Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Fairness in Decision-Making — The Causal Explanation Formula
Authors: Junzhe Zhang, Elias Bareinboim
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply these results to various discrimination analysis tasks and run extensive simulations, including detection, evaluation, and optimization of decision-making under fairness constraints. We conduct experiments in different fairness tasks, including discrimination detection, explanation, and design of reparatory policies. |
| Researcher Affiliation | Academia | Junzhe Zhang, Elias Bareinboim Purdue University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not include any statement about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes generating '5,000 observational samples' or '5,000 samples collected with the counterfactual randomization procedure' for its simulations and experiments, but does not specify or provide concrete access information (link, DOI, formal citation with authors/year) for any publicly available or open dataset. |
| Dataset Splits | No | The paper describes experiments run on 'observational samples' and 'simulations' without providing specific dataset split information (percentages, sample counts, or references to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact CPU/GPU models, memory, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | To witness, we consider a modified logistic model similar to the one studied in (Mac Kinnon et al. 2007) (see Fig. 1(a)). The outcome Y is a threshold-based indicator of a linear function, such that y = I{γ0 + γxyx + γzyz + γwyw + uy}, where I{ } is an indicator function, Uy follows the logistic distribution, and γ0 is the (unknown) decision threshold. |