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Embedding Privacy in Computational Social Science and Artificial Intelligence Research

Jones, Keenan, Zahrah, Fatima, Nurse, Jason R. C. (2024) Embedding Privacy in Computational Social Science and Artificial Intelligence Research. In: International AAAI Conference on Web and Social Media (ICWSM) Workshop Proceedings. . (In press) (KAR id:106052)

Abstract

Privacy is a human right. It ensures that individuals are free to engage in discussions, participate in groups, and form relationships online or offline without fear of their data being inappropriately harvested, analyzed, or otherwise used to harm them. Preserving privacy has emerged as a critical factor in research, particularly in the computational social science (CSS), artificial intelligence (AI) and data science domains, given their reliance on individuals’ data for novel insights. The increasing use of advanced computational models stands to exacerbate privacy concerns because, if inappropriately used, they can quickly infringe privacy rights and lead to adverse effects for individuals - especially vulnerable groups - and society. We have already witnessed a host of privacy issues emerge with the advent of large language models (LLMs), such as ChatGPT, which further demonstrate the importance of embedding privacy from the start. This article contributes to the field by discussing the role of privacy and the issues that researchers working in CSS, AI, data science and related domains are likely to face. It then presents several key considerations for researchers to ensure participant privacy is best preserved in their research design, data collection and use, analysis, and dissemination of research results.

Item Type: Conference or workshop item (Paper)
Subjects: H Social Sciences
Q Science
T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
University-wide institutes > Institute of Cyber Security for Society
Depositing User: Jason Nurse
Date Deposited: 22 May 2024 15:35 UTC
Last Modified: 23 May 2024 12:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/106052 (The current URI for this page, for reference purposes)

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