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.

该奖项将支持利用人工智能 (AI) 和机器学习来改善低技能工人与现有工作的匹配的研究。 虽然美国就业市场从 COVID 19 大流行的破坏中恢复良好，但代表性不足的失业率仍然相对较高。尽管未填补的职位空缺不断增加并保持在高水平，但这表明求职者和雇主可获得的信息之间存在不匹配。这项研究将使用现场实验方法来调查人工智能辅助算法是否可以将技能和心理测量技能与在线职位空缺匹配。帮助工作该研究项目有可能改善技能分布低端工人的劳动力市场运作，从而增加该群体工人的就业机会，并提供人工智能和技术的机制证据。计算算法可以用来提高劳动力市场效率，这项研究的结果可以为增加就业、劳动生产率和经济增长的政策提供指导，因为该研究的重点是低工资工人，所以结果可以减少贫困和减少贫困​​。收入不平等并帮助美国成为减贫政策的全球领导者。该项目利用有关求职者特征和在线求职引擎发布的职位要求的数据，并使用随机对照试验（RCT）设计来调查为职位发布分配人工智能辅助算法匹配是否可以改善职位匹配。RCT 设计有三个特点。处理类别：(i) 求职者领域的职位空缺（不按特定顺序排列）；(ii) 职位空缺（不按特定顺序排列，但具有预测的匹配质量）；以及 (iii) 根据预测匹配的求职者的背景和技能进行排序的空缺职位；对照组是没有特定顺序列出的职位，并且没有附加匹配质量，RCT 将有 2600 名求职者作为样本，这些求职者在性别和空间上存在异质性，通过 Monster.com 社交媒体帐户上发布的广告进行招聘。第一组与对照组将回答治疗是否降低搜索成本从而提高匹配的问题，同时将第二组和第三组的结果与对照组的结果进行比较，第一组将回答治疗是否有帮助的问题以降低成本除了提供有关人工智能和计算算法可用于改善劳动力市场运作的机制的证据外，该研究项目的结果还可以为增加就业、提高生产率和就业机会的政策提供指导。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。