Year: 2025
Type: article
Abstract: This paper introduces a conceptual mathematical framework for evaluating trustworthiness in AI-based big data analysis, emphasizing the critical role trust plays in the adoption and effectiveness of AI systems. The proposed model leverages the trustworthy AI-based big data management (TAI-BDM) reference model, focusing on three fundamental dimensions of trustworthiness: validity, capability, and reproducibility. It formalizes these dimensions mathematically, embedding them into a unified three-dimensional state space that enables the quantification of trustworthiness throughout AI-supported data exploration processes. It further defines update functions capturing the impact of individual data manipulation steps on overall system trustworthiness. Additionally, the paper proposes a scalar metric to integrate and evaluate these dimensions collectively, providing a practical measure of the overall trustworthiness of the system. The paper presents a starting point for modeling trustworthiness in TAI-BDM applications.
Source: Information
DOI: 10.3390/info16060455
Institution: FernUniversität in Hagen
Language: English
Topic: Access Control and Trust
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4410869640