Reimagining Open Source and Openness in AI: Co-Creating Responsible Technological Futures
- URL: http://arxiv.org/abs/2606.07764v1
- Date: Fri, 05 Jun 2026 18:23:39 GMT
- Title: Reimagining Open Source and Openness in AI: Co-Creating Responsible Technological Futures
- Abstract summary: This paper presents findings from a multi-sectoral workshop grounded in futures thinking and participatory design methods.<n>It empirically documents the co-created visions, actions, and research priorities.<n>It identifies four core tensions that emerged as participants translated high-level aspirations into concrete actions.
- Score: 7.279642720684369
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- AI Deception: Risks, Dynamics, and Controls [153.71048309527225]
This project provides a comprehensive and up-to-date overview of the AI deception field.<n>We identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception.<n>We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
arXiv Detail & Related papers (2025-11-27T16:56:04Z) - Toward a Public and Secure Generative AI: A Comparative Analysis of Open and Closed LLMs [0.0]
This study aims to critically evaluate and compare the characteristics, opportunities, and challenges of open and closed generative AI models.<n>The proposed framework outlines key dimensions, openness, public governance, and security, as essential pillars for shaping the future of trustworthy and inclusive Gen AI.
arXiv Detail & Related papers (2025-05-15T15:21:09Z) - Opening the Scope of Openness in AI [1.2894076331861155]
The concept of openness in AI has so far been heavily inspired by the definition and community practice of open source software.<n>We argue that considering the fundamental scope of openness in different disciplines will broaden discussions.<n>Our work contributes to the recent efforts in framing openness in AI by reflecting principles and practices of openness beyond open source software.
arXiv Detail & Related papers (2025-05-09T23:16:44Z) - Open Problems in Mechanistic Interpretability [61.44773053835185]
Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities.<n>Despite recent progress toward these goals, there are many open problems in the field that require solutions.
arXiv Detail & Related papers (2025-01-27T20:57:18Z) - Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence [18.130525337375985]
This paper presents a framework for grappling with openness across the AI stack.
It summarizes previous work on this topic, analyzes the various potential reasons to pursue openness.
It outlines how openness varies in different parts of the AI stack, both at the model and at the system level.
arXiv Detail & Related papers (2024-05-17T20:35:39Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - A Survey on Knowledge Distillation of Large Language Models [99.11900233108487]
Knowledge Distillation (KD) emerges as a pivotal methodology for transferring advanced capabilities to open-source models.
This paper presents a comprehensive survey of KD's role within the realm of Large Language Models (LLMs)
arXiv Detail & Related papers (2024-02-20T16:17:37Z) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z) - The Participatory Turn in AI Design: Theoretical Foundations and the
Current State of Practice [64.29355073494125]
This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation.
We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
arXiv Detail & Related papers (2023-10-02T05:30:42Z) - The Equitable AI Research Roundtable (EARR): Towards Community-Based
Decision Making in Responsible AI Development [4.1986677342209004]
The paper reports on our initial evaluation of The Equitable AI Research Roundtable.
EARR was created in collaboration among a large tech firm, nonprofits, NGO research institutions, and universities.
We outline three principles in practice of how EARR has operated thus far that are especially relevant to the concerns of the FAccT community.
arXiv Detail & Related papers (2023-03-14T18:57:20Z) - Power to the People? Opportunities and Challenges for Participatory AI [9.504176941117493]
Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining momentum.
This paper reviews participatory approaches as situated in historical contexts as well as participatory methods and practices within the AI and ML pipeline.
We argue that as participatory AI/ML becomes in vogue, a contextual and nuanced understanding of the term as well as consideration of who the primary beneficiaries of participatory activities ought to be constitute crucial factors to realizing the benefits and opportunities that participation brings.
arXiv Detail & Related papers (2022-09-15T19:20:13Z) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.