While recent advances in deep learning have demonstrated its transformative
potential, its adoption for real-world manufacturing applications remains
limited. We present an Explanation User Interface (XUI) for a state-of-the-art
deep learning-based robot program optimizer which provides both naive and
expert users with different user experiences depending on their skill level, as
well as Explainable AI (XAI) features to facilitate the application of deep
learning methods in real-world applications. To evaluate the impact of the XUI
on task performance, user satisfaction and cognitive load, we present the
results of a preliminary user survey and propose a study design for a
large-scale follow-up study.

我们提出了一种用于最新深度学习为基础的机器人程序优化器的解释用户界面（XUI），它提供了不同的用户体验以应对用户的不同技能水平，同时引入了可解释的人工智能（XAI）功能以促进深度学习方法在实际应用中的应用。为了评估 XUI 对任务性能、用户满意度和认知负荷的影响，我们提出了初步用户调查的结果，并提出了一个大规模后续研究的研究设计。

工业机器人中的人机交互：可解释 AI 基于机器人程序优化的用户界面设计与实证评估

Human-AI Interaction in Industrial Robotics: Design and Empirical  Evaluation of a User Interface for Explainable AI-Based Robot Program  Optimization

Knowledge bases are widely used for information management on the web,
enabling high-impact applications such as web search, question answering, and
natural language processing. They also serve as the backbone for automatic
decision systems, e.g. for medical diagnostics and credit scoring. As
stakeholders affected by these decisions would like to understand their
situation and verify fair decisions, a number of explanation approaches have
been proposed using concepts in description logics. However, the learned
concepts can become long and difficult to fathom for non-experts, even when
verbalized. Moreover, long concepts do not immediately provide a clear path of
action to change one's situation. Counterfactuals answering the question "How
must feature values be changed to obtain a different classification?" have been
proposed as short, human-friendly explanations for tabular data. In this paper,
we transfer the notion of counterfactuals to description logics and propose the
first algorithm for generating counterfactual explanations in the description
logic $\mathcal{ELH}$. Counterfactual candidates are generated from concepts
and the candidates with fewest feature changes are selected as counterfactuals.
In case of multiple counterfactuals, we rank them according to the likeliness
of their feature combinations. For evaluation, we conduct a user survey to
investigate which of the generated counterfactual candidates are preferred for
explanation by participants. In a second study, we explore possible use cases
for counterfactual explanations.

本研究提出首个在描述逻辑中生成反事实解释的算法，通过调整特征值来生成可被人类理解、人性化的解释，在用户调查和案例研究中证明其可行性。

ELH 概念的反事实解释

Counterfactual Explanations for Concepts in $\mathcal{ELH}$

Many users turn to document retrieval systems (e.g. search engines) to seek
answers to controversial questions. Answering such user queries usually require
identifying responses within web documents, and aggregating the responses based
on their different perspectives.
Classical document retrieval systems fall short at delivering a set of direct
and diverse responses to the users. Naturally, identifying such responses
within a document is a natural language understanding task. In this paper, we
examine the challenges of synthesizing such language understanding objectives
with document retrieval, and study a new perspective-oriented document
retrieval paradigm. We discuss and assess the inherent natural language
understanding challenges in order to achieve the goal. Following the design
challenges and principles, we demonstrate and evaluate a practical prototype
pipeline system. We use the prototype system to conduct a user survey in order
to assess the utility of our paradigm, as well as understanding the user
information needs for controversial queries.

本篇论文探讨了如何将自然语言理解与文档检索相结合，提出了面向角度的文档检索范式。基于此范式实现了一个原型系统，通过用户调查评估了其实用性以及针对争议性问题的信息需求。

多视角搜索引擎的设计挑战

Design Challenges for a Multi-Perspective Search Engine

Selecting the appropriate visual presentation of the data such that it
preserves the semantics of the underlying data and at the same time provides an
intuitive summary of the data is an important, often the final step of data
analytics. Unfortunately, this is also a step involving significant human
effort starting from selection of groups of columns in the structured results
from analytics stages, to the selection of right visualization by experimenting
with various alternatives. In this paper, we describe our \emph{DataVizard}
system aimed at reducing this overhead by automatically recommending the most
appropriate visual presentation for the structured result. Specifically, we
consider the following two scenarios: first, when one needs to visualize the
results of a structured query such as SQL; and the second, when one has
acquired a data table with an associated short description (e.g., tables from
the Web). Using a corpus of real-world database queries (and their results) and
a number of statistical tables crawled from the Web, we show that DataVizard is
capable of recommending visual presentations with high accuracy. We also
present the results of a user survey that we conducted in order to assess user
views of the suitability of the presented charts vis-a-vis the plain text
captions of the data.

该研究介绍了一种名为 DataVizard 的系统，该系统可以自动推荐最佳的数据可视化方案，以提高结构化查询结果的可视化效率，并通过用户调查验证其准确性。