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].
On preserving non-discrimination when combining expert advice
Authors: Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nati Srebro
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our impossibility results for equalized odds. Surprisingly, we show that for a prevalent notion of non-discrimination, equalized odds, it is impossible to preserve non-discrimination while also competing comparably to the best predictor in hindsight (no-regret property). Our results for equalized error rates. The strong impossibility results with respect to equalized odds invite the natural question of whether there exists some alternative fairness notion that, given access to non-discriminatory predictors, achieves ef๏ฌciency while preserving non-discrimination. We answer the above positively by suggesting the notion of equalized error rates... |
| Researcher Affiliation | Academia | Avrim Blum TTI-Chicago EMAIL Suriya Gunasekar TTI-Chicago EMAIL Thodoris Lykouris Cornell University EMAIL Nathan Srebro TTI-Chicago EMAIL |
| Pseudocode | No | The paper describes algorithms like 'multiplicative weights' but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |