explainable ai

Confessions of an AI Brain

Hypothesis-level reference argumentation network for case-based clinical reasoning in medical AI

Deep learning systems have achieved remarkable success in medical image analysis; however, most explainable artificial intelligence (XAI) methods remain descriptive, providing post-hoc explanations without actively validating diagnostic decisions. While concept-based and retrieval-based approaches improve interpretabil...

CampusHire: An AI-Powered Training and Placement Management System with Explainable Skill Matching

Campus training and placement (T&P) activities in many engineering institutions are still coordinated through spreadsheets, e-mail and notice boards, which makes eligibility checks, shortlisting and placement reporting slow and error-prone and gives students little guidance on which opportunities suit them. This paper...

A Systematic Review of Explainable Multimodal Artificial Intelligence for Early Identification of Developmental Potential in Children

Children who are developmentally lagging require quick and tailored supports- this is why focusing on potential development is important. AI has advanced by integrating multiple forms of speech, behaviour, physiology, and vision, and emerging AI systems for speech triage and multi-modal systems are demonstrating promis...

AI-Assisted Audit Performance and Ethical Risk Governance in Financial Fraud Identification

This study constructs a dual-cycle framework integrating financial fraud identification and ethical governance for artificial intelligence-assisted audit. It uses financial indicators, text features, and corporate governance variables to build multi-dimensional detection models. Traditional models and machine learning...

AI IN TAXATION, GST AND TAX ADMINISTRATION: TRANSFORMING COMPLIANCE, REVENUE COLLECTION AND TAXPAYER SERVICE

Artificial Intelligence (AI) is increasingly transforming the way governments design, administer and enforce tax systems. Tax administration is particularly suitable for AI because it involves large volumes of structured and unstructured data, repetitive compliance processes, risk assessment, fraud detection and contin...

Types of Plant Leaf Disease Detection Techniques Using Machine Learning Classification Algorithms

Plant diseases are one of the most serious threats to agricultural production worldwide — shortening harvests and, in turn, threatening our food supply. Recognizing these illnesses in an early, high-accuracy manner is vital for their management and mitigation. Traditional approaches to plant disease diagnosis are norma...

Explainable Multimodal Artificial Intelligence Framework for Real-Time Cognitive Engagement Assessment in Smart Learning Environments

An explainable, multimodal AI framework for assessing cognitive engagement in smart learning environments is presented in this paper. This framework integrates five modalities of sensing: facial expression, gaze, speech, interaction, and physiological sensing. The framework uses a preprocessing pipeline consisting of d...

Explainable AI for Safety of Life Applications in GNSS

Exploring the potential of AI-driven brain–computer interfaces for Alzheimer’s disease: an integrated framework for diagnosis and therapeutic intervention

Early diagnosis and timely intervention are critical in Alzheimer's disease (AD), yet conventional assessments fail to capture longitudinal neural dynamics. Although traditional biomarker frameworks offer essential pathological insights, they remain constrained by high costs and restricted accessibility. Recent advance...

AI behaves more like a brain than a database – cognitive science’s role in its origin story helps explain why

Toward Trustworthy AI for Post-stroke Suicidal Risk: Explainable Interaction Modeling and External Validation