Human-in-the-loop and Storytelling-based Explainable Frameworks for the Next Generation of Explainable Ensemble Methods and Artificial Intelligence Techniques
Year: 2026
Type: dissertation
Abstract: Artificial intelligence (AI) is being adopted in diverse fields, including medical diagnosis and scientific research, to assist with decision-making thanks to its ability to learn underlying patterns from large amounts of data. However, these advances present a challenge. Modern AI systems, particularly deep learning (DL) models and large ensembles, are often difficult for audiences, such as domain experts, to understand due to their inherent black-box nature. This lack of transparency can reduce trust, impede adoption in critical fields, and cause problems with new regulations, such as the European Union’s (EU) AI Act, which impose strict transparency, accountability, and human oversight requirements. This thesis makes the following primary contributions: First, the thesis introduces a five-layer nested model for AI design and validation (Regulation, Domain, Data, Model, Prediction). This model systematically translates regulatory requirements into specific design and validation methodologies, surfacing threats to validity and compliance at each stage. Second, the thesis develops human-centered explainability frameworks, including Storytelling explainable AI (XAI) for medical imaging, which integrates multi-task distillation with visual and textual narratives. Third, the thesis introduces UbiQTree & UbiQVision, frameworks for uncertainty quantification in explainable AI (XAI). UbiQTree is instantiated for ensemble tree models, and UbiQVision is instantiated for deep vision models. They decompose shapley additive explanations (SHAP) based attributions into aleatoric, epistemic, and entangled components using Dirichlet processes and Dempster–Shafer theory (DST). This transformation shifts the focus from point-estimate explanations to distributions of explanations, allowing practitioners to calibrate explanations and facilitate meaningful human intervention. Finally, the thesis demonstrates domain-centric explainability through PepTriX, a framework for explainable peptide analysis. PepTriX integrates protein language models (PLM), predicted 3D structures, graph attention networks (GAT), and cross-modal co-attention to yield mechanistically interpretable representations. Together, these contributions reflect the thesis title, "Human-in-the-Loop and Storytelling-Based Explainable Frameworks for the Next Generation of Explainable Ensemble Methods and Artificial Intelligence Techniques". The work argues that explainability should be a core design principle throughout the AI lifecycle, not just an afterthought. Treating explanations as a means of communication and storytelling — i.e., narratives emphasizing the human reasoning process — brings AI systems closer to becoming not only good at achieving enhanced performance metrics, but also accountable, understandable, and better suited to users’ needs. This thesis is based on peer-reviewed publications and submissions covering AI regulations, human-centered explainability, uncertainty quantification, and domain-specific applications. The thesis allows for interpretive/interpretative human-in-the-loop (HITL) interaction, enabling domain users to inspect and calibrate model behavior as necessary. Together, these works demonstrate that developing trustworthy AI in critical fields requires a comprehensive approach encompassing regulatory compliance, human involvement, technical methodologies, and domain-specific relevance.
Author: Dubey, Akshat
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7172247662