Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance. In practice, this may lead to sub-optimal or discriminatory decisions against minority cases. In this work we model such changes in depth and breadth of knowledge as a partitioning of the problem space into regions of differing expertise. We provide here new algorithms that explicitly consider and adapt to the relationship between problem instances and experts' knowledge. We first propose and highlight the drawbacks of a naive approach based on nearest neighbor queries. To address these drawbacks we then introduce a novel algorithm - expertise trees - that constructs decision trees enabling the learner to select appropriate models. We provide theoretical insights and empirically validate the improved performance of our novel approach on a range of problems for which existing methods proved to be inadequate.

翻译：向决策者提供建议的专家往往会展现出随问题实例变化而不同的专业知识。实践中，这可能导致针对少数案例的次优决策或歧视性决策。本研究将知识深度与广度的这种变化建模为问题空间划分为不同专业领域的区域。我们在此提出新算法，明确考虑并适应问题实例与专家知识之间的关系。首先提出并强调基于最近邻查询的朴素方法的缺陷。为克服这些缺陷，我们随后引入一种新算法——专业知识树——通过构建决策树使学习器能够选择恰当模型。我们提供理论洞见，并在现有方法证明无效的一系列问题上通过实验验证了本新方法性能的改进。