TLDR: Holistic-XAI (H-XAI) is a new framework that expands AI transparency beyond developers to include individual users, regulators, and operational teams. It integrates traditional explanation methods with a novel Rating-Driven Explanation (RDE) approach, allowing stakeholders to interactively ask questions, test hypotheses, and compare AI model behavior against baselines. H-XAI provides both instance-level and global explanations, demonstrated through case studies in credit risk and financial forecasting, aiming to make AI more understandable, robust, and fair for all users.
Artificial intelligence (AI) is increasingly used in critical areas like finance and healthcare, but understanding how these complex systems make decisions remains a significant challenge. Traditional Explainable AI (XAI) methods have primarily focused on helping developers understand model outputs. However, a new framework called Holistic-XAI (H-XAI) aims to extend this transparency to a wider range of stakeholders, including individual users, regulatory bodies, and operational organizations.
The core idea behind H-XAI is to make AI explanations an interactive, multi-method process. Instead of providing a single, static explanation, H-XAI allows different users to ask a series of questions, test their own hypotheses, and compare how an AI model behaves against simple random or biased baselines. This framework combines both explanations for individual decisions and insights into the overall model behavior, adapting to what each stakeholder needs to know.
Addressing Diverse Stakeholder Needs
Imagine a loan applicant wondering why their loan was denied, or a data scientist wanting to know which features most influence loan approvals. A regulator, on the other hand, might be concerned about whether the model unfairly favors certain demographic groups. These are all valid questions, but they require different types of explanations. Current XAI tools often fall short because they are designed more for justifying predictions than for interactive exploration or testing “what-if” scenarios.
H-XAI addresses this by integrating two main types of explanation methods: traditional XAI techniques and a novel approach called Rating-Driven Explanation (RDE). Traditional methods, like SHAP and Partial Dependence Plots (PDPs), are good for understanding individual predictions or identifying which features are most important. For example, SHAP can tell an applicant which factors led to their specific loan rejection, and counterfactuals can suggest what minimal changes they could make to get approved.
Rating-Driven Explanation (RDE)
RDE is where H-XAI truly shines in providing broader insights. It’s designed for questions about a model’s robustness, fairness, and how it reacts to changes in input. RDE uses a causal model to understand how different attributes influence the AI’s outcome. It employs specific metrics like Weighted Rejection Score (WRS) to detect statistical differences across groups, Average Treatment Effect (ATE) to measure the causal impact of a change, and Deconfounded Impact Estimation (DIE %) to determine if an observed effect is truly causal or due to hidden factors.
The RDE workflow is structured: a user poses a question, H-XAI defines the relevant variables, selects the appropriate metric, computes scores for the model, and then compares these scores to automatically generated random and biased reference models. This comparison helps users understand if the model is behaving unreliably or unfairly, and why.
Also Read:
- Explanatory AI: A New Approach to Making AI Understandable for Everyone
- The Fair Game: A Dynamic Approach to Ensuring AI Fairness Over Time
Real-World Applications
The researchers demonstrated H-XAI through two practical case studies: binary credit risk classification and financial time-series forecasting. In the credit risk scenario, they showed how H-XAI could answer questions from an individual applicant (e.g., “What changes would get my loan approved?”), a regulatory official (e.g., “Is the model biased against certain demographics?”), and a data scientist (e.g., “Which model performs best in terms of accuracy and fairness?”).
For time-series forecasting, such as predicting stock prices, H-XAI helped assess model robustness under various conditions, like missing data, and evaluate if prediction errors varied systematically across different companies. This allows stakeholders like retail investors or regulatory officials to understand the reliability and limitations of forecasting tools.
In essence, H-XAI provides a flexible framework that allows users to navigate between different levels of explanation, from understanding a single decision to evaluating the overall fairness and stability of an AI system. It emphasizes that explanation is an iterative process, not a one-time output, empowering diverse stakeholders to better understand and trust AI-driven decisions. You can find more details about this innovative framework in the full research paper available at arXiv.org.
