个人和群体?用可解释的人工智能的方法(XAI)确实考虑到更多的背景问题,并关注回答“为什么作出这一决定?”的问题。无论是现有的公平性还是现有的解释性方法,都无法让我们确定预测模型是否确定了脆弱性。我们讨论这一问题,并提出分析这方面预测模型的方法。