Neutrosophic TOPSIS-OWA framework for evaluating data protection strategies in judicial systems
This study presents a novel neutrosophic multi-criteria decision-making approach, combining the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Ordered Weighted Averaging (OWA) methods, to assess data protection strategies for procedural fairness in judicial systems. R...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ayandegan Institute of Higher Education,
2024-11-01
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| Series: | Journal of Fuzzy Extension and Applications |
| Subjects: | |
| Online Access: | https://www.journal-fea.com/article_209202_0d5fc09c29d0f4965c33a4141ce12e3b.pdf |
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| Summary: | This study presents a novel neutrosophic multi-criteria decision-making approach, combining the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Ordered Weighted Averaging (OWA) methods, to assess data protection strategies for procedural fairness in judicial systems. Recognizing the challenges posed by ambiguity and subjectivity in judicial data handling, the proposed neutrosophic TOPSIS-OWA framework effectively incorporates uncertainty into the evaluation process. The methodology integrates insights from an extensive literature review and expert panel consultations to identify and prioritize feasible data protection measures. Four primary alternatives were evaluated: a certification system, periodic compliance audits, mandatory training for judicial staff, and the establishment of an independent supervisory body. Results indicate that implementing a periodic audit system aligns most closely with the procedural fairness objectives, offering the best balance of data protection effectiveness, regulatory compliance, and stakeholder acceptance. This finding underscores the value of neutrosophic logic in complex decision-making contexts, particularly where nuanced judgments are essential. Future research could extend this framework to other domains where procedural fairness and data integrity are critical. |
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| ISSN: | 2783-1442 2717-3453 |