Fairness Through Computationally-Bounded Awareness
Authors: Michael Kim, Omer Reingold, Guy Rothblum
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a new notion of fairness called metric multifairness and show how to achieve this notion in our setting. [...] At present, the results are theoretical, but we hope this work can open the door to empirical studies across diverse domains, especially since one of the strengths of the framework is its generality. We view testing the empirical performance of metric multifairness with various choices of metric d and collection C as an exciting direction for future research. |
| Researcher Affiliation | Academia | Michael P. Kim Stanford University mpk@cs.stanford.edu Omer Reingold Stanford University reingold@stanford.edu Guy N. Rothblum Weizmann Institute of Science rothblum@alum.mit.edu |
| Pseudocode | Yes | Algorithm 1: Switching Subgradient Descent Let > 0, T 2 N, and C 2X X . Initialize w0 2 F = [ B, B]n; W = ; For k = 1, . . . , T: If 9S 2 C such that ˆRS(wk) > 4 /5: // some constraint violated Sk any S 2 C such that ˆRS(wk) > 4 /5 wk+1 wk M 2 r RSk(wk) /* step according to constraint project onto F if necessary */ Else: // no violations found W W [ {wk} // update set of feasible iterates wk+1 wk GM r L(wk) /* step according to objective project onto F if necessary */ Output w = 1 |W | P w2W w // output average of feasible iterates |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide links to a code repository. |
| Open Datasets | No | The paper defines abstract data distributions ('Let D denote the data distribution over individuals and labels supported on X { 1, 1}') but does not specify any particular publicly available or open dataset used for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental procedures that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and describes algorithms and proofs, but does not mention specific software dependencies with version numbers for implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments or their setup, thus no specific hyperparameter values or training configurations are provided. |