Debiasing Concept-based Explanations with Causal Analysis
Authors: Mohammad Taha Bahadori, David Heckerman
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our synthetic and real-world experiments demonstrate the success of our method in removing biases and improving the ranking of the concepts in terms of their contribution to the explanation of the predictions. |
| Researcher Affiliation | Industry | Mohammad Taha Bahadori, David E. Heckerman {bahadorm, heckerma}@amazon.com |
| Pseudocode | Yes | Algorithm 1 Debiased CBMs |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We evaluate the performance of the proposed approach on the CUB200-2011 dataset (Wah et al., 2011). |
| Dataset Splits | Yes | The dataset includes 11788 pictures (in 5994/5794 train/test partitions) of 200 different types of birds, annotated both for the bird type and 312 different concepts about each picture. ... We randomly choose 15% of the training set and hold out as the validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions 'Torch Vision s pre-trained Res Net152 network' and 'Adam optimization algorithm' but does not specify their version numbers or other crucial software dependencies with versions. |
| Experiment Setup | Yes | We model the distribution of the concept logits as Gaussians with means equal to the Res Net152 s logit outputs and a diagonal covariance matrix. We estimate the variance for each concept by using the logits of the true concept annotation scores that are clamped into [0.05, 0.95] to avoid large logit numbers. In each iteration of the training loop for Line 3, we draw 25 samples from the estimated p(d|x). Predictor of labels from concepts (the function g( ) in Eq. (5)) is a three-layer feed-forward neural network with hidden layer sizes (312, 312, 200). There is a skip connection from the input to the penultimate layer. All algorithms are trained with Adam optimization algorithm (Kingma & Ba, 2014). |