arXiv subjects

David Gray Widder

Publications and source records attributed to David Gray Widder.

14 recordsLinked to original sources

arXiv2026

Open at the Edge, Captured at the Center: llama.cpp and the Political Economy of Local AI Inference

Critical scholarship on open AI has focused on model releases and cloud ecosystems, leaving the local inference infrastructure that makes open-weight models runnable on user-owned devices largely unexamined. We address this gap through a mixed-methods analysis of llama$.$cpp, combining 7,681 merged pull requests from March 2023 through March 2026 with repository discussions, corporate statements, and contributor blogs. We show that local inference broadens participation at execution while relocating capture into the infrastructure that makes execution possible. Through hardware backends, model integration labor, and Hugging Face's February 2026 absorption of the project, we document how control shifts to hardware vendors, model distributors, and core maintainers while model owners and individual contributors bear the cost of making models runnable. These dynamics suggest that preserving openness outside the cloud requires attention to the infrastructure that makes models runnable, not just to the models themselves. This calls for policy mechanisms--analysis of format dependencies and vendor influence, model compatibility requirements, and sustained public funding for inference tooling--that extend beyond model release conditions to the infrastructure layer.

cs.CY↗

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↗

arXiv2024

Basic Research, Lethal Effects: Military AI Research Funding as Enlistment

In the context of unprecedented U.S. Department of Defense (DoD) budgets, this paper examines the recent history of DoD funding for academic research in algorithmically based warfighting. We draw from a corpus of DoD grant solicitations from 2007 to 2023, focusing on those addressed to researchers in the field of artificial intelligence (AI). Considering the implications of DoD funding for academic research, the paper proceeds through three analytic sections. In the first, we offer a critical examination of the distinction between basic and applied research, showing how funding calls framed as basic research nonetheless enlist researchers in a war fighting agenda. In the second, we offer a diachronic analysis of the corpus, showing how a 'one small problem' caveat, in which affirmation of progress in military technologies is qualified by acknowledgement of outstanding problems, becomes justification for additional investments in research. We close with an analysis of DoD aspirations based on a subset of Defense Advanced Research Projects Agency (DARPA) grant solicitations for the use of AI in battlefield applications. Taken together, we argue that grant solicitations work as a vehicle for the mutual enlistment of DoD funding agencies and the academic AI research community in setting research agendas. The trope of basic research in this context offers shelter from significant moral questions that military applications of one's research would raise, by obscuring the connections that implicate researchers in U.S. militarism.

cs.CY↗

arXiv2024

The Ethics of AI Value Chains

Researchers, practitioners, and policymakers with an interest in AI ethics need more integrative approaches for studying and intervening in AI systems across many contexts and scales of activity. This paper presents AI value chains as an integrative concept that satisfies that need. To more clearly theorize AI value chains and conceptually distinguish them from supply chains, we review theories of value chains and AI value chains from the strategic management, service science, economic geography, industry, government, and applied research literature. We then conduct an integrative review of a sample of 67 sources that cover the ethical concerns implicated in AI value chains. Building upon the findings of our integrative review, we recommend three future directions that researchers, practitioners, and policymakers can take to advance more ethical practices across AI value chains. We urge AI ethics researchers and practitioners to move toward value chain perspectives that situate actors in context, account for the many types of resources involved in co-creating AI systems, and integrate a wider range of ethical concerns across contexts and scales.

cs.CY↗

arXiv2024

Watching the Generative AI Hype Bubble Deflate

Only a few short months ago, Generative AI was sold to us as inevitable by the leadership of AI companies, those who partnered with them, and venture capitalists. As certain elements of the media promoted and amplified these claims, public discourse online buzzed with what each new beta release could be made to do with a few simple prompts. As AI became a viral sensation, every business tried to become an AI business. Some businesses added "AI" to their names to juice their stock prices, and companies talking about "AI" on their earnings calls saw similar increases. While the Generative AI hype bubble is now slowly deflating, its harmful effects will last.

cs.CY↗

arXiv2024

From Stem to Stern: Contestability Along AI Value Chains

This workshop will grow and consolidate a community of interdisciplinary CSCW researchers focusing on the topic of contestable AI. As an outcome of the workshop, we will synthesize the most pressing opportunities and challenges for contestability along AI value chains in the form of a research roadmap. This roadmap will help shape and inspire imminent work in this field. Considering the length and depth of AI value chains, it will especially spur discussions around the contestability of AI systems along various sites of such chains. The workshop will serve as a platform for dialogue and demonstrations of concrete, successful, and unsuccessful examples of AI systems that (could or should) have been contested, to identify requirements, obstacles, and opportunities for designing and deploying contestable AI in various contexts. This will be held primarily as an in-person workshop, with some hybrid accommodation. The day will consist of individual presentations and group activities to stimulate ideation and inspire broad reflections on the field of contestable AI. Our aim is to facilitate interdisciplinary dialogue by bringing together researchers, practitioners, and stakeholders to foster the design and deployment of contestable AI.

cs.AI↗

arXiv2024

Epistemic Power in AI Ethics Labor: Legitimizing Located Complaints

What counts as legitimate AI ethics labor, and consequently, what are the epistemic terms on which AI ethics claims are rendered legitimate? Based on 75 interviews with technologists including researchers, developers, open source contributors, and activists, this paper explores the various epistemic bases from which AI ethics is discussed and practiced. In the context of outside attacks on AI ethics as an impediment to "progress," I show how some AI ethics practices have reached toward authority from automation and quantification, and achieved some legitimacy as a result, while those based on richly embodied and situated lived experience have not. This paper draws together the work of feminist Anthropology and Science and Technology Studies scholars Diana Forsythe and Lucy Suchman with the works of postcolonial feminist theorist Sara Ahmed and Black feminist theorist Kristie Dotson to examine the implications of dominant AI ethics practices. By entrenching the epistemic power of quantification, dominant AI ethics practices -- employing Model Cards and similar interventions -- risk legitimizing AI ethics as a project in equal and opposite measure to which they marginalize embodied lived experience as a legitimate part of the same project. In response, I propose humble technical practices: quantified or technical practices which specifically seek to make their epistemic limits clear in order to flatten hierarchies of epistemic power.

cs.CY↗

arXiv2024

Thinking Upstream: Ethics and Policy Opportunities in AI Supply Chains

After children were pictured sewing its running shoes in the early 1990s, Nike at first disavowed the "working conditions in its suppliers' factories", before public pressure led them to take responsibility for ethics in their upstream supply chain. In 2023, OpenAI responded to criticism that Kenyan workers were paid less than $2 per hour to filter traumatic content from its ChatGPT model by stating in part that it had outsourced the work to a subcontractor, who managed workers' payment and mental health concerns. In this position paper, we argue that policy interventions for AI Ethics must consider AI as a supply chain problem, given how the political economy and intra-firm relations structure AI production, in particular examining opportunities upstream.

cs.CY↗

arXiv2024

Power and Play: Investigating "License to Critique" in Teams' AI Ethics Discussions

Past work has sought to design AI ethics interventions--such as checklists or toolkits--to help practitioners design more ethical AI systems. However, other work demonstrates how these interventions may instead serve to limit critique to that addressed within the intervention, while rendering broader concerns illegitimate. In this paper, drawing on work examining how standards enact discursive closure and how power relations affect whether and how people raise critique, we recruit three corporate teams, and one activist team, each with prior context working with one another, to play a game designed to trigger broad discussion around AI ethics. We use this as a point of contrast to trigger reflection on their teams' past discussions, examining factors which may affect their "license to critique" in AI ethics discussions. We then report on how particular affordances of this game may influence discussion, and find that the hypothetical context created in the game is unlikely to be a viable mechanism for real world change. We discuss how power dynamics within a group and notions of "scope" affect whether people may be willing to raise critique in AI ethics discussions, and discuss our finding that games are unlikely to enable direct changes to products or practice, but may be more likely to allow members to find critically-aligned allies for future collective action.

cs.CY↗

arXiv2024

What is a "bug"? On subjectivity, epistemic power, and implications for software research

Considerable effort in software research and practice is spent on bugs. Finding, reporting, tracking, triaging, attempting to fix them automatically, detecting "bug smells" -these comprise a substantial portion of large projects' time and development cost, and are of significant interest to researchers in Software Engineering, Programming Languages, and beyond. But, what is a bug, exactly? While segmentation faults rarely spark joy, most bugs are not so clear cut. Per the Oxford English Dictionary, the word "bug" has been a colloquialism for an engineering "defect" at least since the 1870s. Most modern software-oriented definitions speak to a disconnect between what a developer intended and what a program actually does. Formal verification, from its inception, has developed means to identify deviations from a formal specification, expected to more or less fully encode desired behavior. However, software is rarely accompanied by full and formal specifications, and this intention is instead treated as implicit or partially-documented at best. The International Software Testing Qualifications board writes: "A human being can make an error (mistake), which produces a defect (fault, bug) in the program code, or in a document. If a defect in code is executed, the system may fail to do what it should do (or do something it shouldn't), causing a failure. Defects may result in failures, but not all [do]". Most sources forsake this precision. The influential paper "Finding bugs is easy" begins by saying "bug patterns are code idioms that are often errors"-with no particular elaboration. Other work relies on imperfect practical proxies for specifications. For example, in automatic program repair research, a bug corresponds to a failing test case: when the test passes, the bug is considered fixed. However, when we interrogate fairly straightforward definitions, they start to break down...

cs.SE↗

arXiv2023

To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing

NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception. In this work, we seek to understand how to shape our future by better understanding our past. We study factors that shape NLP as a field, including culture, incentives, and infrastructure by conducting long-form interviews with 26 NLP researchers of varying seniority, research area, institution, and social identity. Our interviewees identify cyclical patterns in the field, as well as new shifts without historical parallel, including changes in benchmark culture and software infrastructure. We complement this discussion with quantitative analysis of citation, authorship, and language use in the ACL Anthology over time. We conclude by discussing shared visions, concerns, and hopes for the future of NLP. We hope that this study of our field's past and present can prompt informed discussion of our community's implicit norms and more deliberate action to consciously shape the future.

cs.CL↗

arXiv2023

Dislocated Accountabilities in the AI Supply Chain: Modularity and Developers' Notions of Responsibility

Responsible artificial intelligence guidelines ask engineers to consider how their systems might harm. However, contemporary artificial intelligence systems are built by composing many preexisting software modules that pass through many hands before becoming a finished product or service. How does this shape responsible artificial intelligence practice? In interviews with 27 artificial intelligence engineers across industry, open source, and academia, our participants often did not see the questions posed in responsible artificial intelligence guidelines to be within their agency, capability, or responsibility to address. We use Suchman's "located accountability" to show how responsible artificial intelligence labor is currently organized and to explore how it could be done differently. We identify cross-cutting social logics, like modularizability, scale, reputation, and customer orientation, that organize which responsible artificial intelligence actions do take place and which are relegated to low status staff or believed to be the work of the next or previous person in the imagined "supply chain." We argue that current responsible artificial intelligence interventions, like ethics checklists and guidelines that assume panoptical knowledge and control over systems, could be improved by taking a located accountability approach, recognizing where relations and obligations might intertwine inside and outside of this supply chain.

cs.CY↗

arXiv2022

Gender and Robots: A Literature Review

Here, I ask what we can learn about how gender affects how people engage with robots. I review 46 empirical studies of social robots, published 2018 or earlier, which report on the gender of their participants or the perceived or intended gender of the robot, or both, and perform some analysis with respect to either participant or robot gender. From these studies, I find that robots are by default perceived as male, that robots absorb human gender stereotypes, and that men tend to engage with robots more than women. I highlight open questions about how such gender effects may be different in younger participants, and whether one should seek to match the gender of the robot to the gender of the participant to ensure positive interaction outcomes. I conclude by suggesting that future research should: include gender diverse participant pools, include non-binary participants, rely on self-identification for discerning gender rather than researcher perception, control for known covariates of gender, test for different study outcomes with respect to gender, and test whether the robot used was perceived as gendered by participants. I include an appendix with a narrative summary of gender-relevant findings from each of the 46 papers to aid in future literature reviews.

cs.RO↗

arXiv2018

Debugging Framework Applications: Benefits and Challenges

Aspects of frameworks, such as inversion of control and the structure of framework applications, require developers to adjust their debugging strategies as compared to debugging sequential programs. However, the benefits and challenges of framework debugging are not fully understood, and gaining this knowledge could provide guidance in debugging strategies and framework tool design. To gain insight into the process developers use to fix problems in framework applications, we performed two human studies investigating how developers fix applications that use a framework API incorrectly. These studies focused on the Android Fragment class and the ROS framework. We analyzed the results of the studies using a mixed-methods approach, consisting of techniques from grounded theory, qualitative content analysis, and case studies. From our analysis, we produced a theory of the benefits and challenges of framework debugging. This theory states that developers find inversion of control challenging when debugging but find the structure of framework applications helpful. This theory could be used to guide strategies for debugging framework applications and framework tool designs.

cs.SE↗