arXiv subjects

Delong Du

Publications and source records attributed to Delong Du.

6 recordsLinked to original sources

arXiv2026

Beyond Companionship: Robotic Pets as Embodied Communication Media for Older Adults

Robotic pets for older adults are typically studied as companions, with the older person positioned as the robot's primary interaction partner. This framing overlooks another role: a petlike robot can mediate relationships between people. We report a formative qualitative interview study with six adults aged 63-77 in Germany. Interviews examined participants' existing communication practices, experiences of being alone, and responses to scenarios in which a robotic pet could carry voice messages, support calls, move through the home, or convey a remote family member's actions. Thematic qualitative content analysis identified four findings. Participants maintained relationships through flexible combinations of telephone, messaging, and video calls; imagined petlike movement, proximity, voice, and touch-like gestures adding embodied presence to those channels; combined expectations of communication, companionship, and practical assistance; and made acceptance conditional on customization, hygiene, ease of use, and human control. Drawing together HCI research on mediated intimacy, CSCW research on awareness and care networks, and HRI research on social and telepresence robots, we contribute (1) an empirical account of how older adults imagine petlike embodiment carrying another person's presence, (2) the concept of robotic pets as embodied communication media, and (3) a four-part sensitizing framework organized around relational purpose, source of agency, mode of embodiment, and governance. The study identifies design hypotheses; it does not test a robot or establish effects on loneliness, depression, or relationship quality.

cs.HC↗

arXiv2026

Public EV Charging Choices: How Users Trade Off Time, Price, and Renewable Energy

The carbon intensity of electric-vehicle (EV) charging varies over time and place, yet EV charging recommender systems and eco-routing interfaces rarely make this variation actionable for drivers. We investigate how renewable-energy information interacts with two attributes that routinely shape public-charging decisions: travel time and price. Fifty car users completed a within-subjects stated-choice study with three navigation-interface variants, and 10 EV drivers participated in semi-structured interviews. Across the three variants, the share choosing the slower option was 38%, 42%, and 66%, respectively. A paired-sample analysis found that choices differed across variants (Cochran's Q(2) = 11.47, p = .003). When the time-price trade-off was held constant, adding a renewable-energy label increased selection of the slower station from 38% to 66% (exact McNemar test, Holm-adjusted p = .004). Interviews nevertheless showed that renewable energy was usually a secondary consideration: participants evaluated it through situational constraints such as urgency, charging cost, traffic, charger availability, and familiarity with locations. We derive a constraint-first design rationale for renewable-energy-aware charging recommendations: filter options using context-sensitive time and cost constraints, disclose the renewable-energy signal and its uncertainty, and preserve user control rather than relying on a green default alone. Together, the results provide an empirical baseline for research on green charging recommendations, while characterizing stated choices in a small exploratory sample rather than real-world charging behavior.

cs.HC↗

arXiv2026

Exploring patient trust in clinical advice from AI-driven LLMs like ChatGPT for self-diagnosis

Trustworthy clinical advice is crucial but can be burdensome to obtain. Limited access and financial costs may lead people to self-diagnose. However, self-diagnosis requires considerable learning and can create risks when people pursue treatment without professional guidance. Large language models (LLMs) such as GPT-4 may offer a convenient yet risky alternative because they can produce inaccurate but convincing information. We therefore ask whether patients can trust clinical advice from AI-driven LLMs. We examined this question through a think-aloud observation in which a patient used GPT-4 for self-diagnosis while a doctor assessed its responses using professional expertise. We then conducted a semi-structured interview with the patient about their trust in the system. Our results show that patients may struggle to identify errors because they lack professional medical knowledge, even when GPT-4 provides advice that a doctor can recognize as false. Patients may develop some trust because GPT-4 explains its responses and acknowledges its limitations, but this trust remains uncertain because its advice can be unreliable. The doctor also reported that checking GPT-4's responses required more effort than making a diagnosis without it. Patients tend to trust doctors because educated and authorized professionals can provide effective guidance. This trust also develops through social connection, certification, institutional accountability, and professional rules. Doctors can adapt their questions, observe patients, and use different methods when patients cannot clearly describe their symptoms. An LLM, however, depends primarily on the information provided in a prompt and may overlook details that a doctor could identify during a clinical consultation. These findings raise questions about competence, responsibility, autonomy, and safety when LLMs are used for clinical advice.

cs.HC↗

arXiv2024

What Matters in Explanations: Towards Explainable Fake Review Detection Focusing on Transformers

Customers' reviews and feedback play crucial role on electronic commerce~(E-commerce) platforms like Amazon, Zalando, and eBay in influencing other customers' purchasing decisions. However, there is a prevailing concern that sellers often post fake or spam reviews to deceive potential customers and manipulate their opinions about a product. Over the past decade, there has been considerable interest in using machine learning (ML) and deep learning (DL) models to identify such fraudulent reviews. Unfortunately, the decisions made by complex ML and DL models - which often function as \emph{black-boxes} - can be surprising and difficult for general users to comprehend. In this paper, we propose an explainable framework for detecting fake reviews with high precision in identifying fraudulent content with explanations and investigate what information matters most for explaining particular decisions by conducting empirical user evaluation. Initially, we develop fake review detection models using DL and transformer models including XLNet and DistilBERT. We then introduce layer-wise relevance propagation (LRP) technique for generating explanations that can map the contributions of words toward the predicted class. The experimental results on two benchmark fake review detection datasets demonstrate that our predictive models achieve state-of-the-art performance and outperform several existing methods. Furthermore, the empirical user evaluation of the generated explanations concludes which important information needs to be considered in generating explanations in the context of fake review identification.

cs.CL↗

arXiv2024

Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments

Smart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption. However, the adoption of artificial intelligence (AI)-enabled decision-making models in smart home systems faces challenges due to the complexity and black-box nature of these systems, leading to concerns about explainability, trust, transparency, accountability, and fairness. The emerging field of explainable artificial intelligence (XAI) addresses these issues by providing explanations for the models' decisions and actions. While state-of-the-art XAI methods are beneficial for AI developers and practitioners, they may not be easily understood by general users, particularly household members. This paper advocates for human-centered XAI methods, emphasizing the importance of delivering readily comprehensible explanations to enhance user satisfaction and drive the adoption of smart home systems. We review state-of-the-art XAI methods and prior studies focusing on human-centered explanations for general users in the context of smart home applications. Through experiments on two smart home application scenarios, we demonstrate that explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions. We thus argue for the necessity of a human-centric approach in representing explanations in smart home systems and highlight relevant human-computer interaction (HCI) methodologies, including user studies, prototyping, technology probes analysis, and heuristic evaluation, that can be employed to generate and present human-centered explanations to users.

cs.HC↗

arXiv2023

Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System

Explainable Artificial Intelligence is a concept aimed at making complex algorithms transparent to users through a uniform solution. Researchers have highlighted the importance of integrating domain specific contexts to develop explanations tailored to end users. In this study, we focus on the Schufa housing scoring system in Germany and investigate how users information needs and expectations for explanations vary based on their roles. Using the speculative design approach, we asked business information students to imagine user interfaces that provide housing credit score explanations from the perspectives of both tenants and landlords. Our preliminary findings suggest that although there are general needs that apply to all users, there are also conflicting needs that depend on the practical realities of their roles and how credit scores affect them. We contribute to Human centered XAI research by proposing future research directions that examine users explanatory needs considering their roles and agencies.

cs.AI↗