New Holistic XAI Framework Targets Transparency in Financial Decision‑Making AI

Global: New Holistic XAI Framework Aims to Boost Transparency in Financial AI

A research team has introduced Holistic eXplainable AI (H‑XAI), a framework designed to improve transparency and fairness in artificial‑intelligence systems that influence credit scoring and stock‑price forecasting. The paper, posted on arXiv in August 2025, outlines how the approach combines causality‑based rating methods with post‑hoc explanation techniques to serve users, auditors, and regulators. By framing explanation as an interactive, hypothesis‑driven process, H‑XAI seeks to address concerns about bias and instability in online decision contexts.

Background and Motivation

Existing explainable‑AI (XAI) tools primarily assist developers by justifying model internals, leaving a gap for affected stakeholders who require clear, actionable insights. Researchers argue that this developer‑centric focus limits accountability and public trust, especially when AI outputs directly impact financial outcomes.

Framework Overview

H‑XAI integrates two complementary components: a causality‑based rating system that quantifies model bias and instability, and a suite of post‑hoc explanations that operate at both global and instance levels. The rating system automatically generates random and biased baselines, enabling direct comparison of a model’s behavior against reference points.

Interactive Evaluation Process

The framework treats explanation as a dialogue, allowing stakeholders to pose questions, test hypotheses, and iteratively refine their understanding of model decisions. This interactive loop is intended to empower non‑technical users to assess whether an AI system aligns with regulatory expectations and ethical standards.

Case Studies

Two illustrative applications are presented. In a credit‑risk assessment scenario, H‑XAI reveals how certain demographic features contribute to disparate outcomes, highlighting potential fairness violations. In a stock‑price prediction model, the framework uncovers temporal instability, showing how predictions shift in response to market volatility.

Implications for Stakeholders

By extending explainability beyond developers, the approach offers auditors a structured method for compliance checks, provides regulators with evidence‑based metrics for oversight, and gives end‑users clearer insight into how algorithmic decisions affect them.

Future Directions

Authors suggest further validation across additional domains, integration with existing governance tools, and exploration of automated hypothesis generation to streamline the evaluation workflow. They emphasize that broader adoption could strengthen accountability in sociotechnical systems that rely on AI.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.