mlsanityData to 14 Oct 2026

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

arXiv 2504.04833 ↗First posted 7 Apr 2025cs.HC cs.AIcited by 0

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Abstract (arXiv)

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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References · 8

  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  2. 2023A Survey of Reinforcement Learning from Human Feedbackcited by 264
  3. 2017Unbiased Online Recurrent Optimizationcited by 47
  4. 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
  5. 2024From Explainable to Interactive AI: A Literature Review on Current Trends in Human-AI Interactioncited by 26
  6. 2023End-User Development for Artificial Intelligence: A Systematic Literature Reviewcited by 4
  7. 2025Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Frameworkcited by 2
  8. 2023Can Physician Judgment Enhance Model Trustworthiness? A Case Study on Predicting Pathological Lymph Nodes in Rectal Cancercited by 1

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