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。