Using AI to Expand the Job Search of Displaced Workers in the Aftermath of the Covid-19 Crisis

Covid-19 危机后利用人工智能扩大失业工人的求职范围

• 批准号: 2242538
• 负责人: Achyuta Adhvaryu
• 金额: $ 50.38万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242538&HistoricalAwards=false
• 关键词: Using; AI; Expand; Job; Search

This award will support research that uses artificial intelligence (AI) and machine learning to improve matching of low skilled workers to available jobs. While the US job market recovered well from the COVID 19 pandemic disruption, the unemployment rate remains relatively high for under-represented groups even as unfilled vacancies have risen and stayed high. This suggests a mismatch between the information available to jobseekers and employers. This research will use field experimental methods to investigate whether AI-assisted algorithmic matching of skills and psychometric skills to on-line vacancies can help job seekers get better job matches. This research project has the potential to improve the functioning of the labor market for workers at the lower end of the skill distribution, hence increase employment for this group of workers. Besides providing evidence on the mechanisms through which AI and computational algorithms can be used to improve labor market efficiency, the results of this research can provide guidance on policies to increase employment, labor productivity, and economic growth. Because the research focuses on low wage workers, the results can decrease poverty as well as decrease income inequality and help establish the US as a global leader in poverty reduction policies. This project leverages data on job seeker characteristics and the requirements of jobs posted by online job search engines and use a randomized control trial (RCT) design to investigate whether assigning an AI-assisted algorithmic matching to job postings improves job matching. The RCT design has three treatment arms: (i) job offers in the job-seekers field in no particular order; (ii) vacancies in no particular order but with predicted match quality; and (iii) vacancies sorted to best match backgrounds and skills of jobseekers with predicted match quality. The control group are jobs listed in no particular order and without a match quality attached. The RCT will have a sample of 2600 job seekers, with heterogeneity across gender and space, recruited via advertisements posted on Monster.com’s social media accounts. Comparison of the first group with the control group will answer the questions of whether treatment reduces search cost hence improves matching, while comparing outcomes for the second and third arms to those of the control group and the first arm will answer the questions as to whether treatment help to lower the cost of incomplete information and improves job match. Besides providing evidence on the mechanisms through which AI and computational algorithms can be used to improve the functioning of labor markets, the results of this research project can provide guidance on policies to increase employment, increase productivity, and economic growth.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项将支持使用人工智能（AI）和机器学习来改善低技能工人与可用工作的匹配的研究。尽管美国就业市场从共同的19个大流行中断中恢复了良好，但代表性不足的群体的失业率仍然相对较高，即使未填充的空位升高并保持较高。这表明对求职者和员工可用的信息之间的信息不匹配。这项研究将使用现场实验方法来研究AI辅助算法的技能和心理测量技能与在线职位空缺是否可以帮助求职者获得更好的工作匹配。该研究项目有可能在技能分配的低端改善工人的劳动力市场功能，从而增加了这组工人的就业机会。除了提供有关AI和计算算法可用于提高实验室市场效率的机制的证据外，这项研究的结果还可以提供有关提高就业，劳动生产率和经济增长的政策的指导。由于该研究的重点是低工资工人，因此结果可以减少贫困和减少收入不平等，并帮助美国成为减少贫困政策的全球领导者。该项目利用求职者特征的数据以及在线求职引擎发布的工作要求，并使用随机控制试验（RCT）设计来调查分配的AI辅助算法匹配到工作发布是否可以改善工作匹配。 RCT设计具有三个治疗臂：（i）没有特定顺序的求职者领域的工作要约； （ii）空缺没有特定顺序，但具有预测的匹配质量； （iii）空缺符合具有预测匹配质量的求职者的最佳匹配背景和技能。对照组是没有特定顺序列出的作业，没有附带的匹配质量。 RCT将有2600个求职者的样本，性别和空间的异质性，通过在Monster.com的社交媒体帐户上发布的广告招募。第一组与对照组的比较将回答有关治疗是否降低搜索成本的问题，因此将第二和第三臂的结果与对照组的结果进行比较，而对照组的结果和第一个臂的结果将回答有关治疗的问题是否有助于降低不完整信息的成本并改善工作匹配。除了提供有关AI和计算算法可用于改善劳动力市场功能的机制的证据外，该研究项目的结果还可以为提高就业，提高生产率和经济增长的政策提供指导。这奖反映了NSF的立法使命，并通过对基础的知识效果进行评估，并值得通过评估来进行评估。