I-Corps: AI-assisted Job Search in the Aftermath of the Covid-19 Crisis

这个I-Corps项目更广泛的影响/商业潜力是开发一种技术，帮助面向客户的企业从一群没有该行业经验的候选人中招聘。 所提出的技术可以由招聘经理在入门级服务人员的中型到大型雇主中使用。预计滩头市场将是零售市场。初步客户发现的关键见解是，在拥有1万名员工的大型商店中，入门级职位的人员流动率非常高（例如，2019年零售业76%），每个入门级职位空缺都吸引了大量的申请（例如，每个职位最多1，000个申请）。由于技术能力和以往经验有限，这些空缺的申请人往往难以区分。 因此，目前对初级职位的筛选过程严重依赖于任意筛选、个人判断和面试，因此特别容易产生歧视。此外，雇主的目标是雇用具有特定软技能的候选人（例如，积极性、对细节的关注、内在动机、沟通），但发现很难客观地衡量它们。此外，不包括生产力损失，替换离职工人的成本很高（通常是工人年薪的1.5至2.5倍）。 通过专注于心理测量和模拟任务，拟议的技术：a）减少招聘时系统性歧视的风险; B）使雇主能够廉价、快速和公平地从一群难以区分的候选人中确定最合适的候选人; c）通过记录技能在职业和部门之间的可转移性，有能力将工人与最合适的职业相匹配。假设是，该技术可以减少筛选和招聘成本超过60%，每个人雇用，除了找到员工，在一个较低的attrit率。这个I-Corps项目是基于机器学习分类算法的开发，使用心理测量配置文件和模拟任务的性能，以确定最适合的候选人入门级面向客户的职业。这些任务是与每个行业的雇主合作设计的，以反映实际工作。为了为每个任务选择最佳算法，所提出的技术使用了一个成本函数，该函数考虑了雇主错误拒绝合格人员以及面试不合格申请人所造成的损失。此外，所提出的技术在选择和校准算法时使用劳动力市场参数来平衡假阳性和假阴性预测，这甚至推动了该领域的前沿，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

The broader impact/commercial potential of this I-Corps project is the development of a technology that will help customer-facing businesses hire from a pool of candidates that have no prior experience in the sector. The proposed technology may be used by hiring managers in mid- to large-scale employers of entry-level service workers. It is anticipated that the beachhead market will be retail. The key insights from preliminary customer discovery were that in large stores with 10k employees, the turnover in entry level positions is very high (e.g., 76% in retail in 2019) and each entry-level vacancy attracts a high volume of applications (e.g., up to 1,000 applications per position). Applicants for these vacancies are often indistinguishable given limited technical skills and previous experience. Therefore, current screening process for entry-level positions relies heavily on arbitrary filtering heuristics, individual judgement, and interviews, and, as a result, can be particularly prone to discrimination. In addition, employers aim to hire candidates with particular soft skills (e.g., conscientiousness, attention to detail, intrinsic motivation, communication), but find it difficult to objectively measure them. In addition, the cost of replacing a worker who leaves, excluding lost productivity, is high (typically 1.5 to 2.5 times the worker’s annual salary). By focusing on psychometric and simulated tasks, the proposed technology: a) reduces the risk of systemic discrimination while hiring; b) allows employers to cheaply, quickly and fairly identify best-suited candidates from a pool of indistinguishable candidates; c) have the ability to match workers to best-suited occupations by documenting the transferability of skills across occupations and sectors. The hypothesis is that the technology may reduce screening and hiring costs by more than 60% per head hired, in addition to finding employees that attrit at a lower rate.This I-Corps project is based on the development of machine learning classification algorithms that use psychometric profiles and performance on simulated tasks to identify best-suited candidates for entry-level customer facing occupations. These tasks have been designed in partnership with employers in each industry to mirror actual work. To select the optimal algorithm for each task, the proposed technology uses a cost function that takes into account the losses to the employer from wrongfully rejecting qualified individuals as well as from interviewing unqualified applicants. Additionally, the proposed technology uses labor market parameters in balancing false positive and false negative predictions in selecting and calibrating the algorithms, which pushes even the frontier in this space, and is beyond the capabilities of any practical solutions in the market today.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.