Interactive Artificial Intelligence (AI) agents are becoming increasingly
prevalent in society. However, application of such systems without
understanding them can be problematic. Black-box AI systems can lead to
liability and accountability issues when they produce an incorrect decision.
Explainable AI (XAI) seeks to bridge the knowledge gap, between developers and
end-users, by offering insights into how an AI algorithm functions. Many modern
algorithms focus on making the AI model "transparent", i.e. unveil the inherent
functionality of the agent in a simpler format. However, these approaches do
not cater to end-users of these systems, as users may not possess the requisite
knowledge to understand these explanations in a reasonable amount of time.
Therefore, to be able to develop suitable XAI methods, we need to understand
the factors which influence subjective perception and objective usability. In
this paper, we present a novel user-study which studies four differing XAI
modalities commonly employed in prior work for explaining AI behavior, i.e.
Decision Trees, Text, Programs. We study these XAI modalities in the context of
explaining the actions of a self-driving car on a highway, as driving is an
easily understandable real-world task and self-driving cars is a keen area of
interest within the AI community. Our findings highlight internal consistency
issues wherein participants perceived language explanations to be significantly
more usable, however participants were better able to objectively understand
the decision making process of the car through a decision tree explanation. Our
work also provides further evidence of importance of integrating user-specific
and situational criteria into the design of XAI systems. Our findings show that
factors such as computer science experience, and watching the car succeed or
fail can impact the perception and usefulness of the explanation.

本论文以自动驾驶汽车为研究对象，研究了四种常见的可解释人工智能方法，即决策树、文本、程序和图示。研究表明，参与者倾向于使用语言解释，但通过决策树解释更能客观地理解汽车的决策过程，并且结果显示，计算机科学经验和观察汽车成功或失败的情况可以影响解释的感知和有用性，因此本研究提出，设计 XAI 系统必须考虑用户的特定需求和情境因素。

关于调和可用性和实用性的可解释人工智能方法

Towards Reconciling Usability and Usefulness of Explainable AI Methodologies

We present an approach for identifying picturesque highlights from large
amounts of egocentric video data. Given a set of egocentric videos captured
over the course of a vacation, our method analyzes the videos and looks for
images that have good picturesque and artistic properties. We introduce novel
techniques to automatically determine aesthetic features such as composition,
symmetry and color vibrancy in egocentric videos and rank the video frames
based on their photographic qualities to generate highlights. Our approach also
uses contextual information such as GPS, when available, to assess the relative
importance of each geographic location where the vacation videos were shot.
Furthermore, we specifically leverage the properties of egocentric videos to
improve our highlight detection. We demonstrate results on a new egocentric
vacation dataset which includes 26.5 hours of videos taken over a 14 day
vacation that spans many famous tourist destinations and also provide results
from a user-study to access our results.

本论文提出一种识别大量自我中心视频数据中风景亮点的方法，通过自动确定美学特征如构图、对称性和颜色活力等，以及利用 GPS 等上下文信息来提高亮点检测的准确性，并通过用户研究验证了其有效性。