Export Citation
Export Citation
Ong, C 
ORCID: 0000-0002-4470-390X and Kayas, OG ORCID: 0000-0003-4541-8171
(2026)
GenAI-Enabled Dark Data Circularity for Sustainable Intelligence.
In:
Pacific Asia Conference on Information Systems (PACIS) 2026 Proceedings
.
(Pacific-Asia Conference on Information Systems, 4th July- 8th July 2026, Jakarta, Indonesia).
Preview |
Text
Paper.pdf - Accepted Version Download (336kB) | Preview |
Abstract
Organisations store large amounts of dark data: records that are retained but rarely used and whose ownership, value or deletion route is unclear. This creates avoidable storage, energy, compliance and security burdens. This short paper develops a GenAI-Dark Data Circularity (GDDC) framework to explain when generative artificial intelligence (GenAI) can help organisations reduce, reuse and recycle such data without creating new risks. Drawing on Green information systems (Green IS), data sustainability, data governance and digital resilience research, we define the core constructs, outline three mechanisms—sorting records, synthesising knowledge and reconfiguring data for reuse and propose six provisional propositions. We argue that GenAI creates sustainable intelligence only when it is governed through clear deletion rights, traceable sources, human validation and compute controls. The paper contributes a theory of responsible data circularity for converting neglected archives into auditable knowledge while managing environmental and resilience trade-offs.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | H Social Sciences > HF Commerce > HF5001 Business Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Liverpool Business School |
| Publisher: | AIS eLibrary (Association for Information Systems) |
| Related URLs: | |
| Date of acceptance: | 1 May 2026 |
| Date of first compliant Open Access: | 21 September 2026 |
| Date Deposited: | 05 May 2026 12:52 |
| Last Modified: | 21 Sep 2026 11:19 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/28514 |
 |
View Item |