A fundamental paradigm shift for in-home sensing is apparent. Special-purpose sensing, where there is a one-to-one relationship between sensors and applications, is evolving into general-purpose sensing, where there is a many-to-many relationship between sensors and applications. This new shift may impact how individuals think about in-home sensing, where utility and comfort are often linked to applications rather than sensed data. We explore the evolution of individuals' perceptions as they become increasingly and contextually aware of sensor capabilities and data characteristics. Through a multi-phase study where 12 participants were progressively led through six exposure conditions across laboratory and home environments, we find that exposure changes represent inflection points for perceptions of utility and comfort with data collection. These changes define opportunities for increasing trust in sensing infrastructures via data- and context-aware interventions, managing over-reliance on awareness notifications, and providing data-enabled "what if" analyses to balance comfort and utility within an individual's unique context and environment.

Explanations of AI Agents' actions are considered to be an important factor in improving users' trust in the decisions made by autonomous AI systems. However, as these autonomous systems evolve from reactive, i.e., acting on user input, to proactive, i.e., acting without requiring user intervention, there is a need to explore how the explanation for the actions of these agents should evolve. In this work, we explore the design of explanations through participatory design methods for a proactive auto-response messaging agent that can reduce perceived obligations and social pressure to respond quickly to incoming messages by providing unavailability-related context. We recruited 14 participants who worked in pairs during collaborative design sessions where they reasoned about the agent's design and actions. We qualitatively analyzed the data collected through these sessions and found that participants' reasoning about agent actions led them to speculate heavily on its design. These speculations significantly influenced participants' desire for explanations and the controls they sought to inform the agents' behavior. Our findings indicate a need to transform users' speculations into accurate mental models of agent design. Further, since the agent acts as a mediator in human-human communication, it is also necessary to account for social norms in its explanation design. Finally, user expertise in understanding their habits and behaviors allows the agent to learn from the user their preferences when justifying its actions.

The ease of smartphone communications has created an expectation of constant connectivity. While the adoption of virtual assistants has improved, their capabilities for handling proactive communication tasks remain underexplored. We present the design, implementation, and evaluation of a Contextual Auto-Response agent to communicate users’ situational awareness. The agent creates auto-responses by modeling availability using smartphone sensors and sharing contextual information on behalf of the user. In a two-week study with 12 participants, we evaluated the perception of this agent and its impact on device usage behavior. Many participants found the agent useful for signaling unavailability, with some caveats. Participants also reported altering device and agent usage based on their understanding of its functions. Our findings indicate the importance of transparency in proactive agent designs and the need for personalization to enable an enhanced and cooperative human-agent interaction.

Identifying instances when a user will not able to attend to an incoming message and constructing an auto-response with relevant contextual information may help reduce social pressures to immediately respond that many users face. Mobile messaging behavior often varies from one person to another. As a result, compared to a generic model considering profiles of several users, a personalized model can capture a user's messaging behavior more accurately to predict their inattentive states. However, creating accurate personalized models requires a non-trivial amount of individual data, which is often not available for new users. In this work, we investigate a weighted hybrid approach to model users' attention to messaging. Through dynamic performance-based weighting, we combine the predictions of three types of models, a general model, a group model and a personalized model to create an approach which can work through the lack of initial data while adapting to the user's behavior. We present the details of our modeling approach and the evaluation of the model with over three weeks of data from 274 users. Our results highlight the value of hybrid weighted modeling to predict when a user cannot attend to their messages.