Fair Allocation Over Time, with Applications to Content Moderation
Fair Allocation Over Time, with Applications to Content Moderation
复制标题
随着时间的推移公平分配,以及内容审核的应用
DOI:
10.1145/3580305.3599340
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发表时间:
2023
期刊:
影响因子:
--
通讯作者:
Stier-Moses, Nicolas
中科院分区:
文献类型:
--
作者:
Allouah, Amine;Kroer, Christian;Zhang, Xuan;Avadhanula, Vashist;Bohanon, Nona;Dania, Anil;Gocmen, Caner;Pupyrev, Sergey;Shah, Parikshit;Stier-Moses, Nicolas
In today's digital world, interaction with online platforms is ubiquitous, and thus content moderation is important for protecting users from content that do not comply with pre-established community guidelines. Given the vast volume of content generated online daily, having an efficient content moderation system throughout every stage of planning is particularly important. We study the short-term planning problem of allocating human content reviewers to different harmful content categories. We use tools from fair division and study the application of competitive equilibrium and leximin allocation rules for addressing this problem. On top of the traditional Fisher market setup, we additionally incorporate novel aspects that are of practical importance. The first aspect is the forecasted workload of different content categories, which puts constraints on the allocation chosen by the planner. We show how a formulation that is inspired by the celebrated Eisenberg-Gale program allows us to find an allocation that not only satisfies the forecasted workload, but also fairly allocates the remaining working hours from the content reviewers among all content categories. A fair allocation of oversupply provides a guardrail in cases where the actual workload deviates from the predicted workload. The second practical consideration is time dependent allocation that is motivated by the fact that partners need scheduling guidance for the reviewers across days to achieve efficiency. To address the time component, we introduce new extensions of the various fair allocation approaches for the single-time period setting, and we show that many properties extend in essence, albeit with some modifications. Lastly, related to the time component, we additionally investigate how to satisfy markets' desire for smooth allocation (i.e, an allocation that does not vary much from time to time) so that the switch in staffing is minimized. We demonstrate the performance of our proposed approaches through real-world data obtained from Meta.
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DOI:
--
发表时间:
2021
期刊:
International Conference on Artificial Intelligence and Statistics
影响因子:
--
作者:
Evrard Garcelon;Vashist Avadhanula;A. Lazaric;and Matteo Pirotta
通讯作者:
and Matteo Pirotta
DOI:
--
发表时间:
2019
期刊:
International Joint Conference on Artificial Intelligence
影响因子:
--
作者:
H. Aziz;Simon Rey
通讯作者:
Simon Rey
影响因子:
3.7
作者:
K. Arrow;M. Intriligator
通讯作者:
K. Arrow;M. Intriligator
DOI:
10.1016/j.geb.2008.11.011
发表时间:
2010
期刊:
Games Econ. Behav.
影响因子:
--
作者:
K. Jain;V. Vazirani
通讯作者:
V. Vazirani
DOI:
--
发表时间:
2021
期刊:
arXiv.org
影响因子:
--
作者:
Rahul M. Makhijani;P. Shah;Vashist Avadhanula;C. Gocmen;N. Stier;Julián Mestre
通讯作者:
Julián Mestre