arXiv subjects

Vyoma Raman

Publications and source records attributed to Vyoma Raman.

7 recordsLinked to original sources

arXiv2026

Abstracted Away: Resisting Alienation and Ungrounded Abstraction in AI Research Communities

Logics of abstraction in computational AI research often push important forms of knowledge and reflection aside: dominant standards of legitimacy separate from lived experience of harm; the goals of work misalign with the practices that operationalize them; and career demands crowd out critical reflection. Even as prior academic and community-oriented efforts have sought to recontextualize and challenge common practices, exposure to sociotechnical harms and epistemic injustice persists. As three early-career critical AI researchers, we experienced this as alienation: feeling like outsiders in our research communities. This alienation has involved having some aspects of our backgrounds overlooked and others tokenized. We argue our alienation occurred through mechanisms that mirror abstraction by creating distance from relevant material realities. Beyond abstraction's role in computational AI research as a foundational practice structuring complex computational tasks, we have encountered it as a social norm in computational research spaces, illustrated through an autoethnographic inquiry into our alienation. We narrate three vignettes describing how we encountered and resisted alienation in our research communities. By analyzing themes across these accounts, we construct an interpretive framework of alienation categorizing its preconditions, mechanisms, and harms. Finally, we identify affect abstraction, one of the mechanisms of alienation we describe, as a high-leverage mechanism that is resistible by staying attuned to our affective responses, and collective action as a way to reduce risk and isolation when engaging in resistance. To assist others with similar reflection, we present our framework as a hermeneutic resource. Critical self-reflection and meaning-making are necessary steps toward challenging exclusionary disciplinary norms and cultivating more inclusive forms of AI research.

cs.CY↗

arXiv2025

Attention to Non-Adopters

Although language model-based chat systems are increasingly used in daily life, most Americans remain non-adopters of chat-based LLMs -- as of June 2025, 66% had never used ChatGPT. At the same time, LLM development and evaluation rely mainly on data from adopters (e.g., logs, preference data), focusing on the needs and tasks for a limited demographic group of adopters in terms of geographic location, education, and gender. In this position paper, we argue that incorporating non-adopter perspectives is essential for developing broadly useful and capable LLMs. We contend that relying on methods that focus primarily on adopters will risk missing a range of tasks and needs prioritized by non-adopters, entrenching inequalities in who benefits from LLMs, and creating oversights in model development and evaluation. To illustrate this claim, we conduct case studies with non-adopters and show: how non-adopter needs diverge from those of current users, how non-adopter needs point us towards novel reasoning tasks, and how to systematically integrate non-adopter needs via human-centered methods.

cs.CY↗

arXiv2025

Assessing Human Rights Risks in AI: A Framework for Model Evaluation

The Universal Declaration of Human Rights and other international agreements outline numerous inalienable rights that apply across geopolitical boundaries. As generative AI becomes increasingly prevalent, it poses risks to human rights such as non-discrimination, health, and security, which are also central concerns for AI researchers focused on fairness and safety. We contribute to the field of algorithmic auditing by presenting a framework to computationally assess human rights risk. Drawing on the UN Guiding Principles on Business and Human Rights, we develop an approach to evaluating a model to make grounded claims about the level of risk a model poses to particular human rights. Our framework consists of three parts: selecting tasks that are likely to pose human rights risks within a given context, designing metrics to measure the scope, scale, and likelihood of potential risks from that task, and analyzing rights with respect to the values of those metrics. Because a human rights approach centers on real-world harms, it requires evaluating AI systems in the specific contexts in which they are deployed. We present a case study of large language models in political news journalism, demonstrating how our framework helps to design an evaluation and benchmarking different models. We then discuss the implications of the results for the rights of access to information and freedom of thought and broader considerations for adopting this approach.

cs.CY↗

arXiv2025

Disclosure and Evaluation as Fairness Interventions for General-Purpose AI

Despite conflicting definitions and conceptions of fairness, AI fairness researchers broadly agree that fairness is context-specific. However, when faced with general-purpose AI, which by definition serves a range of contexts, how should we think about fairness? We argue that while we cannot be prescriptive about what constitutes fair outcomes, we can specify the processes that different stakeholders should follow in service of fairness. Specifically, we consider the obligations of two major groups: system providers and system deployers. While system providers are natural candidates for regulatory attention, the current state of AI understanding offers limited insight into how upstream factors translate into downstream fairness impacts. Thus, we recommend that providers invest in evaluative research studying how model development decisions influence fairness and disclose whom they are serving their models to, or at the very least, reveal sufficient information for external researchers to conduct such research. On the other hand, system deployers are closer to real-world contexts and can leverage their proximity to end users to address fairness harms in different ways. Here, we argue they should responsibly disclose information about users and personalization and conduct rigorous evaluations across different levels of fairness. Overall, instead of focusing on enforcing fairness outcomes, we prioritize intentional information-gathering by system providers and deployers that can facilitate later context-aware action. This allows us to be specific and concrete about the processes even while the contexts remain unknown. Ultimately, this approach can sharpen how we distribute fairness responsibilities and inform more fluid, context-sensitive interventions as AI continues to advance.

cs.CY↗

arXiv2025

AI for Scaling Legal Reform: Mapping and Redacting Racial Covenants in Santa Clara County

Legal reform can be challenging in light of the volume, complexity, and interdependence of laws, codes, and records. One salient example of this challenge is the effort to restrict and remove racially restrictive covenants, clauses in property deeds that historically barred individuals of specific races from purchasing homes. Despite the Supreme Court holding such racial covenants unenforceable in 1948, they persist in property records across the United States. Many jurisdictions have moved to identify and strike these provisions, including California, which mandated in 2021 that all counties implement such a process. Yet the scale can be overwhelming, with Santa Clara County (SCC) alone having over 24 million property deed documents, making purely manual review infeasible. We present a novel approach to addressing this pressing issue, developed through a partnership with the SCC Clerk-Recorder's Office. First, we leverage an open large language model, finetuned to detect racial covenants with high precision and recall. We estimate that this system reduces manual efforts by 86,500 person hours and costs less than 2% of the cost for a comparable off-the-shelf closed model. Second, we illustrate the County's integration of this model into responsible operational practice, including legal review and the creation of a historical registry, and release our model to assist the hundreds of jurisdictions engaged in similar efforts. Finally, our results reveal distinct periods of utilization of racial covenants, sharp geographic clustering, and the disproportionate role of a small number of developers in maintaining housing discrimination. We estimate that by 1950, one in four properties across the County were subject to racial covenants.

cs.CL↗

arXiv2023

Centering the Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection

The impact of AI models on marginalized communities has traditionally been measured by identifying performance differences between specified demographic subgroups. Though this approach aims to center vulnerable groups, it risks obscuring patterns of harm faced by intersectional subgroups or shared across multiple groups. To address this, we draw on theories of marginalization from disability studies and related disciplines, which state that people farther from the norm face greater adversity, to consider the "margins" in the domain of toxicity detection. We operationalize the "margins" of a dataset by employing outlier detection to identify text about people with demographic attributes distant from the "norm". We find that model performance is consistently worse for demographic outliers, with mean squared error (MSE) between outliers and non-outliers up to 70.4% worse across toxicity types. It is also worse for text outliers, with a MSE up to 68.4% higher for outliers than non-outliers. We also find text and demographic outliers to be particularly susceptible to errors in the classification of severe toxicity and identity attacks. Compared to analysis of disparities using traditional demographic breakdowns, we find that our outlier analysis frequently surfaces greater harms faced by a larger, more intersectional group, which suggests that outlier analysis is particularly beneficial for identifying harms against those groups.

cs.CL↗

arXiv2023

Bias, Consistency, and Partisanship in U.S. Asylum Cases: A Machine Learning Analysis of Extraneous Factors in Immigration Court Decisions

In this study, we introduce a novel two-pronged scoring system to measure individual and systemic bias in immigration courts under the U.S. Executive Office of Immigration Review (EOIR). We analyze nearly 6 million immigration court proceedings and 228 case features to build on prior research showing that U.S. asylum decisions vary dramatically based on factors that are extraneous to the merits of a case. We close a critical gap in the literature of variability metrics that can span space and time. Using predictive modeling, we explain 58.54% of the total decision variability using two metrics: partisanship and inter-judge cohort consistency. Thus, whether the EOIR grants asylum to an applicant or not depends in majority on the combined effects of the political climate and the individual variability of the presiding judge - not the individual merits of the case. Using time series analysis, we also demonstrate that partisanship increased in the early 1990s but plateaued following the turn of the century. These conclusions are striking to the extent that they diverge from the U.S. immigration system's commitments to independence and due process. Our contributions expose systemic inequities in the U.S. asylum decision-making process, and we recommend improved and standardized variability metrics to better diagnose and monitor these issues.

cs.SI↗