Categorizing Sources of Information for Explanations in Conversational AI Systems for Older Adults Aging in Place

TL;DR

This paper explores how conversational AI systems for older adults can generate effective explanations by categorizing information sources, emphasizing the importance of multi-source explanations tailored to user mental models in home caregiving scenarios.

Contribution

It introduces a framework for categorizing information sources used by AI to generate explanations in multi-user, home-based caregiving environments, guiding future design of transparent AI systems.

Findings

Identifies multiple information sources for AI explanations in home settings

Highlights the importance of aligning explanations with user mental models

Proposes a framework for categorizing explanation sources

Abstract

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.