arXiv subjects

Priya Kumar

Publications and source records attributed to Priya Kumar.

3 recordsLinked to original sources

arXiv2026

Opportunities and Challenges of Operating Semi-Autonomous Vehicles: A Layered Vulnerability Perspective

This study examines how vulnerability is produced for human operators of Tesla's Full Self-Driving (FSD), a Level 2 semi-autonomous vehicle (SAV) system, by applying Florencia Luna's layered vulnerability framework. While existing road safety models conceptualize vulnerability as a fixed attribute of external road users, emerging evidence suggests that semi-autonomous vehicle operators themselves experience dynamic and situational vulnerability as they supervise automated systems that they do not fully control. To investigate this phenomenon, we conducted semi-structured interviews with 17 active FSD users, analyzing their accounts through a combined deductive-inductive coding process aligned with Luna's framework. Findings reveal three interacting layers of operator vulnerability, namely psychological, operational, and social. Vulnerability emerged not from any single layer but from how these layers converged in specific situations, creating fluctuating supervisory demands and uneven capacity to recognize and manage risk. The findings extend debates on contextual trust calibration, automation complacency, and meaningful human control by demonstrating how factors commonly treated as liabilities such as trust or informal learning, can both increase and mitigate vulnerability depending on context. This analysis determines the need for design and regulatory interventions that address psychological, operational, and social conditions together rather than in isolation, and highlights how responsibility is implicitly shifted onto individual operators within inadequately supported supervisory regimes.

cs.HC↗

arXiv2025

Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes

Despite high performance on clinical benchmarks, large language models may reach correct conclusions through faulty reasoning, a failure mode with safety implications for oncology decision support that is not captured by accuracy-based evaluation. In this two-cohort retrospective study, we developed a hierarchical taxonomy of reasoning errors from GPT-4 chain-of-thought responses to real oncology notes and tested its clinical relevance. Using breast and pancreatic cancer notes from the CORAL dataset, we annotated 600 reasoning traces to define a three-tier taxonomy mapping computational failures to cognitive bias frameworks. We validated the taxonomy on 822 responses from prostate cancer consult notes spanning localized through metastatic disease, simulating extraction, analysis, and clinical recommendation tasks. Reasoning errors occurred in 23 percent of interpretations and dominated overall errors, with confirmation bias and anchoring bias most common. Reasoning failures were associated with guideline-discordant and potentially harmful recommendations, particularly in advanced disease management. Automated evaluators using state-of-the-art language models detected error presence but could not reliably classify subtypes. These findings show that large language models may provide fluent but clinically unsafe recommendations when reasoning is flawed. The taxonomy provides a generalizable framework for evaluating and improving reasoning fidelity before clinical deployment.

cs.CL↗

arXiv2023

Losing One's Story: How Vulnerable Users Experience Harm in Online Support Seeking

Online support communities are a critical resource for individuals facing distress, stigma, and limited offline support. However, these spaces are not uniformly supportive, particularly for users with little margin for error. In this paper, we examine how vulnerable users experience harm within online support-seeking interactions. Through 25 semi-structured interviews with Reddit users, we show that support seeking is often a constrained practice shaped by structural vulnerability. We introduce the concept of \emph{narrative harm} to describe how participants experience harm through losses of narrative authority, coherence, and space. Their personal disclosures are questioned, reframed, or displaced by others, reflecting asymmetries in voice, credibility, and interpretive power embedded in platform dynamics and moderation regimes. In response, users engage in defensive strategies, including selective self-disclosure, self-silencing, identity separation, and informal mutual aid, shifting the burden of safety onto those already most vulnerable. Our findings highlight how current platform designs insufficiently account for situated vulnerability and redistribute discursive power away from support seekers. We discuss implications for the design of online support systems that better preserve narrative integrity and reduce the burden of safety labor.

cs.HC↗