Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Fairness-Accuracy Trade-Offs: A Causal Perspective
Authors: Drago Plecko, Elias Bareinboim
AAAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 16,841 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,425 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach is evaluated across multiple real-world datasets, providing new insights into the tension between fairness and accuracy. In this section, we perform the causal fairness-accuracy analysis described in Sec. 2 on the Census 2018 dataset (Ex. 2). Additional analyses of the COMPAS (Ex. 3) and UCI Credit (Ex. 4) datasets are reported in Appendix E. |
| Researcher Affiliation | Academia | Department of Computer Science, Columbia University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Path-Specific Excess Loss Attributions, Algorithm 2: Causally-Fair Constrained Learning (CFCL) |
| Open Source Code | Yes | All code for reproducing the experiments can be found in our Github repository https://github.com/dplecko/causal-acc-decomp. |
| Open Datasets | Yes | In this section, we perform the causal fairness-accuracy analysis described in Sec. 2 on the Census 2018 dataset (Ex. 2). Additional analyses of the COMPAS (Ex. 3) and UCI Credit (Ex. 4) datasets are reported in Appendix E. (Referring to UCI Credit dataset) Yeh, I.-C. 2016. Default of Credit Card Clients. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C55S3H. |
| Dataset Splits | No | Input: training data Dt, evaluation data De, set S, precision ϵ. The text mentions 'splits the data into train and evaluation folds, Dt and De' but does not provide specific percentages, sample counts, or a detailed splitting methodology. |
| Hardware Specification | No | No specific hardware details (such as CPU/GPU models, memory, or cloud instance types) are mentioned in the paper. |
| Software Dependencies | No | No specific software libraries or frameworks with version numbers are provided. The paper mentions using neural networks and the Adam optimizer, but without specific versions for any libraries like TensorFlow or PyTorch. |
| Experiment Setup | No | Algorithm 2 mentions 'fit a neural network to solves the optimization problem in Eqs. 52-56 with λ = λmid on Dt to obtain the predictor b Y S(λmid)'. It also specifies 'nh hidden layers and nv nodes in each layer' and a 'precision ϵ' for the binary search. However, the paper does not provide concrete values for these hyperparameters (nh, nv, ϵ, or initial λhigh) used in the experiments. |