Incentivizing Recourse through Auditing in Strategic Classification
Authors: Andrew Estornell, Yatong Chen, Sanmay Das, Yang Liu, Yevgeniy Vorobeychik
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments using four common datasets: Adult Income [Kohavi and others, 1996], Law School [Wightman and Council, 1998], German Credit [Dua and Graff, 2019], and Lending Club [Lending Club, 2018]... We measure the fraction of the population performing recourse or manipulation, as well as the average cost incurred by agents for either action (Figure 1). |
| Researcher Affiliation | Academia | 1Washington University in Saint Louis 2University of California Santa Cruz 3George Mason University |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled "Pseudocode" or "Algorithm", nor are there any structured code-like blocks. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We conduct experiments using four common datasets: Adult Income [Kohavi and others, 1996], Law School [Wightman and Council, 1998], German Credit [Dua and Graff, 2019], and Lending Club [Lending Club, 2018] |
| Dataset Splits | Yes | Full experimental details are provided in the supplement Section A.5. We use a 70/30 train/test split, and for all datasets we train a Logistic Regression and a 2-layer Neural Network model... We use 5-fold cross validation for hyperparameter tuning... |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, or memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using "Logistic Regression" and "2-layer Neural Networks" but does not list specific software packages with version numbers (e.g., Python, PyTorch, scikit-learn, TensorFlow versions). |
| Experiment Setup | Yes | Full experimental details are provided in the supplement Section A.5. We use a 70/30 train/test split, and for all datasets we train a Logistic Regression and a 2-layer Neural Network model. We normalize all features... We use 5-fold cross validation for hyperparameter tuning, and use the Adam optimizer with a batch size of 1024 for 1000 epochs, with a learning rate of 1e-3 and a weight decay of 1e-4. |