Avinandan Bose, Tracey Li, Arunesh Sinha, Tien Mai
{"title":"A Fair Incentive Scheme for Community Health Workers","authors":"Avinandan Bose, Tracey Li, Arunesh Sinha, Tien Mai","doi":"10.1609/aaai.v37i12.26653","DOIUrl":null,"url":null,"abstract":"Community health workers (CHWs) play a crucial role in\nthe last mile delivery of essential health services to underserved\npopulations in low-income countries. Many nongovernmental\norganizations (NGOs) provide training and\nsupport to enable CHWs to deliver health services to their\ncommunities, with no charge to the recipients of the services.\nThis includes monetary compensation for the work that\nCHWs perform, which is broken down into a series of well defined\ntasks. In this work, we partner with a NGO D-Tree\nInternational to design a fair monetary compensation scheme\nfor tasks performed by CHWs in the semi-autonomous region\nof Zanzibar in Tanzania, Africa. In consultation with\nstakeholders, we interpret fairness as the equal opportunity\nto earn, which means that each CHW has the opportunity to\nearn roughly the same total payment over a given T month\nperiod, if the CHW reacts to the incentive scheme almost rationally.\nWe model this problem as a reward design problem\nfor a Markov Decision Process (MDP) formulation for the\nCHWs’ earning. There is a need for the mechanism to be\nsimple so that it is understood by the CHWs, thus, we explore\nlinear and piecewise linear rewards in the CHWs’ measured\nunits of work. We solve this design problem via a novel\npolicy-reward gradient result. Our experiments using two real\nworld parameters from the ground provide evidence of reasonable\nincentive output by our scheme.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"46 1","pages":"14127-14135"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/aaai.v37i12.26653","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Community health workers (CHWs) play a crucial role in
the last mile delivery of essential health services to underserved
populations in low-income countries. Many nongovernmental
organizations (NGOs) provide training and
support to enable CHWs to deliver health services to their
communities, with no charge to the recipients of the services.
This includes monetary compensation for the work that
CHWs perform, which is broken down into a series of well defined
tasks. In this work, we partner with a NGO D-Tree
International to design a fair monetary compensation scheme
for tasks performed by CHWs in the semi-autonomous region
of Zanzibar in Tanzania, Africa. In consultation with
stakeholders, we interpret fairness as the equal opportunity
to earn, which means that each CHW has the opportunity to
earn roughly the same total payment over a given T month
period, if the CHW reacts to the incentive scheme almost rationally.
We model this problem as a reward design problem
for a Markov Decision Process (MDP) formulation for the
CHWs’ earning. There is a need for the mechanism to be
simple so that it is understood by the CHWs, thus, we explore
linear and piecewise linear rewards in the CHWs’ measured
units of work. We solve this design problem via a novel
policy-reward gradient result. Our experiments using two real
world parameters from the ground provide evidence of reasonable
incentive output by our scheme.