  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.
