Evaluating the Impact of Explainable AI Models on Transparency in Scientific Research Applications
Keywords:
Explainable AI, Scientific Transparency, Research Ethics, Interpretability, Reproducibility, AI in Science, Trust in AIAbstract
Purpose: This paper investigates how the adoption of Explainable Artificial Intelligence (XAI) models influences transparency within scientific research workflows, particularly in domains where AI systems play a critical role in data analysis, hypothesis generation, and decision-making processes.
Methodology: A mixed-method approach was utilized, combining systematic literature review, case analysis of selected research projects using XAI tools, and survey data from 150 academic researchers across multiple disciplines. Both qualitative and quantitative data were synthesized to assess perceptions of transparency and interpretability improvements attributable to XAI models.
Findings: Results indicate a significant increase in perceived research transparency, reproducibility, and trust in AI-driven results when explainable models are employed. Researchers emphasized the value of post-hoc interpretability tools and inherently interpretable models in facilitating peer review and collaborative validation.
Practical implications: The study provides empirical support for the integration of XAI systems in research workflows, especially where regulatory compliance and reproducibility are essential. These findings are particularly relevant for funding bodies and institutions prioritizing ethical AI usage in research settings.
Originality: This paper contributes a novel synthesis of XAI’s role in fostering epistemic transparency in science, offering both practical insights and a conceptual framework for evaluating AI interpretability in knowledge-producing contexts.
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Copyright (c) 2026 Marcelo K. Henrique (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
