arXiv subjects

Sijia Xiao

Publications and source records attributed to Sijia Xiao.

6 recordsLinked to original sources

arXiv2026

Push and Pushback in Contesting AI: Demands for and Resistance to Accountability

As AI becomes increasingly embedded in daily life, it has been shown to fail critically, cause harm, and spark public controversy, prompting affected communities, workers, and public-interest groups to contest it. Yet how these contestations unfold in practice remains underexplored. We address this gap by developing an empirically grounded account of AI contestation dynamics. We do so through a thematic analysis of 43 real-world cases in which affected actors direct demands toward those responsible for AI development and deployment, seeking redress, influence, or changes to AI practices. Situating our work within Bovens's relational model of accountability, we conceptualize contestation as accountability-seeking: a dynamic, iterative process in which actors "from below" direct explicit demands at actors "from above," who respond by accepting, resisting, or circumventing accountability. Our analysis produces empirically grounded categories of contestation strategies, institutional response tactics, outcome types, and the contextual factors that shape them, illuminating how accountability is pursued and evaded in practice. We show that those being contested often deploy a range of strategies to limit their accountability. Based on these insights, we offer guidance for researchers, policymakers, advocates, and other stakeholders seeking to support effective AI contestation, with particular attention to anticipating and countering institutional strategies used to evade accountability.

cs.HC↗

arXiv2026

Vipera: Blending Visual and LLM-Driven Guidance for Systematic Auditing of Text-to-Image Generative AI

Despite their increasing capabilities, text-to-image generative AI systems are known to produce biased, offensive, and otherwise problematic outputs. While recent advancements have supported testing and auditing of generative AI, existing auditing methods still face challenges in supporting effectively explore the vast space of AI-generated outputs in a structured way. To address this gap, we conducted formative studies with five AI auditors and synthesized five design goals for supporting systematic AI audits. Based on these insights, we developed Vipera, an interactive auditing interface that employs multiple visual cues including a scene graph to facilitate image sensemaking and inspire auditors to explore and hierarchically organize the auditing criteria. Additionally, Vipera leverages LLM-powered suggestions to facilitate exploration of unexplored auditing directions. Through a controlled experiment with 24 participants experienced in AI auditing, we demonstrate Vipera's effectiveness in helping auditors navigate large AI output spaces and organize their analyses while engaging with diverse criteria.

cs.HC↗

arXiv2025

What Comes After Harm? Mapping Reparative Actions in AI through Justice Frameworks

As Artificial Intelligence (AI) systems are integrated into more aspects of society, they offer new capabilities but also cause a range of harms that are drawing increasing scrutiny. A large body of work in the Responsible AI community has focused on identifying and auditing these harms. However, much less is understood about what happens after harm occurs: what constitutes reparation, who initiates it, and how effective these reparations are. In this paper, we develop a taxonomy of AI harm reparation based on a thematic analysis of real-world incidents. The taxonomy organizes reparative actions into four overarching goals: acknowledging harm, attributing responsibility, providing remedies, and enabling systemic change. We apply this framework to a dataset of 1,060 AI-related incidents, analyzing the prevalence of each action and the distribution of stakeholder involvement. Our findings show that reparation efforts are concentrated in early, symbolic stages, with limited actions toward accountability or structural reform. Drawing on theories of justice, we argue that existing responses fall short of delivering meaningful redress. This work contributes a foundation for advancing more accountable and reparative approaches to Responsible AI.

cs.HC↗

arXiv2025

SnuggleSense: Empowering Online Harm Survivors Through a Structured Sensemaking Process

Online interpersonal harm, such as cyberbullying and sexual harassment, remains a pervasive issue on social media platforms. Traditional approaches, primarily content moderation, often overlook survivors' needs and agency. We introduce SnuggleSense, a system that empowers survivors through structured sensemaking. Inspired by restorative justice practices, SnuggleSense guides survivors through reflective questions, offers personalized recommendations from similar survivors, and visualizes plans using interactive sticky notes. A controlled experiment demonstrates that SnuggleSense significantly enhances sensemaking compared to an unstructured process of making sense of the harm. We argue that SnuggleSense fosters community awareness, cultivates a supportive survivor network, and promotes a restorative justice-oriented approach toward restoration and healing. We also discuss design insights, such as tailoring informational support and providing guidance while preserving survivors' agency.

cs.HC↗

arXiv2025

Vipera: Towards systematic auditing of generative text-to-image models at scale

Generative text-to-image (T2I) models are known for their risks related such as bias, offense, and misinformation. Current AI auditing methods face challenges in scalability and thoroughness, and it is even more challenging to enable auditors to explore the auditing space in a structural and effective way. Vipera employs multiple visual cues including a scene graph to facilitate image collection sensemaking and inspire auditors to explore and hierarchically organize the auditing criteria. Additionally, it leverages LLM-powered suggestions to facilitate exploration of unexplored auditing directions. An observational user study demonstrates Vipera's effectiveness in helping auditors organize their analyses while engaging with diverse criteria.

cs.HC↗

arXiv2023

Addressing interpersonal harm in online gaming communities: the opportunities and challenges for a restorative justice approach

Most social media platforms implement content moderation to address interpersonal harms such as harassment. Content moderation relies on offender-centered, punitive approaches, e.g., bans and content removal. We consider an alternative justice framework, restorative justice, which aids victims in healing, supports offenders in repairing the harm, and engages community members in addressing the harm collectively. To assess the utility of restorative justice in addressing online harm, we interviewed 23 users from Overwatch gaming communities, including moderators, victims, and offenders; such communities are particularly susceptible to harm, with nearly three quarters of all online game players suffering from some form of online abuse. We study how the communities currently handle harm cases through the lens of restorative justice and examine their attitudes toward implementing restorative justice processes. Our analysis reveals that cultural, technical, and resource-related obstacles hinder implementation of restorative justice within the existing punitive framework despite online community needs and existing structures to support it. We discuss how current content moderation systems can embed restorative justice goals and practices and overcome these challenges.

cs.HC↗