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].
Individual Fairness, Base Rate Tracking and the Lipschitz Condition
Authors: Benjamin Eva
JAIR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, I demonstrate that the conception of individual fairness advocated by Dwork et al. is closely related to a criterion of group fairness, called base rate tracking, introduced in Eva (2022). I subsequently show that base rate tracking solves some fundamental conceptual problems associated with the Lipschitz criterion, before arguing that group level fairness criteria are at least as powerful as their individual level counterparts when it comes to diagnosing algorithmic bias. |
| Researcher Affiliation | Academia | Benjamin Eva EMAIL Duke University, Department of Philosophy, Durham, NC 27712 USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It discusses theoretical concepts and fairness criteria, formalizing them using mathematical notation but not algorithmic steps. |
| Open Source Code | No | The paper does not mention providing concrete access to source code for the methodology described. It is a theoretical paper focusing on conceptual analysis rather than new algorithmic implementations. |
| Open Datasets | No | The paper uses hypothetical scenarios and general group designations (e.g., 'loan application algorithm', 'James Bond movie', 'group S') to illustrate concepts. It does not refer to or use any specific publicly available or open datasets for empirical evaluation, nor does it provide any access information for a dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets. Therefore, there is no information provided regarding dataset splits for training, testing, or validation. |
| Hardware Specification | No | The paper is a theoretical work that does not describe any experiments requiring computational hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementation or experiments. Therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and focuses on conceptual analysis and comparison of fairness criteria, rather than conducting empirical experiments. Consequently, no specific experimental setup details, hyperparameters, or training configurations are provided. |