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].
Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
Authors: Drago Plecko, Elias Bareinboim
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our method to three real-world datasets and derive new insights on bias amplification in prediction and decision-making. |
| Researcher Affiliation | Academia | Drago Plecko and Elias Bareinboim Causal Artificial Intelligence Lab Columbia University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Auditing Weak & Strong Business Necessity |
| Open Source Code | Yes | The source code for reproducing all the experiments can be found in our Github code repository https://github.com/dplecko/mind-the-gap. The code is also included with the supplementary materials, in the folder source-code. |
| Open Datasets | Yes | We analyze the MIMIC-IV (Ex. 2), COMPAS (Ex. 3), and Census (Ex. 4, Appendix C) datasets. |
| Dataset Splits | No | The paper mentions using real-world datasets but does not explicitly provide details about training, validation, or test splits (e.g., percentages or sample counts) for the experiments. |
| Hardware Specification | Yes | All experiments were performed on a Mac Book Pro, with the M3 Pro chip and 36 GB RAM on mac OS 14.1 (Sonoma). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used (e.g., Python, PyTorch, scikit-learn versions). |
| Experiment Setup | No | The paper describes the context and purpose of the experiments (e.g., decision rules like b Y = 1(S > Quant(0.5; S))), but it does not specify concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations for the underlying machine learning models. |