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.

通过借鉴澳大利亚两所大学的经验，本文提出了一个框架，以帮助研究机构促进和促使生成性人工智能的负责任使用。此框架目的在于将复杂的监管环境转化为基于原则的立场声明，并为培训、沟通、基础设施和流程变革提供指导。此论文强调了研究机构在这一领域采取行动的紧迫性，并建议了一个实用且可适应的框架。

研究中生成 AI 的负责任使用的大学框架

A University Framework for the Responsible use of Generative AI in  Research

Generative artificial intelligence tools like large language models are
rapidly transforming academic research and real world applications. However,
discussions on ethical guidelines for generative AI in science remain
fragmented, underscoring the urgent need for consensus based standards. This
paper offers an initial framework by developing analyses and mitigation
strategies across five key themes: understanding model limitations regarding
truthfulness and bias; respecting privacy, confidentiality, and copyright;
avoiding plagiarism and policy violations when incorporating model output;
ensuring applications provide overall benefit; and using AI transparently and
reproducibly. Common scenarios are outlined to demonstrate potential ethical
violations. We argue that global consensus coupled with professional training
and reasonable enforcement are critical to promoting the benefits of AI while
safeguarding research integrity.

发展分析和缓解策略，提供了一个初始框架来理解模型限制、尊重隐私、保密和版权、避免抄袭和政策违规、确保应用程序提供整体利益，并透明可重现地使用人工智能，以保护研究的完整性。

构建科学研究中生成型人工智能的道德准则

Building ethical guidelines for generative AI in scientific research

This article is concerned with setting up practical guardrails within the
research activities and environments of CSS. It aims to provide CSS scholars,
as well as policymakers and other stakeholders who apply CSS methods, with the
critical and constructive means needed to ensure that their practices are
ethical, trustworthy, and responsible. It begins by providing a taxonomy of the
ethical challenges faced by researchers in the field of CSS. These are
challenges related to (1) the treatment of research subjects, (2) the impacts
of CSS research on affected individuals and communities, (3) the quality of CSS
research and to its epistemological status, (4) research integrity, and (5)
research equity. Taking these challenges as a motivation for cultural
transformation, it then argues for the end-to-end incorporation of habits of
responsible research and innovation (RRI) into CSS practices, focusing on the
role that contextual considerations, anticipatory reflection, impact
assessment, public engagement, and justifiable and well-documented action
should play across the research lifecycle. In proposing the inclusion of habits
of RRI in CSS practices, the chapter lays out several practical steps needed
for ethical, trustworthy, and responsible CSS research activities. These
include stakeholder engagement processes, research impact assessments, data
lifecycle documentation, bias self-assessments, and transparent research
reporting protocols.

本文提出在 CSS（Comparative Survey Science）研究活动和环境中设定实用的限制，以确保 CSS 研究是道德、可信和负责任的，并指出在 CSS 实践中应当全方位地融入负责任的研究和创新（RRI）习惯。为了实现这些目标，本文提出了一些实际步骤，包括：利益相关者参与流程，研究影响评估，数据生命周期文档，偏见自我评估和透明的研究报告协议。