Explanation-Driven Interventions for Artificial Intelligence Model Customization: Empowering End-Users to Tailor Black-Box AI in Rhinocytology

• URL: http://arxiv.org/abs/2504.04833v2
• Date: Mon, 14 Apr 2025 16:21:20 GMT
• Title: Explanation-Driven Interventions for Artificial Intelligence Model Customization: Empowering End-Users to Tailor Black-Box AI in Rhinocytology
• Authors: Andrea Esposito, Miriana Calvano, Antonio Curci, Francesco Greco, Rosa Lanzilotti, Antonio Piccinno,
• Abstract summary: This article presents a novel End-User Development (EUD) approach for black-box AI models.<n>The proposed method advances Human-Centered AI (HCAI), promoting a symbiotic relationship between humans and adaptive, user-tailored AI systems.
• Score: 3.4705962607086973
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of Artificial Intelligence (AI) in modern society is transforming how individuals perform tasks. In high-risk domains, ensuring human control over AI systems remains a key design challenge. This article presents a novel End-User Development (EUD) approach for black-box AI models, enabling users to edit explanations and influence future predictions through targeted interventions. By combining explainability, user control, and model adaptability, the proposed method advances Human-Centered AI (HCAI), promoting a symbiotic relationship between humans and adaptive, user-tailored AI systems.

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