Explanation Fidelity Evaluation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

6 leaderboard tables shown for this task, 6 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

6 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (7 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations
  21 Sep 2023 1 repository listed
  Post-hoc explanation techniques on graph neural networks (GNNs) provide economical solutions for opening the black-box graph models without model retraining.
• Can local explanation techniques explain linear additive models?
  19 Sep 2023 1 repository listed
  Local model-agnostic additive explanation techniques decompose the predicted output of a black-box model into additive feature importance scores.
• Towards Better Understanding Attribution Methods
  20 May 2022 1 repository listed
  Finally, we propose a post-processing smoothing step that significantly improves the performance of some attribution methods, and discuss its applicability.
• Developing a Fidelity Evaluation Approach for Interpretable Machine Learning
  16 Jun 2021 1 repository listed
  Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency.
• EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation
  19 Jul 2020 1 repository listed
  Defining a representative locality is an urgent challenge in perturbation-based explanation methods, which influences the fidelity and soundness of explanations.
• Meaningful Data Sampling for a Faithful Local Explanation Method
  18 Oct 2019 1 repository listed
  Data sampling has an important role in the majority of local explanation methods.