The increasing deployment of Artificial Intelligence (AI) systems in critical applications necessitates methods for understanding and trusting their decisions. Traditional explainable AI (XAI) techniques often struggle to capture the complex, dynamic relationships within systems, particularly those evolving over time. This paper introduces a novel approach to generating counterfactual explanations that explicitly accounts for temporal dependencies. Our method leverages a temporal difference learning framework to model the impact of changes in input variables on the AI model's output, propagating these effects over time to generate more accurate and informative counterfactual scenarios. We demonstrate the efficacy of this approach through a theoretical framework, outlining the key components and mathematical formulations. The core contribution lies in the ability to provide explanations that move beyond static snapshots, reflecting the dynamic evolution of the AI system's behavior. This offers a significantly improved understanding of AI decision-making processes, crucial for building robust and trustworthy AI systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The increasing reliance on Artificial Intelligence (AI) in hardware design presents both significant opportunities and challenges. While AI algorithms can accelerate the design process and optimize performance, their "black box" nature raises concerns regarding trust, accountability, and potential biases. This paper argues for the critical importance of algorithmic transparency and explainable AI (XAI) within the context of hardware design. We propose a framework for developing techniques that elucidate the decision-making processes of AI-powered hardware design tools. Specifically, we explore methods for visualizing and interpreting the reasoning behind AI's choices, offering insights into the underlying algorithms and their influence on design outcomes. The core claim is that understanding these design choices is fundamental to building trust and ensuring accountability. This work lays the groundwork for a future where AI and human designers can collaborate effectively, leveraging the strengths of both while mitigating potential risks. The proposed approach involves a combination of algorithmic analysis, visualization techniques, and the development of metrics to quantify the explainability of AI-driven hardware design systems. Ultimately, this research contributes to the broader field of XAI by addressing a particularly challenging and impactful domain: hardware.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The increasing deployment of neural networks in critical applications necessitates methods for understanding their decision-making processes – a field often referred to as Explainable AI (XAI). Traditional XAI techniques frequently provide post-hoc explanations that lack a solid causal foundation, potentially masking spurious correlations and leading to misleading interpretations. This paper proposes a novel approach to XAI that leverages causal mediation analysis to uncover the underlying causal pathways driving neural network predictions. We treat the neural network's prediction as a mediator variable, allowing us to rigorously assess the relationships between input features and the target variable through this intermediary. This framework provides a more robust and interpretable explanation by explicitly considering causal mechanisms. We demonstrate the potential of this approach through a theoretical exploration and illustrate its benefits with a simplified example. The core claim of this work is that causal mediation analysis significantly enhances the explainability of neural network predictions by revealing the true causal influences.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The increasing deployment of neural networks in critical applications necessitates a deeper understanding of their decision-making processes – a field commonly addressed through explainability methods. However, current explainability techniques often rely on approximations and heuristics, lacking rigorous guarantees about their accuracy and reliability. This work introduces a novel approach to validate neural network explainability methods by leveraging formal verification techniques, specifically model checking. Our core claim is that many existing explainability methods fail to provide sufficient assurance regarding their correctness. We propose a mechanism for rigorously proving the accuracy of these algorithms by formally verifying their ability to faithfully represent the underlying decision-making process of the neural network. This framework provides a foundation for building more trustworthy and reliable AI systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel Neural Symbolic Reasoning Engine (NSRE) designed to bridge the gap between the pattern recognition capabilities of neural networks and the logical reasoning capabilities of symbolic systems. The core claim is that combining these two approaches yields a system with enhanced knowledge representation and reasoning abilities. The mechanism involves constructing an NSRE that leverages neural networks for knowledge extraction and utilizes symbolic reasoning algorithms for logical inference and knowledge transfer. The system addresses the limitations of current approaches in representing and reasoning with complex knowledge. The architecture consists of a neural network component, responsible for encoding contextual information and identifying relevant knowledge fragments, and a symbolic reasoning engine, which employs a rule-based system to perform logical deductions and integrate the extracted knowledge. This approach offers a pathway to more robust and explainable AI systems, particularly in domains requiring both perceptual understanding and logical deduction. We explore the fundamental components and the interaction between the neural and symbolic modules, outlining a framework for building intelligent systems capable of dynamic knowledge acquisition and sophisticated reasoning. The goal is to create a system that can not only recognize patterns but also reason about them in a logically consistent manner, ultimately leading to more reliable and adaptable AI.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Reinforcement learning (RL) has achieved remarkable success in various domains, from game playing to robotics. However, the "black box" nature of many RL agents presents a significant challenge, particularly in applications where transparency and trust are paramount. This work proposes a novel approach to explainable AI (XAI) within the context of RL by integrating causal reasoning. Traditional RL methods primarily rely on correlation-based learning, which can lead to spurious correlations and opaque decision-making processes. Our approach introduces a mechanism where RL agents explicitly identify and reason about causal relationships between states, actions, and rewards. This allows the agent to articulate *why* it took a particular action, connecting it to the underlying causes of the situation. We formalize this process using a causal Bayesian network and demonstrate how this framework can be implemented within a standard RL architecture. The core contribution is a method for generating explanations that are not only plausible but also reflect a genuine understanding of the environment's causal structure. This framework offers a path towards more robust, trustworthy, and interpretable RL systems, addressing a critical limitation of current approaches.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to artificial intelligence by integrating hyperdimensional computing (HDC) with neural symbolic computation. The core idea is to leverage the strengths of both paradigms: HDC's efficiency in high-dimensional pattern recognition and neural networks' ability to generate and manipulate symbolic representations. We outline a hybrid architecture where HDC acts as a fast, distributed pattern detector, while a neural network constructs and refines symbolic representations of the detected patterns. These symbolic representations then guide the HDC computations, creating a feedback loop that enhances both recognition accuracy and interpretability. The paper details the proposed architecture, focusing on the interaction between the two components and the mechanisms for knowledge transfer. We argue that this integration represents a significant step towards more robust and explainable AI systems, and demonstrate a potential path for achieving emergent intelligence through the synergistic combination of these computational approaches. The architecture is designed to minimize the computational burden of symbolic processing while maximizing the pattern recognition capabilities of HDC. The system's ability to translate complex patterns into a symbolic form allows for reasoning and deduction, ultimately leading to more sophisticated cognitive processes.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The increasing deployment of machine learning (ML) models in critical applications raises significant concerns regarding transparency and trustworthiness. Many ML models, particularly deep neural networks, operate as "black boxes," making it difficult to understand their internal decision-making processes. This paper proposes a novel approach to algorithmic transparency by leveraging formal model verification techniques. We argue that formal verification provides a rigorous method for establishing guarantees about the behavior of ML algorithms, moving beyond post-hoc explanations and offering verifiable proof of correctness. Specifically, we apply formal methods, such as model checking and theorem proving, to analyze ML algorithms. This process generates detailed proofs of correctness, identifies potential vulnerabilities, and ultimately enhances the trustworthiness of ML systems. The core claim is that formal model verification offers a robust solution to the black-box problem, providing a path towards more reliable and explainable AI. We demonstrate the feasibility and potential benefits of this approach, highlighting its importance in ensuring responsible AI development and deployment. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to artificial intelligence combining the strengths of neural networks and symbolic reasoning through dynamic rule extraction. The core idea is to leverage neural networks for pattern recognition and expression generation, followed by a dynamic rule extraction module that iteratively refines these expressions into logical rules. This process ensures that the extracted rules are both consistent and supported by the underlying data. Unlike traditional rule-based systems, this method offers enhanced explainability and robustness by allowing the system to learn and refine its reasoning capabilities directly from data. The system's architecture involves a neural network component that maps input data to symbolic expressions, and a rule extraction module which employs a consistency checking mechanism and iterative refinement steps. The key innovation lies in the dynamic adaptation of the rules based on continuous feedback, resulting in a more adaptable and reliable AI system. This approach addresses limitations of static rule systems and simple neural networks by providing a framework for intelligent rule discovery.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Automated theorem proving (ATP) systems have achieved remarkable success in verifying complex mathematical theorems. However, their opacity – the lack of human-understandable explanations for their reasoning – remains a significant barrier to widespread adoption. This research proposes a novel approach to enhance the explainability of ATP systems by integrating counterfactual reasoning. We leverage the "what if" scenario – exploring alternative possibilities – to provide transparent justifications for each step in the theorem proving process. This method identifies critical steps and generates counterfactual scenarios, allowing users to understand *why* a particular step was taken and to assess the robustness of the overall proof. The core claim is that integrating counterfactual analysis into ATP systems significantly improves transparency and trustworthiness, ultimately increasing their accessibility and utility. This paper details the framework, outlines the key components, and provides a conceptual overview of the approach, highlighting its potential impact on the field of automated reasoning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to neuro-symbolic reasoning by representing symbolic knowledge as continuous tensor networks. Traditional neuro-symbolic systems often struggle with the disjointed nature of neural network pattern recognition and symbolic logic inference. Our method overcomes this challenge by allowing for a seamless transition between continuous and discrete reasoning. Neural networks are employed to learn the parameters within these tensor networks, effectively bridging the gap between perception and deduction. The core idea is to encode logical rules and relationships directly into the structure and parameters of a tensor network, allowing the system to perform both pattern recognition and logical inference. We demonstrate that this continuous representation facilitates more efficient and robust reasoning compared to conventional approaches. The system learns to manipulate the tensor network, effectively 'solving' logical problems by evolving its structure and parameter values. This approach offers a potential path towards more explainable and adaptable AI systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to explainable artificial intelligence (XAI) that leverages causal modeling to enhance the transparency and trustworthiness of AI systems. Current XAI techniques often prioritize interpretability without considering the underlying causal relationships governing the data. We introduce a framework that utilizes causal graphs to represent domain knowledge and incorporates machine learning algorithms for inference, leading to more robust and explainable decision-making. The core claim is to design an AI model capable of reasoning and decision-making based on causal relationships, thereby improving transparency and trust. The mechanism involves constructing causal models using causal graphs and integrating machine learning algorithms for inference. This approach addresses a critical gap in existing XAI methods by explicitly incorporating causal reasoning, offering a pathway towards AI systems that not only explain their decisions but also understand the true causal drivers behind them. This work lays the foundation for building more reliable and justifiable AI systems across various domains.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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