Fairwashing explanations with off-manifold detergent
Authors: Christopher Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical predictions in detailed experiments for various explanation methods, classifier architectures, and datasets, as well as for different tasks. |
| Researcher Affiliation | Academia | 1Machine Learning Group, Technische Universit at Berlin, Germany 2Max-Planck-Institut f ur Informatik, Saarbr ucken, Germany 3Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea. |
| Pseudocode | No | The paper does not contain any sections explicitly labeled "Pseudocode" or "Algorithm", nor does it present structured code-like blocks. |
| Open Source Code | Yes | The code for all our experiments is publicly available at https://github.com/fairwashing/fairwashing. |
| Open Datasets | Yes | Datasets: We consider the MNIST, Fashion MNIST, and CIFAR10 datasets. We use the standard training and test sets for our analysis. |
| Dataset Splits | Yes | We use the standard training and test sets for our analysis. |
| Hardware Specification | Yes | This is computationally expensive and takes about 48h using four Tesla P100 GPUs. |
| Software Dependencies | No | The paper mentions using "standard deep learning libraries" (e.g., Kokhlikyan et al., 2019; Alber et al., 2019; Ancona et al., 2018) but does not specify exact version numbers for any software dependencies. |
| Experiment Setup | Yes | We train the model g by minimizing the standard cross entropy loss for classification. The manipulated model g is then trained by minimizing the loss (11) for a given target explanation ht. This target was chosen to have the shape of the number 42. For more details about the architectures and training, we refer to the Appendix D. |