An Information-Theoretic Quantification of Discrimination with Exempt Features
Authors: Sanghamitra Dutta, Praveen Venkatesh, Piotr Mardziel, Anupam Datta, Pulkit Grover3825-3833
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then perform a case study using one observational measure to show how one might train a model allowing for exemption of discrimination due to critical features. Case Study: The goal is to decide whether to show ads for an editorial job requiring English proficiency, based on whether a score generated from internet activity is above a threshold. We train a classifier of the form ˆY = 1/(1 + e (w T X+b)) (logistic regression). |
| Researcher Affiliation | Academia | Sanghamitra Dutta, Praveen Venkatesh, Piotr Mardziel, Anupam Datta, Pulkit Grover Carnegie Mellon University sanghamd@andrew.cmu.edu, {vpraveen, piotrm, danupam, pulkit}@cmu.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper describes a synthetic dataset for the case study, e.g., 'Z Bern(1/2) is a protected attribute...', 'U1, U2, U3 i.i.d. N(0, 1)'. However, it does not provide concrete access information (link, DOI, citation, or repository) for this dataset. |
| Dataset Splits | No | The paper mentions training a classifier and performing '100 simulations of 7000 iterations each with batch size 200' but does not specify explicit training, validation, or test dataset splits or their sizes. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers. |
| Experiment Setup | Yes | The paper specifies training details such as 'We train a classifier of the form ˆY = 1/(1 + e (w T X+b)) (logistic regression)... We train using the following loss functions: Loss L1: minw,b LCross Entropy(Y, ˆY ). Loss L2: minw,b LCross Entropy(Y, ˆY )+λ I(Z; ˆY ), ... Loss L3: minw,b LCross Entropy(Y, ˆY |Xc)... (100 simulations of 7000 iterations each with batch size 200).' |