arXiv subjects

Steven Luo

Publications and source records attributed to Steven Luo.

4 recordsLinked to original sources

arXiv2026

Reimagining Open Source and Openness in AI: Co-Creating Responsible Technological Futures

Debates over open source and openness in artificial intelligence have intensified as policymakers, researchers, and practitioners grapple with how foundation models should be developed and governed to balance innovation, accountability, and public interest. However, there has been limited empirical work examining how diverse stakeholders collectively understand and negotiate responsible openness in AI, particularly through participatory processes that extend beyond industry-led definitions and frameworks. This paper presents findings from a multi-sectoral workshop grounded in futures thinking and participatory design methods. The workshop generated co-created visions of desirable futures and the role of AI, alongside a set of action pathways and a research roadmap focused on responsible open source and openness in AI. This paper makes three key contributions. First, it empirically documents the co-created visions, actions, and research priorities. Second, it identifies four core tensions that emerged as participants translated high-level aspirations into concrete actions, revealing conflicting interpretations of openness regarding its purpose (as an end or a means), its scope (expansion versus meaningful access), and its operation (mandatory versus conditional, sufficient versus dependent on governance and use). These tensions illustrate that responsible openness is not a singular technical solution, but a negotiated sociotechnical project shaped by values, positionalities, and priorities. Third, the paper advances methodological approaches in AI governance by demonstrating how participatory futures methods can surface plural visions, actions, and research priorities that extend beyond dominant, largely corporate, narratives, offering empirical insight into how openness, power, and accountability are negotiated in practice.

cs.CY↗

arXiv2026

Political Neutrality as Balanced Approval: A Large-Scale Human Evaluation of AI Responses

As AI systems increasingly shape political views, defining and evaluating AI political neutrality is an urgent problem. Here, we propose a new definition of AI political neutrality and design a large-scale user study to test it, releasing a new dataset PARETO with 7,434 participants and 208,152 evaluations of AI responses. Our definition follows a simple principle grounded in political theory: when asked about a controversial issue, an AI model should generate responses that maximize approval across groups with opposing viewpoints, while balancing approval between groups. This definition allows empirical testing of whether an AI response is "neutral" and generalizes to any political context without pre-supposing a single left-right axis of division. We construct a benchmark of controversial U.S. issues, with prompts sourced from politically charged questions on Reddit and responses from frontier AI models, and recruit human participants to rate AI responses. Across all 20 issues, we find that it is possible for AI responses to achieve high rates of approval on both sides, even as those sides disagree strongly with each other on the substance of the issues. We also find that default responses lean liberal for GPT, Gemini, Claude, and Llama, but not Grok, and that user prompts with political charges are harder to respond to than neutral prompts. This work introduces a rigorous definition and benchmark of AI political neutrality, and a dataset to measure progress toward it.

cs.CY↗

arXiv2026

We Need Strong Preconditions For Using Simulations In Policy

Simulations, and more recently LLM agent simulations, have been adopted as useful tools for policymakers to explore interventions, rehearse potential scenarios, and forecast outcomes. While LLM simulations have enormous potential, two critical challenges remain understudied: the dual-use potential of accurate models of individual or population-level human behavior and the difficulty of validating simulation outputs. In light of these limitations, we must define boundaries for both simulation developers and decision-makers to ensure responsible development and ethical use. We propose and discuss three preconditions for societal-scale LLM agent simulations: 1) do not treat simulations of marginalized populations as neutral technical outputs, 2) do not simulate populations without their participation, and 3) do not simulate without accountability. We believe that these guardrails, combined with our call for simulation development and deployment reports, will help build trust among policymakers while promoting responsible development and use of societal-scale LLM agent simulations for the public benefit.

cs.CY↗

arXiv2025

Narrative Scaffolding: A Narrative-First Framework for Data-Driven Sensemaking

When exploring data, analysts construct narratives about what the data means by asking questions, generating visualizations, reflecting on patterns, and revising their interpretations as new insights emerge. Yet existing analysis tools treat narrative as an afterthought, breaking the link between reasoning, reflection, and the evolving story from exploration. Consequently, analysts lose the ability to see how their reasoning evolves, making it harder to reflect systematically or build coherent explanations. To address this gap, we propose Narrative Scaffolding, a framework for narrative-driven exploration that positions narrative construction as the primary interface for exploration and reasoning. We implement this framework in a system that externalizes iterative reasoning through narrative-first entry, semantically aligned view generation, and reflection support via insight provenance and inquiry tracking. In a within-subject study N=20, we demonstrate that narrative scaffolding facilitates broader exploration, deeper reflection, and more defensible narratives. An evaluation with visualization literacy experts (N = 6) confirmed that the system produced outputs aligned with narrative intent and facilitated intentional exploration.

cs.HC↗