Stability in social, technical, and financial systems, as well as the capacity of organizations to work across borders, requires consistency in public policy across jurisdictions. The diffusion of laws and regulations across political boundaries can reduce the tension that arises between innovation and consistency. Policy diffusion has been a topic of focus across the social sciences for several decades

Previous work has shown that some user cognitive abilities relevant for processing information visualizations can be predicted from eye-tracking data. Performing this type of user modeling is important for devising visualizations that can detect a user's abilities and adapt accordingly during the interaction. In this article, we extend previous user modeling work by investigating for the first time

Our research aims to develop interactive, social agents that can coach people to learn new tasks, skills, and habits. In this article, we focus on coaching sedentary, overweight individuals (i.e., “trainees”) to exercise regularly. We employ adaptive goal setting in which the intelligent health coach generates, tracks, and revises personalized exercise goals for a trainee. The goals become incrementally

How should reinforcement learning (RL) agents explain themselves to humans not trained in AI? To gain insights into this question, we conducted a 124-participant, four-treatment experiment to compare participants’ mental models of an RL agent in the context of a simple Real-Time Strategy (RTS) game. The four treatments isolated two types of explanations vs. neither vs. both together. The two types

Think-aloud protocols are a highly valued usability testing method for identifying usability problems. Despite the value of conducting think-aloud usability test sessions, analyzing think-aloud sessions is often time-consuming and labor-intensive. Consequently, previous research has urged the community to develop techniques to support fast-paced analysis. In this work, we took the first step to design

This paper presents an online algorithms for personal preference modeling to estimate context-dependent user selection by using combination of a data-driven approach and sequential user feedback. The proposed approach is based on an algorithm of online prediction with experts in which our online learner uses a set of policies generated by supervised learning with collected other users' data and adapts

Understanding why automatic recommendation systems make decisions is an important area of research because users' satisfaction improves when they understand the reasoning behind the suggestions. In the area of visual art recommendation, explanation is a critical part of the process of selling artworks. Traditionally, artwork has been sold in galleries where people can see different physical items,

Assessing and understanding intelligent agents is a difficult task for users who lack an AI background. ?Explainable AI? (XAI) aims to address this problem, but what should be in an explanation? One route toward answering this question is to turn to theories of how humans try to obtain information they seek. Information Foraging Theory (IFT) is one such theory. In this paper, we present a series of

This paper proposes a novel study on personality recognition in different scenarios. Our goal is to jointly model nonverbal behavioral cues with contextual information for a robust, multi-scenario, personality recognition system. Therefore, we build a novel multi-stream Convolutional Neural network framework (CNN), which considers multiple sources of information. From a given scenario, we extract spatio-temporal

The complex nature of intelligent systems motivates work on supporting users during interaction, for example through explanations. However, there is yet little empirical evidence on specific problems users face in such systems in everyday use. This paper contributes a novel method and analyses to investigate such problems as reported by users: We analysed 45,448 reviews of four apps on the Google Play

It is essential that users understand how algorithmic decisions are made, as we increasingly delegate important decisions to intelligent systems. Prior work has often taken a techno-centric approach, focusing on new computational techniques to support transparency. In contrast, this paper employs empirical methods to better understand user reactions to transparent systems, in order to motivate user-centric

Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools. Taking this into account, we developed Smell Pittsburgh, a system that enables community members to report odors and track where these odors are frequently concentrated

Personality detection is an important task in psychology, as different personality traits are linked to different behaviours and real-life outcomes. Traditionally it involves filling out lengthy questionnaires which is time consuming, and may also be unreliable if respondents do not fully understand the questions or are not willing to honestly answer them. In this paper, we propose a framework for

Word clouds are widely used for non-analytic purposes, such as introducing a topic to students, or creating a gift with personally meaningful text. Surveys show that users prefer tools that yield word clouds with a stronger emotional impact. Fonts and color palettes are powerful typographical signals that may determine this impact. Typically, these signals are assigned randomly, or expected to chosen

The advent of mobile health (mHealth) technologies challenges the capabilities of current visualizations, interactive tools, and algorithms. We present Chronodes, an interactive system that unifies data mining and human-centric visualization techniques to support explorative analysis of longitudinal mHealth data. Chronodes extracts and visualizes frequent event sequences that reveal chronological patterns

We focus on a fundamental looking behavior in human-robot interactions - gazing at each other's face. Eye contact and mutual gaze between two social partners are critical in smooth human-human interactions. Therefore, investigating at what moments and in what ways a robot should look at a human user's face as a response to the human's gaze behavior is an important topic. Toward this goal, we developed