Towards Robust and Reliable Algorithmic Recourse
Authors: Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation on multiple synthetic and real-world datasets demonstrates the efficacy of the proposed framework. We evaluated our approach ROAR on real world data from financial lending and education domains... |
| Researcher Affiliation | Academia | Sohini Upadhyay Harvard University supadhyay@g.harvard.edu Shalmali Joshi Harvard University shalmali@seas.harvard.edu Himabindu Lakkaraju Harvard University hlakkaraju@hbs.harvard.edu |
| Pseudocode | Yes | Algorithm 1 Our Optimization Procedure |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | Our first dataset is the widely used and publicly available German credit dataset [8] from the UCI repository. Our second dataset is the Small Business Administration (SBA) case dataset [17]. Our last dataset contains student performance records of 649 students from two Portuguese secondary schools, Gabriel Pereira (GP) and Mousinho da Silveira (MS) [8, 6]. |
| Dataset Splits | Yes | We use 5-fold cross validation throughout our real world and synthetic experiments. On D1, we use 4 folds to train predictive models and the remaining fold to generate and evaluate recourses. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper only mentions 'binary cross entropy loss and the Adam optimizer' without providing specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Our framework, ROAR, has the following parameters: the set of acceptable perturbations (defined in practice by δmax) and the tradeoff parameter λ. In our experiments on evaluating robustness to real world shifts, we choose δmax = 0.1 given that continuous features are scaled to zero mean and unit variance. We use binary cross entropy loss and the Adam optimizer to operationalize our framework, ROAR. |