Fair Sortition Made Transparent
Authors: Bailey Flanigan, Gregory Kehne, Ariel D. Procaccia
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in Section 5, we consider the algorithmic question in practice: given a maximally fair distribution over panels, can we actually find nearly maximally fair uniform lotteries that match our theoretical guarantees? To answer this question, we implement two standard rounding algorithms, along with near-optimal (but more computationally intensive) integer programming methods, for finding uniform lotteries. We then evaluate the performance of these algorithms in 11 real-world panel selection instances. |
| Researcher Affiliation | Academia | Bailey Flanigan Department of Computer Science Carnegie Mellon University bflaniga@andrew.cmu.edu Gregory Kehne School of Engineering and Applied Sciences Harvard University gkehne@g.harvard.edu Ariel D. Procaccia School of Engineering and Applied Sciences Harvard University arielpro@seas.harvard.edu |
| Pseudocode | No | The first is Pipage rounding [16], or PIPAGE, a randomized rounding scheme satisfying negative association [10]. The second is BECK-FIALA, the dependent rounding scheme used in the proof of Theorem 3.2. |
| Open Source Code | No | There is now a publicly available implementation of the techniques of Flanigan et al. [12], called Panelot, which optimizes the egalitarian notion that no pool member has too little selection probability via the Leximin objective from fair division [21, 14]. |
| Open Datasets | No | Finally, in Section 5, we consider the algorithmic question in practice: given a maximally fair distribution over panels, can we actually find nearly maximally fair uniform lotteries that match our theoretical guarantees? To answer this question, we implement two standard rounding algorithms, along with near-optimal (but more computationally intensive) integer programming methods, for finding uniform lotteries. We then evaluate the performance of these algorithms in 11 real-world panel selection instances. Acknowledgements. We would foremost like to thank Paul G olz for helpful technical conversations and insights on the practical motivations for this research. We also thank Anupam Gupta for helpful technical conversations. Finally, several organizations for supplying real-world citizens assembly data, including the Sortition Foundation, the Center for Climate Assemblies, Healthy Democracy, MASS LBP, Nexus Institute, Of by For, and New Democracy. |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, or predefined splits) is provided. |
| Hardware Specification | No | No specific hardware details (like CPU/GPU models, memory) are mentioned for the experimental setup. |
| Software Dependencies | No | We implement versions of two existing rounding algorithms, which are implicit in our worst-case upper bounds.6 The first is Pipage rounding [16], or PIPAGE, a randomized rounding scheme satisfying negative association [10]. The second is BECK-FIALA, the dependent rounding scheme used in the proof of Theorem 3.2. To benchmark these algorithms against the highest level of fairness they could possibly achieve, we use integer programming (IP) to compute the fairest possible uniform lotteries over supp(p ), the panels over which p randomizes.7 We define IPMAXIMIN and IP-NW to find uniform lotteries over supp(p ) maximizing Maximin and NW, respectively. |
| Experiment Setup | Yes | In all experiments, we generate a lottery of size m = 1000. |