Addressing the potential social risk of self-reported data within a computational-intensive world

Pileggi, SF

Addressing the potential social risk of self-reported data within a computational-intensive world

Pileggi, SF

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: SN Social Sciences, 2025, 5, (7), pp. 99
Issue Date:: 2025-07-01

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Field	Value	Language
dc.contributor.author	Pileggi, SF
dc.date.accessioned	2025-08-04T01:00:28Z
dc.date.available	2025-08-04T01:00:28Z
dc.date.issued	2025-07-01
dc.identifier.citation	SN Social Sciences, 2025, 5, (7), pp. 99
dc.identifier.issn	2662-9283
dc.identifier.issn	2662-9283
dc.identifier.uri	http://hdl.handle.net/10453/189021
dc.description.abstract	This paper addresses the potential social risk associated with the capability to infer sensitive opinions from large self-reported data within a computational-intensive world, in which AI is pervasively and inherently adopted as part of the resulting socio-technical system. Such a social risk should be framed assuming a variety of socio-political contexts, including also non-democratic systems or, more in general, systems with significant lacks in terms of human rights. A simplified view of social risk is considered proportional to the sensitivity of the information and the prediction performance. The related computational experiments are conducted by applying Machine Learning techniques (Neural Networks) on a pre-existent case study based on a subset of the popular World Values Survey. Despite such a use case is not explicitly designed to maximise the prediction performance and is characterised by low dimensionality, the empirical results pointed out an overall interesting capability to infer potentially sensitive information. Additionally, the prediction accuracy resulted to be proportional to the likelihood of data to change along the time. Those results are discussed in context in the paper, looking holistically at the associated social risk, as well as at possible practical implications. In a continuously evolving context, characterised by fast advances of AI technology in contrast with a lack of systematic frameworks for reasoning about risk, uncertainty, and their potentially catastrophic consequences, this study focuses on computational experimentation and case studies to further stimulate the convergence of analysis frameworks and to nurture awareness from both a social and a user perspective.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	SN Social Sciences
dc.relation.isbasedon	10.1007/s43545-025-01129-2
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Addressing the potential social risk of self-reported data within a computational-intensive world
dc.type	Journal Article
utslib.citation.volume	5
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-08-04T01:00:27Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	5
utslib.citation.issue	7

Abstract:

This paper addresses the potential social risk associated with the capability to infer sensitive opinions from large self-reported data within a computational-intensive world, in which AI is pervasively and inherently adopted as part of the resulting socio-technical system. Such a social risk should be framed assuming a variety of socio-political contexts, including also non-democratic systems or, more in general, systems with significant lacks in terms of human rights. A simplified view of social risk is considered proportional to the sensitivity of the information and the prediction performance. The related computational experiments are conducted by applying Machine Learning techniques (Neural Networks) on a pre-existent case study based on a subset of the popular World Values Survey. Despite such a use case is not explicitly designed to maximise the prediction performance and is characterised by low dimensionality, the empirical results pointed out an overall interesting capability to infer potentially sensitive information. Additionally, the prediction accuracy resulted to be proportional to the likelihood of data to change along the time. Those results are discussed in context in the paper, looking holistically at the associated social risk, as well as at possible practical implications. In a continuously evolving context, characterised by fast advances of AI technology in contrast with a lack of systematic frameworks for reasoning about risk, uncertainty, and their potentially catastrophic consequences, this study focuses on computational experimentation and case studies to further stimulate the convergence of analysis frameworks and to nurture awareness from both a social and a user perspective.

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http://hdl.handle.net/10453/189021