Responsible and ethical use of generative AI in research

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Document Type

Lecture

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

CIT Disciplines

Library science

Disciplines

Library and Information Science

Publication Details

Presentation delivered by Prof. Nick Baker with accompanying slides at MTU event "Research Integrity & Responsible Use of GenAI".

Abstract

The integration of generative artificial intelligence (AI) into institutional and personal technologies and the emergence of specialised AI agents targeted at various parts of the research process is fundamentally changing research practices across all academic disciplines. While discussions thus far have largely centred on preserving research integrity, generative AI's transformative potential in research demands broader consideration. AI assistants are already enabling novel approaches to data analysis, accelerating discovery processes, supporting ideation, expanding research capacity, identifying cross-disciplinary connections, and providing accessibility supports for researchers with disabilities or neurodivergence. They are also already performing at or above human level in many domains, which raises questions about the role humans will play in research in the future. Research Ethics Boards are already facing the widespread use of AI in all aspects of the research enterprise, but are they prepared for independent AI agents leading research projects, or as the subject of proposals?

The use of generative AI in research raises similar concerns to its applications in other areas, including output accuracy and bias, data security, privacy, and the perpetuation of colonial research practices and norms. Additionally, recent partnerships between commercial AI companies and academic publishers raise ethical questions about the use of academic work for model training without author consent. These concerns have long existed in the open publishing and open science communities, aligning with the larger concern about commodification of knowledge by extractive technologies and the role of open science.

While acknowledging those concerns, organisations including UNESCO and the Open Data Institute emphasise that open science and open data are crucial for ethical and equitable AI development, potentially serving as a counterweight to online misinformation and the biases that exist in other training datasets. While work remains to make open datasets AI-ready, generative AI could ultimately make these datasets more accessible and interpretable.

This presentation will explore some of the practical applications of AI for researchers, consider how to use these responsibly and ethically, and discuss some of the implications for research ethics.

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