Despite its technological breakthroughs, eXplainable Artificial Intelligence
(XAI) research has limited success in producing the {\em effective
explanations} needed by users. In order to improve XAI systems' usability,
practical interpretability, and efficacy for real users, the emerging area of
{\em Explainable Interfaces} (EIs) focuses on the user interface and user
experience design aspects of XAI. This paper presents a systematic survey of 53
publications to identify current trends in human-XAI interaction and promising
directions for EI design and development. This is among the first systematic
survey of EI research.

该论文通过对 53 篇出版物的系统调查，介绍了可解释人工智能（XAI）和可解释界面（EI）研究的最新趋势，旨在提高 XAI 系统的可用性、实用性和用户体验设计方面的效果，并为 EI 设计与开发指明了有前途的方向。

人机交互中，可解释人工智能界面的设计与评估：系统调查

How Human-Centered Explainable AI Interface Are Designed and Evaluated:  A Systematic Survey

While recent years have witnessed a rapid growth of research papers on
recommender system (RS), most of the papers focus on inventing machine learning
models to better fit user behavior data. However, user behavior data is
observational rather than experimental. This makes various biases widely exist
in the data, including but not limited to selection bias, position bias,
exposure bias, and popularity bias. Blindly fitting the data without
considering the inherent biases will result in many serious issues, e.g., the
discrepancy between offline evaluation and online metrics, hurting user
satisfaction and trust on the recommendation service, etc. To transform the
large volume of research models into practical improvements, it is highly
urgent to explore the impacts of the biases and perform debiasing when
necessary. When reviewing the papers that consider biases in RS, we find that,
to our surprise, the studies are rather fragmented and lack a systematic
organization. The terminology ``bias'' is widely used in the literature, but
its definition is usually vague and even inconsistent across papers. This
motivates us to provide a systematic survey of existing work on RS biases. In
this paper, we first summarize seven types of biases in recommendation, along
with their definitions and characteristics. We then provide a taxonomy to
position and organize the existing work on recommendation debiasing. Finally,
we identify some open challenges and envision some future directions, with the
hope of inspiring more research work on this important yet less investigated
topic. The summary of debiasing methods reviewed in this survey can be found at
https://github.com/jiawei-chen/RecDebiasing.

本文系统概要总结研究论文中七种推荐偏差类型及其特点、定义，提供分类学和组织现有关于推荐去偏差的工作，并确定一些开放式挑战和未来方向。