explainable ai

Intelligent Purchase Order Generation with AI

This white paper presents a comprehensive functional and technical framework for applying artificial intelligence to purchase order generation and lifecycle management. It explains how AI, natural language processing, retrieval-augmented generation, business rules, enterprise data, and ERP integrations can transform ap...

From static analytics to continuous intelligence: toward a dynamic model of real-time organizational decision systems

The paper shows how familiar decision-system properties generate emergent organizational effects when recursively coupled within operational workflows and why comparable investments can produce divergent outcomes.

Don’t Take Their Word for It: Reply to Fan

In my (2024), I argued that there are principled reasons for thinking that AI cannot be an authority about one’s own mind. The idea is that it is only rational to treat another as an epistemic authority under certain conditions, and AI does not meet one of those conditions. Fan (2025) rejects the account of epistemic...

PREVENTING WAR/BURNOUT: THE IRONIC PERSPECTIVES OF TRUTH, REALITY, AND WORKABILITY

PREVENTING WAR/BURNOUT: THE IRONIC PERSPECTIVES OF TRUTH, REALITY, AND WORKABILITY Gerhard Ris, author, is a pensioner, former DA magistrate, and lawyer · Preface This article has a one-page introduction as an excerpt on the topic: Preventing war/burnout: the ironic perspectives of truth, reality, and workability. It p...

Time Is One-Dimensional Because Prediction Demands It, Not Because a Law Says So ── The Same Procedure, With Only the Signature Changed, Gives Amplifications of 0.012 and 3.5 × 10^32 ── [Paper 253]

Why is time one-dimensional? The answer offered here is that no law decrees it; the requirement that one be able to predict allows nothing else. The equations of a universe with two times can be written, and their solutions exist. What breaks is prediction. No new mathematical theorem and no new law is claimed. Scope o...

Explainable AI (XAI) via Causal Inference and Counterfactual Analysis

Current Explainable AI (XAI) methods frequently deliver post-hoc explanations that lack a fundamental understanding of the causal relationships underpinning AI decision-making. This paper proposes a novel approach to XAI that integrates causal inference and counterfactual analysis, aiming to generate more insightful an...

Auditable Credit Risk Intelligence for the U.S. Financial System: A Scalable Explainable-AI Framework Reconciling Predictive Performance with ECOA, FCRA, and Model Risk Governance

Machine-learning credit underwriting in the United States operates under three families of obligation that are not reducible to one another: a statutory prohibition on discrimination, a statutory duty to disclose the specific principal reasons for an adverse action, and prudential expectations for model risk management...

Advancing Decoding Methods for Enhanced AI Model Performance and Interpretability

The rapid advancement of artificial intelligence, particularly in large language models, has brought significant challenges in decoding methods that balance performance with interpretability. This paper presents a comprehensive analysis of advanced decoding techniques that enhance both model capabilities and explainabi...

aiDIVA – hybrid AI for rare disease diagnostics using evidence-based, machine learning and language models

aiDIVA is presented, an ensemble-AI combining statistical and machine learning models trained on genomic and phenotypic data to identify causal variants among tens of thousands per patient, and applies a random forest model to classify pathogenicity and generates evidence-based scores for dominant and recessive disease...

AI as an Aviation Safety Decision Maker

Artificial intelligence is rapidly evolving from an analytical support technology into an active participant in aviation safety decision-making. This paper examines how Safety Management Systems (SMS) must evolve as AI increasingly recommends or executes safety-critical operational decisions within aircraft operations,...

Advanced multi-modal Threat Detection: Explainable AI for Early Warning from Human Behavioral Cues Advanced multi-modal Threat Detection Preprint

Autonomous Enterprise Platforms: A Framework for AI-Guided Decision Loops, Predictive Intelligence, and Continuous Organizational Adaptation

Enterprise platforms are evolving from systems that primarily record and analyze business operations into intelligent environments capable of predicting outcomes, recommending interventions, executing decisions, and learning from their consequences. This article proposes a conceptual Autonomous Enterprise Platform (AEP...