explainable ai

Algorithmic Transparency via Interpretable Representation Learning for Deep Neural Networks

Deep neural networks have achieved remarkable success in various domains, including image recognition, natural language processing, and game playing. However, their inherent complexity often leads to a "black box" problem, where the reasoning behind their predictions remains opaque to humans. This research investigates...

XMER: An Explainable Multimodal Emotion Recognition Framework for Trustworthy Human–AI Collaboration

Based on Neural-Symbolic AI for Causal Reasoning and Decision-Making

This paper presents a novel approach to causal reasoning and decision-making by integrating the strengths of neural and symbolic artificial intelligence. The core idea is to leverage the pattern recognition capabilities of neural networks to learn causal relationships while utilizing symbolic reasoning for logical infe...

Neuro-Symbolic Reasoning with Bayesian Networks

This paper proposes a novel approach to reasoning in complex domains by integrating the strengths of neural networks and Bayesian networks. The core idea is to leverage neural networks for feature extraction and symbolic representation learning, subsequently feeding these representations into a Bayesian network to faci...

Neuro-Symbolic Computation via Predictive Coding

This paper explores the integration of predictive coding, a core mechanism in neuroscience, with symbolic computation to create a novel approach to artificial intelligence reasoning. The central argument is that by combining the pattern recognition capabilities of neural networks with the rule-based reasoning of symbol...

Neuro-Assisted Therapy - A clinical-epistemic architecture for dynamic psychophysiological mapping, explainable artificial intelligence, and multimodal therapeutic inquiry

This article proposes Neuro-Assisted Therapy (NAT), an experimental clinical architecture integrating therapeutic conversation, naturalistic or controlled-provocative experiences, multimodal psychophysiological acquisition, and explainable artificial intelligence. Its aim is not to read thoughts, reveal unconscious con...

Explainable AI via Graph-Based Causal Inference for Model Decisions

The increasing deployment of deep learning models in critical applications necessitates a means to understand and trust their decisions. However, deep learning models are often 'black boxes,' offering limited insight into the reasoning behind their predictions. This paper proposes a novel approach to explain model deci...

Neural Symbolic Hybrid Models with Explainable Attention Mechanisms

This paper introduces a novel approach to enhancing the interpretability and robustness of neural models by integrating symbolic reasoning. We propose a hybrid model architecture where a neural network's attention weights are constrained and guided by symbolic rules. Simultaneously, the neural network learns to refine...

Explainable Artificial Intelligence via Causal Intervention Methods

This paper proposes a novel approach to explainable artificial intelligence (XAI) by leveraging causal intervention methods. The core idea is to model systems using causal graphs and then employ techniques like do-calculus for precise intervention and control. Traditional intervention methods often lack interpretabilit...

Probabilistic Programming for Explainable AI – Causal Chain Exploration

Explainable AI (XAI) is a critical area of research aimed at increasing the transparency and interpretability of complex AI models. Traditional XAI techniques often focus on post-hoc explanations, which can be misleading or lack a deep understanding of the underlying causal relationships. This paper proposes a novel me...

Brand Visibility Mediation in Conversational AI (Chatbots)

This paper highlights how conversational AI systems (chatbots) mediate brand visibility during user‑initiated discovery. It explains why these systems surface some brands more readily than others, how structural behaviours in AI interaction shape exposure, and why this creates representational imbalance. The paper outl...

Explainable AI for Cybersecurity Threat Detection with Causal Reasoning

The increasing sophistication of cyber threats necessitates robust and reliable cybersecurity systems. However, many current AI-based threat detection systems operate as "black boxes," offering limited transparency and explainability. This paper proposes a novel approach to cybersecurity threat detection that integrate...