A University Framework for the Responsible use of Generative AI in Research
- URL: http://arxiv.org/abs/2404.19244v1
- Date: Tue, 30 Apr 2024 04:00:15 GMT
- Title: A University Framework for the Responsible use of Generative AI in Research
- Authors: Shannon Smith, Melissa Tate, Keri Freeman, Anne Walsh, Brian Ballsun-Stanton, Mark Hooper, Murray Lane,
- Abstract summary: Generative Artificial Intelligence (generative AI) poses both opportunities and risks for the integrity of research.
We propose a framework to help institutions promote and facilitate the responsible use of generative AI.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative Artificial Intelligence (generative AI) poses both opportunities and risks for the integrity of research. Universities must guide researchers in using generative AI responsibly, and in navigating a complex regulatory landscape subject to rapid change. By drawing on the experiences of two Australian universities, we propose a framework to help institutions promote and facilitate the responsible use of generative AI. We provide guidance to help distil the diverse regulatory environment into a principles-based position statement. Further, we explain how a position statement can then serve as a foundation for initiatives in training, communications, infrastructure, and process change. Despite the growing body of literature about AI's impact on academic integrity for undergraduate students, there has been comparatively little attention on the impacts of generative AI for research integrity, and the vital role of institutions in helping to address those challenges. This paper underscores the urgency for research institutions to take action in this area and suggests a practical and adaptable framework for so doing.
Related papers
- Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act)
Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence.
As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
arXiv Detail & Related papers (2024-10-10T17:38:38Z) - Towards an Operational Responsible AI Framework for Learning Analytics in Higher Education [0.2796197251957245]
We map 11 established Responsible AI frameworks, including those by leading tech companies, to the context of LA in Higher Education.
This led to the identification of seven key principles such as transparency, fairness, and accountability.
We present a novel framework that offers practical guidance to HE institutions and is designed to evolve with community input.
arXiv Detail & Related papers (2024-10-08T08:55:24Z) - AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities [14.26619701452836]
Generative AI has drawn significant attention from stakeholders in higher education.
It simultaneously poses challenges to academic integrity and leads to ethical issues.
Leading universities have already published guidelines on Generative AI.
This study focuses on strategies for responsible AI governance as demonstrated in these guidelines.
arXiv Detail & Related papers (2024-09-03T16:06:45Z) - Responsible Artificial Intelligence: A Structured Literature Review [0.0]
The EU has recently issued several publications emphasizing the necessity of trust in AI.
This highlights the urgent need for international regulation.
This paper introduces a comprehensive and, to our knowledge, the first unified definition of responsible AI.
arXiv Detail & Related papers (2024-03-11T17:01:13Z) - 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) - Responsible AI Governance: A Systematic Literature Review [8.318630741859113]
This paper aims to examine the existing literature on AI Governance.
The focus of this study is to analyse the literature to answer key questions: WHO is accountable for AI systems' governance, WHAT elements are being governed, WHEN governance occurs within the AI development life cycle, and HOW it is executed through various mechanisms like frameworks, tools, standards, policies, or models.
The findings of this study provides a foundational basis for future research and development of comprehensive governance models that align with RAI principles.
arXiv Detail & Related papers (2023-12-18T05:22:36Z) - Investigating Responsible AI for Scientific Research: An Empirical Study [4.597781832707524]
The push for Responsible AI (RAI) in such institutions underscores the increasing emphasis on integrating ethical considerations within AI design and development.
This paper aims to assess the awareness and preparedness regarding the ethical risks inherent in AI design and development.
Our results have revealed certain knowledge gaps concerning ethical, responsible, and inclusive AI, with limitations in awareness of the available AI ethics frameworks.
arXiv Detail & Related papers (2023-12-15T06:40:27Z) - 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) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable
Claims [59.64274607533249]
AI developers need to make verifiable claims to which they can be held accountable.
This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems.
We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
arXiv Detail & Related papers (2020-04-15T17:15:35Z)
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.