arXiv subjects

Jennah Gosciak

Publications and source records attributed to Jennah Gosciak.

5 recordsLinked to original sources

arXiv2026

Scrutinizing Index-Based Risk Assessments: A Case Study in NYC Decision-making for Heat Emergency Management

Cities are increasingly turning to large-scale data analysis and machine learning to make consequential decisions. While the algorithmic fairness community has focused on analyzing the risks and benefits associated with these complex methods, there has been much less scrutiny of the many simpler, but still widely used, data-driven tools that support government decision-making in a variety of settings. In this work, we study hand-crafted indices for geographic targeting and decision-making in emergency management -- a field responsible for coordinating preparedness and response efforts to hazards ranging from natural disasters to human threats. Indices, which capture abstract principles and overarching priorities (e.g., reducing social vulnerability), are low-complexity models that statistically aggregate chosen variables. They are generally flexible and interpretable, but can also be sensitive to key design choices and require strong assumptions. Through a case study of decision-making for extreme heat emergencies in NYC, we examine the challenges that practitioners may face in selecting an index for preparedness and response actions. We map empirical findings from index-based simulations to concerns related to validity and reliability from the measurement literature and show via sensitivity analyses that different reasonable choices of input variables or spatial scale can result in substantive differences to index risk scores, thereby affecting downstream government decision-making. We contrast these challenges with considerations for developing predictive algorithms that more narrowly relate to concrete, measurable outcomes. Ultimately, we provide generalizable recommendations that practitioners and public-sector technologists can use for navigating the trade-offs between indices and predictive algorithms in other government settings.

cs.CY↗

arXiv2026

A Critical Pragmatism Approach for Algorithmic Fairness: Lessons from Urban Planning Theory

As data scientists grapple with increasingly complex ethical decisions in machine learning (ML) and data science, the field of algorithmic fairness has offered multiple solutions, from formal mathematical definitions to holistic notions of fairness drawn from various academic disciplines. However, navigating and implementing these fairness approaches in practice remains an ongoing challenge. In this paper, we draw a parallel between the types of problems arising in algorithmic fairness and urban planning. We frame algorithmic fairness problems as `wicked problems,' a term originating from the planning and policy space to describe the intractable, value-laden, and complex nature of this work. As such, we argue that the field of algorithmic fairness can learn from theoretical work in urban planning in ameliorating its own set of wicked problems. Urban planning is typically concerned with practical issues of governance, resource allocation, stakeholder engagement, and conflicts involving deep-seated differences. These are challenges that existing fairness frameworks can easily overlook. We present a flexible framework for designing fairer algorithms based on the urban planning theory approach of critical pragmatism -- a reflective and deliberative approach to addressing wicked problems that considers what practitioners actually do in the face of conflict and power. We provide specific recommendations and apply them to several case studies in ML and algorithm design: automated mortgage lending, school choice, and feminicide counterdata collection. Researchers and practitioners can incorporate these recommendations derived from urban planning into their ongoing work to more holistically address practical problems arising in fair algorithm design.

cs.CY↗

arXiv2026

LLMs in social services: How does chatbot accuracy affect human accuracy?

Social service programs like the Supplemental Nutrition Assistance Program (SNAP, or food stamps) have eligibility rules that can be challenging to understand. For nonprofit caseworkers who often support clients in navigating a dozen or more complex programs, LLM-based chatbots may offer a means to provide better, faster help to clients whose situations may be less common. In this paper, we measure the potential effects of LLM-based chatbot suggestions on caseworkers' ability to provide accurate guidance. We first created a 770-question multiple-choice benchmark dataset of difficult, but realistic questions that a caseworker might receive. Next, using these benchmark questions and corresponding expert-verified answers, we conducted a randomized experiment with caseworkers recruited from nonprofit outreach organizations in Los Angeles. Caseworkers in the control condition did not see chatbot suggestions and had a mean accuracy of 49%. Caseworkers in the treatment condition saw chatbot suggestions that we artificially varied to range in aggregate accuracy from low (53%) to high (100%). Caseworker performance significantly improves as chatbot quality improves: high-quality chatbots (96-100% accurate) improved caseworker accuracy by 27 percentage points. At the question-level, incorrect chatbot suggestions substantially reduce caseworker accuracy, with a two-thirds reduction on easy questions where the control group performed best (without chatbot suggestions). Finally, improvements in caseworker accuracy level off as chatbot accuracy increases, a phenomenon that we call the "AI underreliance plateau," which is a concern for real-world deployment and highlights the importance of evaluating human-in-the-loop tools with their users.

cs.HC↗

arXiv2025

Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments

Conducting disparity assessments at regular time intervals is critical for surfacing potential biases in decision-making and improving outcomes across demographic groups. Because disparity assessments fundamentally depend on the availability of demographic information, their efficacy is limited by the availability and consistency of available demographic identifiers. While prior work has considered the impact of missing data on fairness, little attention has been paid to the role of delayed demographic data. Delayed data, while eventually observed, might be missing at the critical point of monitoring and action -- and delays may be unequally distributed across groups in ways that distort disparity assessments. We characterize such impacts in healthcare, using electronic health records of over 5M patients across primary care practices in all 50 states. Our contributions are threefold. First, we document the high rate of race and ethnicity reporting delays in a healthcare setting and demonstrate widespread variation in rates at which demographics are reported across different groups. Second, through a set of retrospective analyses using real data, we find that such delays impact disparity assessments and hence conclusions made across a range of consequential healthcare outcomes, particularly at more granular levels of state-level and practice-level assessments. Third, we find limited ability of conventional methods that impute missing race in mitigating the effects of reporting delays on the accuracy of timely disparity assessments. Our insights and methods generalize to many domains of algorithmic fairness where delays in the availability of sensitive information may confound audits, thus deserving closer attention within a pipeline-aware machine learning framework.

cs.CY↗

arXiv2025

AI Rules? Characterizing Reddit Community Policies Towards AI-Generated Content

How are Reddit communities responding to AI-generated content? We explored this question through a large-scale analysis of subreddit community rules and their change over time. We collected the metadata and community rules for over $300,000$ public subreddits and measured the prevalence of rules governing AI. We labeled subreddits and AI rules according to existing taxonomies from the HCI literature and a new taxonomy we developed specific to AI rules. While rules about AI are still relatively uncommon, the number of subreddits with these rules more than doubled over the course of a year. AI rules are more common in larger subreddits and communities focused on art or celebrity topics, and less common in those focused on social support. These rules often focus on AI images and evoke, as justification, concerns about quality and authenticity. Overall, our findings illustrate the emergence of varied concerns about AI, in different community contexts. Platform designers and HCI researchers should heed these concerns if they hope to encourage community self-determination in the age of generative AI. We make our datasets public to enable future large-scale studies of community self-governance.

cs.CY↗