A multi-dimensional hierarchical evaluation system for data quality in trustworthy AI

Recently, the widespread adoption of artificial intelligence (AI) has given rise to a significant trust crisis, stemming from the persistent emergence of issues in practical applications. As a crucial component of AI, data has a profound impact on the trustworthiness of AI. Nevertheless, researchers...

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Bibliographic Details
Published in:Journal of big data 2024-09, Vol.11 (1), p.136-26, Article 136
Main Authors: Zhang, Hui-Juan, Chen, Can-Can, Ran, Peng, Yang, Kai, Liu, Quan-Chao, Sun, Zhe-Yuan, Chen, Jia, Chen, Jia-Ke
Format: Article
Language:English
Subjects:
Artificial intelligence
Assessment dimension
Big Data
Communications Engineering
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Data quality
Database Management
Effectiveness
Information Storage and Retrieval
Mathematical Applications in Computer Science
Methodology
Multi-dimensional hierarchical evaluation system
Multidimensional methods
Networks
Quality assessment
Trustworthiness
Trustworthy AI
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Summary:Recently, the widespread adoption of artificial intelligence (AI) has given rise to a significant trust crisis, stemming from the persistent emergence of issues in practical applications. As a crucial component of AI, data has a profound impact on the trustworthiness of AI. Nevertheless, researchers have struggled with the challenge of rationally assessing data quality, primarily due to the scarcity of versatile and effective evaluation methods. To address this trouble, a multi-dimensional hierarchical evaluation system (MDHES) is proposed to estimate the data quality. Initially, multiple key dimensions are devised to evaluate specific data conditions separately by the calculation of individual scores. Then, the strengths and weaknesses among various dimensions can be provided a clearer understanding. Furthermore, a comprehensive evaluation method, incorporating a fuzzy evaluation model, is developed to synthetically evaluate the data quality. Then, this evaluation method can achieve a dynamic balance, and meanwhile achieve a harmonious integration of subjectivity and objectivity criteria to ensure a more precise assessment result. Finally, rigorous experiment verification and comparison in both benchmark problems and real-world applications demonstrate the effectiveness of the proposed MDHES, which can accurately assess data quality to provide a strong data support for the development of trustworthy AI.
ISSN:2196-1115
2196-1115
DOI:10.1186/s40537-024-00999-2