arXiv subjects

Brian Ballsun-Stanton

Publications and source records attributed to Brian Ballsun-Stanton.

2 recordsLinked to original sources

arXiv2025

Ethical conundrums: Hacked data in the study of far-right violent extremism

Ethical conduct in digital research is full of grey areas. Disciplinary, institutional and individual norms and conventions developed to support research are challenged, often leaving scholars with a sense of unease or lack of clarity. The growing availability of hacked data is one area. Discussions and debates around the use of these datasets in research are extremely limited. Reviews of the history, culture, or morality of the act of hacking are topics that have attracted some scholarly attention. However, how to undertake research with this data is less examined and provides an opportunity for the generation of reflexive ethical practice. This article presents a case-study outlining the ethical debates that arose when considering the use of hacked data to examine online far-right violent extremism. It argues that under certain circumstances, researchers can do ethical research with hacked data. However, to do so we must proactively and continually engage deeply with ethical quandaries and dilemmas.

cs.CY↗

arXiv2024

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

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.

cs.CY↗