Yi-Shyuan Chiang

Research into community content moderation often assumes that moderation teams govern with a single, unified voice. However, recent work has found that moderators disagree with one another at modest, but concerning rates. The problem is not the root disagreements themselves. Subjectivity in moderation is unavoidable, and there are clear benefits to including diverse perspectives within a moderation team. Instead, the crux of the issue is that, due to resource constraints, moderation decisions end up being made by individual decision-makers. The result is decision-making that is inconsistent, which is frustrating for community members. To address this, we develop Venire, an ML-backed system for panel review on Reddit. Venire uses a machine learning model trained on log data to identify the cases where moderators are most likely to disagree. Venire fast-tracks these cases for multi-person review. Ideally, Venire allows moderators to surface and resolve disagreements that would have otherwise gone unnoticed. We conduct three studies through which we design and evaluate Venire: a set of formative interviews with moderators, technical evaluations on two datasets, and a think-aloud study in which moderators used Venire to make decisions on real moderation cases. Quantitatively, we demonstrate that Venire is able to improve decision consistency and surface latent disagreements. Qualitatively, we find that Venire helps moderators resolve difficult moderation cases more confidently. Venire represents a novel paradigm for human-AI content moderation, and shifts the conversation from replacing human decision-making to supporting it.

Social media websites thrive on user engagement by employing Attention Capture Damaging Patterns (ACDPs), e.g., infinite scroll, that prey on cognitive vulnerabilities to distract users. Prior work has taxonomized these ACDPs, but we have yet to measure how the presence of ACDPs impacts perceived distraction nor how mechanisms that suppress ACDPs reduce distraction. We conducted a two-week, mixed-methods field study with 29 participants to model how people get distracted when browsing social media websites, and how ACDPs might play a role. In the first week of the study, we sample participants’ in-situ perceptions of distraction, subjective perceptions of the browsing session (e.g., satisfaction), and the presence/absence of ACDPs. Participants reported feeling distracted 28% of the time, and that subjective perceptions and some ACDPs (e.g., notifications) highly correlated with when they felt distracted. In the second week of the study, participants were given access to Purpose Mode — a browser extension that allows users to “toggle off” ACDPs. Participants reported feeling distracted only 7% of the time and spent 21 fewer daily minutes browsing these websites. We discovered that Purpose Mode empowered users to feel more in control over their social media browsing and made participants feel less irritated and frustrated.

Data collection without proper consent is a growing concern as smart home devices gain prevalence. It is especially difficult to obtain consent from incidental users because they may be unaware or feel pressured to consent. To understand what appropriate consent means in smart homes, we conducted an online survey (N=360) covering 6 common consent facets: freely given, revertible, informed, enthusiastic, specific, and unburdensome. We study how these facets affect perceived acceptability of data collection and how users would allocate responsibility for obtaining consent. Our results show that all facets have meaningful impacts on perceived acceptability of data collection, and eroding freely given had the greatest impact. Device owners were considered the most responsible for obtaining consent. Based on these findings, we provide recommendations for users, device manufacturers, and policymakers to improve consent practices in smart homes, such as designing consent interfaces that prioritize multiple facets of consent.

AI-enabled smart-home agents that automate household routines are increasingly viable, but the design space of how and what such systems should communicate with their users remains underexplored. Through a user-enactment study, we identified various interpretations of and feelings toward such a system's confidence in its automated acts. That confidence and their own mental models influenced what and how the participants wanted the system to communicate, as well as how they would assess, diagnose, and subsequently improve it. Automated acts resulted from false predictions were not generally considered improper, provided that they were perceived as reasonable or potentially useful. The participants' improvement strategies were of four general types, all of which will be discussed. Factors affecting their preferred levels of involvement in automated acts and their interest in system confidence were also identified. We conclude by making practical design recommendations for the user-system communication design spaces of smart-home routine assistants.

Virtual reality (VR) platforms provide their users with immersive virtual environments, but disconnect them from real-world events. The increasing length of VR sessions can therefore be expected to boost users' needs to obtain information about external occurrences such as message arrival. Yet, how and when to present these real-world notifications to users engaged in VR activities remains underexplored. We conducted an experiment to investigate individuals' receptivity during four VR activities (Loading, 360 Video, Treasure Hunt, Rhythm Game) to message notifications delivered using three types of displays (head-mounted, controller, and movable panel). While higher engagement generally led to higher perceptions that notifications were ill-timed and/or disruptive, the suitability of notification displays to VR activities was influenced by the time-sensitiveness of VR content, overlapping use of modalities for delivering alerts, the display locations, and a requirement that the display be moved for notifications to be seen. Specific design suggestions are also provided.

We present FRIDAYS, a financial risk information detecting and analyzing system that enables financial professionals to efficiently comprehend financial reports in terms of risk and domain-specific sentiment cues. Our system is designed to integrate multiple NLP models trained on financial reports but on different levels (i.e., word, multi-word, and sentence levels) and to illustrate the prediction results generated by the models. The system is available online at https://cfda.csie.org/FRIDAYS/.