A Vision for the Future of an AI-Integrated Research Ecosystem
- URL: http://arxiv.org/abs/2608.05438v1
- Date: Wed, 05 Aug 2026 22:16:41 GMT
- Title: A Vision for the Future of an AI-Integrated Research Ecosystem
- Abstract summary: We argue that a focus on authorship and disclosure, although necessary, risks obscuring and ballooning a set of entrenched problems and strains within publication systems.<n>We argue for a shift from policing GenAI to building the infrastructure of provenance, calibration, and accountability that would make trustworthy scholarship the default.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative AI has infiltrated every stage of the research lifecycle: how scholarship is conducted, written, published, and reviewed. Recent policy responses, such as ACM's authorship policy, address an immediate concern about responsible and transparent disclosure of AI use. We argue that a focus on authorship and disclosure, although necessary, risks obscuring and ballooning a set of entrenched problems and strains within publication systems. The central question is not about how papers and other research artifacts should incorporate AI, but how scientific communication itself should evolve when all relevant parties (authors, reviewers, readers) may rely on AI assistance. We draw on our experience within these and other roles to illustrate two contrasting but feasible visions of 2036 with four entwined questions, namely about the purpose of papers as artifacts, reviews, human reviewers, and the incentives that bind all of them. We argue for a shift from policing GenAI and other disruptive technologies to building the infrastructure of provenance, calibration, and accountability that would make trustworthy scholarship the default. We conclude with three grand challenges and invite the community to a broader conversation and research pathways.
Related papers
- Bridging the Gap in the Responsible AI Divides [1.5120428871017098]
Tensions between AI Safety (AIS) and AI Ethics (AIE) have increasingly surfaced in AI governance and public debates about AI.<n>We introduce a model that categorizes four modes of engagement with the tensions: radical confrontation, disengagement, compartmentalized coexistence, and critical bridging.
arXiv Detail & Related papers (2026-03-15T17:19:30Z) - Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
arXiv Detail & Related papers (2025-08-12T09:42:56Z) - Advancing Science- and Evidence-based AI Policy [163.43609502905707]
This paper tackles the problem of how to optimize the relationship between evidence and policy to address the opportunities and challenges of AI.<n>An increasing number of efforts address this problem by often either (i) contributing research into the risks of AI and their effective mitigation or (ii) advocating for policy to address these risks.
arXiv Detail & Related papers (2025-08-02T23:20:58Z) - Societal AI Research Has Become Less Interdisciplinary [3.9599054392856483]
This study analyzes over 100,000 AI-related papers published on ArXiv between 2014 and 2024.<n>Computer science-only teams now account for a growing share of the field's overall societal output.<n>These findings challenge common assumptions about the drivers of societal AI.
arXiv Detail & Related papers (2025-06-10T12:34:53Z) - The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
arXiv Detail & Related papers (2025-06-09T18:37:14Z) - Position: We Need Responsible, Application-Driven (RAD) AI Research [5.472297350890938]
This position paper argues that achieving meaningful scientific and societal advances with artificial intelligence (AI) requires a responsible, application-driven approach (RAD) to AI research.<n>We present a vision for the future of application-driven AI research to unlock new value through technically feasible methods.
arXiv Detail & Related papers (2025-05-07T03:43:52Z) - AI Research is not Magic, it has to be Reproducible and Responsible: Challenges in the AI field from the Perspective of its PhD Students [1.1922075410173798]
We surveyed 28 AI doctoral candidates from 13 European countries.
Challenges underscore the findability and quality of AI resources such as datasets, models, and experiments.
There is need for immediate adoption of responsible and reproducible AI research practices.
arXiv Detail & Related papers (2024-08-13T12:19:02Z) - Ten Hard Problems in Artificial Intelligence We Must Get Right [72.99597122935903]
We explore the AI2050 "hard problems" that block the promise of AI and cause AI risks.
For each problem, we outline the area, identify significant recent work, and suggest ways forward.
arXiv Detail & Related papers (2024-02-06T23:16:41Z) - Responsible AI Considerations in Text Summarization Research: A Review
of Current Practices [89.85174013619883]
We focus on text summarization, a common NLP task largely overlooked by the responsible AI community.
We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022.
We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals.
arXiv Detail & Related papers (2023-11-18T15:35:36Z) - A Systematic Literature Review of Human-Centered, Ethical, and
Responsible AI [12.456385305888341]
We review and analyze 164 research papers from leading conferences in ethical, social, and human factors of AI.
We find that the current emphasis on governance and fairness in AI research may not adequately address the potential unforeseen and unknown implications of AI.
arXiv Detail & Related papers (2023-02-10T14:47:33Z) - 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.