As the permeability of AI systems in interpersonal domains like the home expands, their technical capabilities of generating explanations are required to be aligned with user expectations for transparency and reasoning. This paper presents insights from our ongoing work in understanding the effectiveness of explanations in Conversational AI systems for older adults aging in place and their family caregivers. We argue that in collaborative and multi-user environments like the home, AI systems will make recommendations based on a host of information sources to generate explanations. These sources may be more or less salient based on user mental models of the system and the specific task. We highlight the need for cross technological collaboration between AI systems and other available sources of information in the home to generate multiple explanations for a single user query. Through example scenarios in a caregiving home setting, this paper provides an initial framework for categorizing these sources and informing a potential design space for AI explanations surrounding everyday tasks in the home.

翻译：随着人工智能系统在家庭等人际交往领域的渗透日益深入，其生成解释的技术能力需要与用户对透明度和推理过程的期望保持一致。本文展示了我们在理解面向居家养老老年人及其家庭照护者的对话式人工智能系统中解释有效性方面的持续研究成果。我们认为，在家庭这类协作式多用户环境中，人工智能系统将基于多种信息来源生成解释性建议。这些信息源的重要性会因用户对系统的心理模型及具体任务性质而产生差异。我们强调，人工智能系统需要与家庭中其他可用信息源进行跨技术协作，从而为单一用户查询生成多重解释。通过照护家庭场景的示例分析，本文提出了对这些信息源进行分类的初步框架，并为围绕家庭日常任务的人工智能解释系统构建了潜在的设计空间。