Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Auditing Local Explanations is Hard
Authors: Robi Bhattacharjee, Ulrike Luxburg
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove upper and lower bounds on the amount of queries that are needed for an auditor to succeed within this framework. Our results show that successful auditing requires a potentially exorbitant number of queries particularly in high dimensional cases. Our analysis also reveals that a key property is the locality of the provided explanations a quantity that so far has not been paid much attention to in the explainability literature. |
| Researcher Affiliation | Academia | Robi Bhattacharjee University of Tübingen and Tübingen AI Center EMAIL Ulrike von Luxburg University of Tübingen and Tübingen AI Center EMAIL |
| Pseudocode | Yes | Algorithm 1 simple_audit(X, f(X), E(f, X), ϵ1, ϵ2, γ, δ) |
| Open Source Code | No | This is a theory paper, and therefore does not provide open-source code for a practical methodology. |
| Open Datasets | No | This is a theory paper and does not conduct experiments with real-world datasets. It discusses theoretical data distributions like 'µ'. |
| Dataset Splits | No | This is a theory paper and does not conduct experiments with data splits. |
| Hardware Specification | No | This is a theory paper and does not conduct experiments requiring specific hardware specifications. |
| Software Dependencies | No | This is a theory paper and does not conduct experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theory paper and does not describe an experimental setup with hyperparameters or system-level training settings. |