Statistical inference for individual fairness
Authors: Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments we first verify our methodology in simulations and then present a case-study of testing individual fairness on the Adult dataset (Dua & Graff, 2017). We demonstrate the utility of our tools in a real-world case study. |
| Researcher Affiliation | Collaboration | Subha Maity Department of Statistics University of Michigan smaity@umich.edu Songkai Xue Department of Statistics University of Michigan sxue@umich.edu Mikhail Yurochkin IBM Research MIT-IBM Watson AI lab mikhail.yurochkin@ibm.com Yuekai Sun Department of Statistics University of Michigan yuekai@umich.edu |
| Pseudocode | Yes | Algorithm 1 Individual fairness testing |
| Open Source Code | No | Codes for Sen SR (Yurochkin et al., 2020) is provided with submission with a demonstration for fitting the model, where the choice of hyperparameters are provided. The codes can also be found in https://github.com/fairlearn/fairlearn. There is no explicit statement or link for the code implementing *their* specific individual fairness testing tools, only for components used or for anonymous review. |
| Open Datasets | Yes | Adult dataset (Dua & Graff, 2017)., and the corresponding reference: Dua & Graff. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml. Also COMPAS recidivism prediction dataset (Larson et al., 2016). |
| Dataset Splits | No | For each model, 10 random train/test splits of the dataset is used, where we use 80% data for training purpose. This statement describes train/test splits but does not mention a validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and links to the 'fairlearn' library on GitHub, but it does not specify any software names with version numbers for reproducibility. |
| Experiment Setup | Yes | For both the models same parameters are involved: learning_rate for step size for Adam optimizer, batch_size for mini-batch size at training time, and num_steps for number of training steps to be performed. We present the choice of hyperparameters in Table 2. Table 2: Parameters learning_rate batch_size num_steps Choice 10 4 250 8K. For each of the models we choose regularizer λ = 50, number of steps T = 500 and step size ϵt = 0.01. |