Limiting Data Exposure in MultiLabel Classification Processes Administrative services such social care, tax reduction, and many others using complex decision processes, request individuals to provide large amounts of private data items, in order to calibrate their proposal to the specific situation of the applicant. This data is subsequently processed and stored by the organization. However, all the requested information is not needed to reach the same decision. We have recently proposed an approach, termed Minimum Exposure, to reduce the quantity of information provided by the users, in order to protect her privacy, reduce processing costs for the organization, and financial lost in the case of a data breach. In this paper, we address the case of decision making processes based on sets of classifiers, typically multilabel classifiers. We propose a practical implementation using state of the art multilabel classifiers, and analyze the effectiveness of our solution on several real multilabel data sets.

Towards Explainable MultiLabel Classification Multilabel classification is a very active research area and many realworld applications need efficient multilabel learning. During recent years, explaining machine learning predictions is also a very hot topic. A lot of approaches have been proposed for explaining multiclass classifier predictions. However, almost nothing has been proposed for multilabel and ensemble approaches. This paper brings two main contributions. It first proposes a natural framework consisting in reasoning with base classifier explanations in order to provide explanations for the multilabel predictions. The second contribution focuses on binary relevance, a widely used approach in multilabel classification, and distinguishes two kinds of explanations: common explanations shared by all base classifiers predicting positive labels and joint explanations combining explanations from each base classifier predicting a positive label. The paper proposes an efficient approach for deriving such explanations. Experimental studies show positive results that can be achieved on many multilabel datasets.