With the recent advances in the field of artificial intelligence, an
increasing number of decision-making tasks are delegated to software systems. A
key requirement for the success and adoption of such systems is that users must
trust system choices or even fully automated decisions. To achieve this,
explanation facilities have been widely investigated as a means of establishing
trust in these systems since the early years of expert systems. With today's
increasingly sophisticated machine learning algorithms, new challenges in the
context of explanations, accountability, and trust towards such systems
constantly arise. In this work, we systematically review the literature on
explanations in advice-giving systems. This is a family of systems that
includes recommender systems, which is one of the most successful classes of
advice-giving software in practice. We investigate the purposes of explanations
as well as how they are generated, presented to users, and evaluated. As a
result, we derive a novel comprehensive taxonomy of aspects to be considered
when designing explanation facilities for current and future decision support
systems. The taxonomy includes a variety of different facets, such as
explanation objective, responsiveness, content and presentation. Moreover, we
identified several challenges that remain unaddressed so far, for example
related to fine-grained issues associated with the presentation of explanations
and how explanation facilities are evaluated.

本文系统审查了建议系统中的解释问题，探究了其目的、生成方式、呈现方式和评估方式，并提出了一个全面的分类法，其中包括解释目标、响应性、内容和呈现等方面，同时还识别了许多未解决的挑战。

决策支持和推荐系统中解释的系统审查和分类

A systematic review and taxonomy of explanations in decision support and  recommender systems

In order to meet usability requirements, most logic-based applications
provide explanation facilities for reasoning services. This holds also for
Description Logics, where research has focused on the explanation of both TBox
reasoning and, more recently, query answering. Besides explaining the presence
of a tuple in a query answer, it is important to explain also why a given tuple
is missing. We address the latter problem for instance and conjunctive query
answering over DL-Lite ontologies by adopting abductive reasoning; that is, we
look for additions to the ABox that force a given tuple to be in the result. As
reasoning tasks we consider existence and recognition of an explanation, and
relevance and necessity of a given assertion for an explanation. We
characterize the computational complexity of these problems for arbitrary,
subset minimal, and cardinality minimal explanations.

通过采用外推推理，我们研究了 DL-Lite 本体论上的逻辑、存在性和充分性问题，以解决联合查询应答中的缺失的单个事实的解释问题。