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André Panisson

@panisson.bsky.social

748 followers 469 following 11 posts

Principal Researcher @ CENTAI.eu | Leading the Responsible AI Team. Building Responsible AI through Explainable AI, Fairness, and Transparency. Researching Graph Machine Learning, Data Science, and Complex Systems to understand collective human behavior.

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André Panisson @panisson.bsky.social · 26/11/2024

Check out our poster at #LoG2024, based on our #TMLR paper: 📍 “A True-to-the-Model Axiomatic Benchmark for Graph-based Explainers” 🗓️ Tuesday 4–6 PM CET 📌 Poster Session 2, GatherTown Join us to discuss graph ML explainability and benchmarks #ExplainableAI #GraphML openreview.net/forum?id=HSQTv3R8Iz

A poster with a light blue background, featuring the paper with title: “A True-to-the-Model Axiomatic Benchmark for Graph-based Explainers”.
Authors: Corrado Monti, Paolo Bajardi, Francesco Bonchi, André Panisson, Alan Perotti

Background
Explainability in GNNs is crucial for enhancing trust understanding in machine learning models. Current benchmarks focus on data, ignoring the model’s actual decision logic, leading to gaps in understanding. Furthermore, existing methods often lack standardized benchmarks to measure their reliability and effectiveness.

Motivation
Reliable, standardised benchmarks are needed to ensure explainers reflect the internal logic of graph-based models, aiding in fairness, accountability, and regulatory compliance.

Research Question
If a model M is using a protected feature f , for instance using the gender of a user to classify whether their ads should gain more visibility, is a given explainer E able to detect it?

Core Idea
An explainer should detect if a model relies on specific features for node classification.
Implements a “true-to-the-model” rather than “truth-to-the-data” logic.

Key Components
White-Box Classifiers: Local, Neighborhood, and Two-Hop Models with hardcoded logic for feature importance.
Axioms: an explainer must assign higher scores to truly important features.
Findings:
Explainer Performance
Deconvolution: Perfect fidelity but limited to GNNs.
GraphLIME: Fails with non-local correlations and high sparsity.
LRP/Integrated Gradients: Struggle with zero-valued features.
GNNExplainer: Sensitive to sparsity and edge masking.

Real-World Insights: Facebook Dataset
Fidelity in detecting protected feature use in classification.
Results for different explainers, highlighting strengths and limitations.
Contributions:
Proposed a rigorous framework for benchmarking explainers
Demonstrated practical biases and flaws in popular explainers

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André Panisson @panisson.bsky.social · 26/11/2024

Check out our poster at #LoG2024, based on our #TMLR paper: 📍 “A True-to-the-Model Axiomatic Benchmark for Graph-based Explainers” 🗓️ Tuesday 4–6 PM CET 📌 Poster Session 2, GatherTown Join us to discuss graph ML explainability and benchmarks #ExplainableAI #GraphML openreview.net/forum?id=HSQTv3R8Iz

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