arXiv subjects

Hoda Heidari

Publications and source records attributed to Hoda Heidari.

At least 19 recordsLinked to original sources

arXiv2026

Beyond the Single Turn: Reframing Refusals as Dynamic Experiences Embedded in the Context of Mental Health Support Interactions with LLMs

Content Warning: This paper contains participant quotes and discussions related to mental health challenges, emotional distress, and suicidal ideation. Large language models (LLMs) are increasingly used for mental health support, yet the model safeguards -- particularly refusals to engage with sensitive content -- remain poorly understood from the perspectives of users and mental health professionals (MHPs) and have been reported to cause real-world harms. This paper presents findings from a sequential mixed-methods study examining how LLM refusals are experienced and interpreted in mental health support interactions. Through surveys (N=53) and in-depth interviews (N=16) with individuals using LLMs for mental health support and MHPs, we reveal that refusals are not isolated, single-turn system behaviors but rather constitute dynamic, multi-phase experiences: pre-refusal expectation formation, refusal triggering and encounter, refusal message framing, resource referral provision, and post-refusal outcomes. We contribute a multi-phase framework for evaluating refusals beyond binary policy compliance accuracy and design recommendations for future refusal mechanisms. These findings suggest that understanding LLM refusals requires moving beyond single-turn interactions toward recognizing them as holistic experiences embedded within users' support-seeking trajectories and the broader LLM design pipeline.

cs.HC↗

arXiv2026

Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics

Measurement is essential to improving AI performance and mitigating harms for marginalized groups. As generative AI systems are rapidly deployed across geographies and contexts, AI measurement practices must be designed to support repeatable, automatable application across different models, datasets, and evaluation settings. But the drive to automate measurement can be in tension with the ability for measurement instruments to capture the expertise and perspectives of communities impacted by AI. Recent work advocates for breaking measurement into several key stages: first moving from an abstract concept to be measured into a precise, "systematized" concept; next operationalizing the systematized concept into a concrete measurement instrument; and finally applying the measurement instrument on data to produce measurements. This opens up an opportunity to concentrate community engagement in the systematization phase before operationalizing and applying measurement instruments. In this paper, we explore how to involve communities in systematizing the concept of "cultural appropriateness" in text-to-image models' representation of culturally significant artifacts through case studies with three communities: blind and low vision individuals residing in the UK, residents of Kerala, and residents of Tamil Nadu. Our systematized concepts reflect community members' lived experiences interacting with each artifact and how they want their material culture to be depicted, demonstrating the value of community involvement in defining valid measures. We explore how these systematized concepts can be operationalized into automated measurement instruments that could be applied using a multimodal LLM-as-a-judge approach and challenges that remain. We reflect on the benefits and limitations of such approaches.

cs.CY↗

arXiv2026

What People See (and Miss) About Generative AI Risks: Perceptions of Failures, Risks, and Who Should Address Them

Despite growing concerns about the risks of Generative AI (GenAI), there is limited understanding of public perceptions of these risks and their associated failure modes -- defined as recurring patterns of sociotechnical breakdown across the GenAI lifecycle that contribute to risks of real-world harm. To address this gap, we present a survey instrument, validated with eight subject matter experts and deployed on a sample of 960 U.S.-based participants, to assess awareness and perceptions of GenAI's failure modes, their associated risks, and stakeholder responsibilities to address them. To support realism and content validity, our instrument is structured around scenarios grounded in publicly reported incidents and a taxonomy of GenAI's failure modes. Findings suggest that our instrument is (1) effective for assessing risk awareness and perceptions in a way that is grounded in people's current contexts of use, yet is extensible to new contexts that will inevitably arise; and (2) potentially useful for informing the design of AI literacy tools and interventions. We argue for AI literacy and governance approaches that align with how people encounter and reason about GenAI in everyday life.

cs.HC↗

arXiv2026

Disclosure or Marketing? Analyzing the Efficacy of Vendor Self-reports for Vetting Public-sector AI

Documentation-based disclosure has become a central governance strategy for responsible AI, particularly in public-sector procurement. Tools such as model cards, datasheets, and AI FactSheets are increasingly expected to support accountability, risk assessment, and informed decision-making across organizational boundaries. Yet there is limited empirical evidence about how these artifacts are produced, interpreted, and used in practice. In this paper, we present a qualitative study of the GovAI Coalition FactSheet, a widely adopted transparency document designed to support AI procurement and governance in government contexts. Drawing on semi-structured interviews with vendors and public-sector practitioners, alongside a systematic analysis of completed FactSheets, we examine how FactSheets are used, what information they surface, and where they fall short. We find that FactSheets are asked to serve multiple and conflicting purposes simultaneously: showcasing vendor offerings, supporting evaluation and due diligence, and facilitating early-stage dialogue between vendors and agencies. These competing expectations, combined with the structural constraints of voluntary and public self-disclosure, limit the ability of FactSheets to function as standalone evaluation or risk-assessment tools. At the same time, our findings suggest that when understood as relational artifacts used to establish trust, shared understanding, and ongoing dialogue, FactSheets can help create conditions that support more meaningful disclosure and governance over time.

cs.HC↗

arXiv2026

Navigating Uncertainties: How GenAI Developers Document Their Models on Open-Source Platforms

Model documentation plays a crucial role in promoting transparency and responsible development of AI systems. With the rise of Generative AI (GenAI), open-source platforms have increasingly become hubs for hosting and distributing these models, prompting platforms like Hugging Face to develop dedicated model documentation guidelines that align with responsible AI principles. Despite these growing efforts, there remains a lack of understanding of how developers document their GenAI models on open-source platforms. Through interviews with 13 GenAI developers active on open-source platforms, we provide empirical insights into their documentation practices and challenges. Our analysis reveals that despite existing resources, developers of GenAI models still face multiple layers of uncertainties in their model documentation: (1) uncertainties about what specific content should be included; (2) uncertainties about how to effectively report key components of their models; and (3) uncertainties in deciding who should take responsibilities for various aspects of model documentation. Based on our findings, we discuss the implications for policymakers, open-source platforms, and the research community to support meaningful, effective and actionable model documentation in the GenAI era, including cultivating better community norms, building robust evaluation infrastructures, and clarifying roles and responsibilities.

cs.HC↗

arXiv2026

Toward Third-Party Assurance of AI Systems: Design Requirements, Prototype, and Early Testing

As Artificial Intelligence (AI) systems proliferate, the need for systematic, transparent, and actionable processes for evaluating them is growing. While many resources exist to support AI evaluation, they have several limitations. Few address both the process of designing, developing, and deploying an AI system and the outcomes it produces. Furthermore, few are end-to-end and operational, give actionable guidance, or present evidence of usability or effectiveness in practice. In this paper, we introduce a third-party AI assurance framework that addresses these gaps. We focus on third-party assurance to prevent conflict of interest and ensure credibility and accountability of the process. We begin by distinguishing assurance from audits in several key dimensions. Then, following design principles, we reflect on the shortcomings of existing resources to identify a set of design requirements for AI assurance. We then construct a prototype of an assurance process that consists of (1) a responsibility assignment matrix to determine the different levels of involvement each stakeholder has at each stage of the AI lifecycle, (2) an interview protocol for each stakeholder of an AI system, (3) a maturity matrix to assess AI systems' adherence to best practices, and (4) a template for an assurance report that draws from more mature assurance practices in business accounting. We conduct early validation of our AI assurance framework by applying the framework to two distinct AI use cases -- a business document tagging tool for downstream processing in a large private firm, and a housing resource allocation tool in a public agency -- and conducting six expert validation interviews. Our findings show early evidence that our AI assurance framework is sound and comprehensive, usable across different organizational contexts, and effective at identifying bespoke issues with AI systems.

cs.CY↗

arXiv2025

Moral Change or Noise? On Problems of Aligning AI With Temporally Unstable Human Feedback

Alignment methods in moral domains seek to elicit moral preferences of human stakeholders and incorporate them into AI. This presupposes moral preferences as static targets, but such preferences often evolve over time. Proper alignment of AI to dynamic human preferences should ideally account for "legitimate" changes to moral reasoning, while ignoring changes related to attention deficits, cognitive biases, or other arbitrary factors. However, common AI alignment approaches largely neglect temporal changes in preferences, posing serious challenges to proper alignment, especially in high-stakes applications of AI, e.g., in healthcare domains, where misalignment can jeopardize the trustworthiness of the system and yield serious individual and societal harms. This work investigates the extent to which people's moral preferences change over time, and the impact of such changes on AI alignment. Our study is grounded in the kidney allocation domain, where we elicit responses to pairwise comparisons of hypothetical kidney transplant patients from over 400 participants across 3-5 sessions. We find that, on average, participants change their response to the same scenario presented at different times around 6-20% of the time (exhibiting "response instability"). Additionally, we observe significant shifts in several participants' retrofitted decision-making models over time (capturing "model instability"). The predictive performance of simple AI models decreases as a function of both response and model instability. Moreover, predictive performance diminishes over time, highlighting the importance of accounting for temporal changes in preferences during training. These findings raise fundamental normative and technical challenges relevant to AI alignment, highlighting the need to better understand the object of alignment (what to align to) when user preferences change significantly over time.

cs.HC↗

arXiv2025

Modeling the Economic Impacts of AI Openness Regulation

Regulatory frameworks, such as the EU AI Act, encourage openness of general-purpose AI models by offering legal exemptions for "open-source" models. Despite this legislative attention on openness, the definition of open-source foundation models remains ambiguous. This paper models the strategic interactions among the creator of a general-purpose model (the generalist) and the entity that fine-tunes the general-purpose model to a specialized domain or task (the specialist), in response to regulatory requirements on model openness. We present a stylized model of the regulator's choice of an open-source definition to evaluate which AI openness standards will establish appropriate economic incentives for developers. Our results characterize market equilibria -- specifically, upstream model release decisions and downstream fine-tuning efforts -- under various openness regulations and present a range of effective regulatory penalties and open-source thresholds. Overall, we find the model's baseline performance determines when increasing the regulatory penalty vs. the open-source threshold will significantly alter the generalist's release strategy. Our model provides a theoretical foundation for AI governance decisions around openness and enables evaluation and refinement of practical open-source policies.

cs.GT↗

arXiv2025

Persona-Augmented Benchmarking: Evaluating LLMs Across Diverse Writing Styles

Current benchmarks for evaluating Large Language Models (LLMs) often do not exhibit enough writing style diversity, with many adhering primarily to standardized conventions. Such benchmarks do not fully capture the rich variety of communication patterns exhibited by humans. Thus, it is possible that LLMs, which are optimized on these benchmarks, may demonstrate brittle performance when faced with "non-standard" input. In this work, we test this hypothesis by rewriting evaluation prompts using persona-based LLM prompting, a low-cost method to emulate diverse writing styles. Our results show that, even with identical semantic content, variations in writing style and prompt formatting significantly impact the estimated performance of the LLM under evaluation. Notably, we identify distinct writing styles that consistently trigger either low or high performance across a range of models and tasks, irrespective of model family, size, and recency. Our work offers a scalable approach to augment existing benchmarks, improving the external validity of the assessments they provide for measuring LLM performance across linguistic variations.

cs.CL↗

arXiv2025

From Firewalls to Frontiers: AI Red-Teaming is a Domain-Specific Evolution of Cyber Red-Teaming

A red team simulates adversary attacks to help defenders find effective strategies to defend their systems in a real-world operational setting. As more enterprise systems adopt AI, red-teaming will need to evolve to address the unique vulnerabilities and risks posed by AI systems. We take the position that AI systems can be more effectively red-teamed if AI red-teaming is recognized as a domain-specific evolution of cyber red-teaming. Specifically, we argue that existing Cyber Red Teams who adopt this framing will be able to better evaluate systems with AI components by recognizing that AI poses new risks, has new failure modes to exploit, and often contains unpatchable bugs that re-prioritize disclosure and mitigation strategies. Similarly, adopting a cybersecurity framing will allow existing AI Red Teams to leverage a well-tested structure to emulate realistic adversaries, promote mutual accountability with formal rules of engagement, and provide a pattern to mature the tooling necessary for repeatable, scalable engagements. In these ways, the merging of AI and Cyber Red Teams will create a robust security ecosystem and best position the community to adapt to the rapidly changing threat landscape.

cs.LG↗

arXiv2025

Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment

Recent AI trends seek to align AI models to learned human-centric objectives, such as personal preferences, utility, or societal values. Using standard preference elicitation methods, researchers and practitioners build models of human decisions and judgments, to which AI models are aligned. However, standard elicitation methods often fail to capture the cognitive processes behind human decision making, such as heuristics or simplifying structured thought patterns. To address this failure, we take an axiomatic approach to learning cognitively faithful decision processes from pairwise comparisons. Building on the literature analyzing cognitive processes that shape human decision-making, we derive a model class in which features are first processed with learned rules, then aggregated via a fixed rule, such as the Bradley-Terry rule, to produce a decision. This structured processing of information ensures that such models are realistic and feasible candidates to represent underlying human decision-making processes. We demonstrate the efficacy of this modeling approach by learning interpretable models of human decision making in a kidney allocation task, and show that our proposed models match or surpass the accuracy of prior models of human pairwise decision-making.

cs.LG↗

arXiv2025

Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption

Growing excitement around deploying AI across various domains calls for a careful assessment of how human decision-makers interact with AI-powered systems. In particular, it is essential to understand when decision-makers voluntarily choose to consult AI tools, which we term decision-maker adoption. We interviewed experts across four domains -- medicine, law, journalism, and the public sector -- to explore current AI use cases and perceptions of adoption. From these interviews, we identify key factors that shape decision-maker adoption of AI tools: the decision-maker's background, perceptions of the AI, consequences for the decision-maker, and perceived implications for other stakeholders. We translate these factors into an AI adoption sheet to analyze how decision-makers approach adoption choices through comparative, cross-domain case studies, highlighting how our factors help explain inter-domain differences in adoption. Our findings offer practical guidance for supporting the responsible and context-aware deployment of AI by better accounting for the decision-maker's perspective.

cs.HC↗

arXiv2025

The "Who", "What", and "How" of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Tools

The implementation of responsible AI in an organization is inherently complex due to the involvement of multiple stakeholders, each with their unique set of goals and responsibilities across the entire AI lifecycle. These responsibilities are often ambiguously defined and assigned, leading to confusion, miscommunication, and inefficiencies. Even when responsibilities are clearly defined and assigned to specific roles, the corresponding AI actors lack effective tools to support their execution. Toward closing these gaps, we present a systematic review and comprehensive meta-analysis of the current state of responsible AI tools, focusing on their alignment with specific stakeholder roles and their responsibilities in various AI lifecycle stages. We categorize over 220 tools according to AI actors and stages they address. Our findings reveal significant imbalances across the stakeholder roles and lifecycle stages addressed. The vast majority of available tools have been created to support AI designers and developers specifically during data-centric and statistical modeling stages while neglecting other roles such as institutional leadership, deployers, end-users, and impacted communities, and stages such as value proposition and deployment. The uneven distribution we describe here highlights critical gaps that currently exist in responsible AI governance research and practice. Our analysis reveals that despite the myriad of frameworks and tools for responsible AI, it remains unclear \emph{who} within an organization and \emph{when} in the AI lifecycle a tool applies. Furthermore, existing tools are rarely validated, leaving critical gaps in their usability and effectiveness. These gaps provide a starting point for researchers and practitioners to create more effective and holistic approaches to responsible AI development and governance.

cs.CY↗

arXiv2025

Toward Valid Measurement Of (Un)fairness For Generative AI: A Proposal For Systematization Through The Lens Of Fair Equality of Chances

Disparities in the societal harms and impacts of Generative AI (GenAI) systems highlight the critical need for effective unfairness measurement approaches. While numerous benchmarks exist, designing valid measurements requires proper systematization of the unfairness construct. Yet this process is often neglected, resulting in metrics that may mischaracterize unfairness by overlooking contextual nuances, thereby compromising the validity of the resulting measurements. Building on established (un)fairness measurement frameworks for predictive AI, this paper focuses on assessing and improving the validity of the measurement task. By extending existing conceptual work in political philosophy, we propose a novel framework for evaluating GenAI unfairness measurement through the lens of the Fair Equality of Chances framework. Our framework decomposes unfairness into three core constituents: the harm/benefit resulting from the system outcomes, morally arbitrary factors that should not lead to inequality in the distribution of harm/benefit, and the morally decisive factors, which distinguish subsets that can justifiably receive different treatments. By examining fairness through this structured lens, we integrate diverse notions of (un)fairness while accounting for the contextual dynamics that shape GenAI outcomes. We analyze factors contributing to each component and the appropriate processes to systematize and measure each in turn. This work establishes a foundation for developing more valid (un)fairness measurements for GenAI systems.

cs.CY↗

arXiv2025

The Backfiring Effect of Weak AI Safety Regulation

Recent policy proposals aim to improve the safety of general-purpose AI, but there is little understanding of the efficacy of different regulatory approaches to AI safety. We present a strategic model that explores the interactions between safety regulation, the general-purpose AI creators, and domain specialists--those who adapt the technology for specific applications. Our analysis examines how different regulatory measures, targeting different parts of the AI development chain, affect the outcome of this game. In particular, we assume AI technology is characterized by two key attributes: safety and performance. The regulator first sets a minimum safety standard that applies to one or both players, with strict penalties for non-compliance. The general-purpose creator then invests in the technology, establishing its initial safety and performance levels. Next, domain specialists refine the AI for their specific use cases, updating the safety and performance levels and taking the product to market. The resulting revenue is then distributed between the specialist and generalist through a revenue-sharing parameter. Our analysis reveals two key insights: First, weak safety regulation imposed predominantly on domain specialists can backfire. While it might seem logical to regulate AI use cases, our analysis shows that weak regulations targeting domain specialists alone can unintentionally reduce safety. This effect persists across a wide range of settings. Second, in sharp contrast to the previous finding, we observe that stronger, well-placed regulation can in fact mutually benefit all players subjected to it. When regulators impose appropriate safety standards on both general-purpose AI creators and domain specialists, the regulation functions as a commitment device, leading to safety and performance gains, surpassing what is achieved under no regulation or regulating one player alone.

cs.GT↗

arXiv2025

Designing Algorithmic Delegates: The Role of Indistinguishability in Human-AI Handoff

As AI technologies improve, people are increasingly willing to delegate tasks to AI agents. In many cases, the human decision-maker chooses whether to delegate to an AI agent based on properties of the specific instance of the decision-making problem they are facing. Since humans typically lack full awareness of all the factors relevant to this choice for a given decision-making instance, they perform a kind of categorization by treating indistinguishable instances -- those that have the same observable features -- as the same. In this paper, we define the problem of designing the optimal algorithmic delegate in the presence of categories. This is an important dimension in the design of algorithms to work with humans, since we show that the optimal delegate can be an arbitrarily better teammate than the optimal standalone algorithmic agent. The solution to this optimal delegation problem is not obvious: we discover that this problem is fundamentally combinatorial, and illustrate the complex relationship between the optimal design and the properties of the decision-making task even in simple settings. Indeed, we show that finding the optimal delegate is computationally hard in general. However, we are able to find efficient algorithms for producing the optimal delegate in several broad cases of the problem, including when the optimal action may be decomposed into functions of features observed by the human and the algorithm. Finally, we run computational experiments to simulate a designer updating an algorithmic delegate over time to be optimized for when it is actually adopted by users, and show that while this process does not recover the optimal delegate in general, the resulting delegate often performs quite well.

cs.GT↗

arXiv2025

A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents

Due to its general-purpose nature, Generative AI is applied in an ever-growing set of domains and tasks, leading to an expanding set of risks of harm impacting people, communities, society, and the environment. These risks may arise due to failures during the design and development of the technology, as well as during its release, deployment, or downstream usages and appropriations of its outputs. In this paper, building on prior taxonomies of AI risks, harms, and failures, we construct a taxonomy specifically for Generative AI failures and map them to the harms they precipitate. Through a systematic analysis of 499 publicly reported incidents, we describe what harms are reported, how they arose, and who they impact. We report the prevalence of each type of harm, underlying failure mode, and harmed stakeholder, as well as their common co-occurrences. We find that most reported incidents are caused by use-related issues but bring harm to parties beyond the end user(s) of the Generative AI system at fault, and that the landscape of Generative AI harms is distinct from that of traditional AI. Our work offers actionable insights to policymakers, developers, and Generative AI users. In particular, we call for the prioritization of non-technical risk and harm mitigation strategies, including public disclosures and education and careful regulatory stances.

cs.CY↗

arXiv2025

Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needs

Most AI tools adopted by governments are not developed internally, but instead are acquired from third-party vendors in a process called public procurement. In this paper, we conduct the first empirical study of how United States cities' procurement practices shape critical decisions surrounding public sector AI. We conduct semi-structured interviews with 19 city employees who oversee AI procurement across 7 U.S. cities. We found that cities' legacy procurement practices, which are shaped by decades-old laws and norms, establish infrastructure that determines which AI is purchased, and which actors hold decision-making power over procured AI. We characterize the emerging actions cities have taken to adapt their purchasing practices to address algorithmic harms. From employees' reflections on real-world AI procurements, we identify three key challenges that motivate but are not fully addressed by existing AI procurement reform initiatives. Based on these findings, we discuss implications and opportunities for the FAccT community to support cities in foreseeing and preventing AI harms throughout the public procurement processes.

cs.CY↗