Determining systematic differences in human graders for machine learning-based automated hiring

Firms routinely utilize natural language processing combined with other machine learning (ML) tools to assess prospective employees through automated resume classification based on pre-codified skill databases. The rush to automation can however backfire by encoding unintentional bias against groups of candidates. We run two experiments with human evaluators from two different countries to determine how cultural differences may affect hiring decisions. We use hiring materials provided by an international skill testing firm which runs hiring assessments for Fortune 500 companies. The company conducts a video-based interview assessment using machine learning, which grades job applicants automatically based on verbal and visual cues. Our study has three objectives: to compare the automatic assessments of the video interviews to assessments of the same interviews by human graders in order to assess how they differ; to examine which characteristics of human graders may lead to systematic differences in their assessments; and to propose a method to correct human evaluations using automation. We find that systematic differences can exist across human graders and that some of these differences can be accounted for by an ML tool if measured at the time of training

WorkerRank: Using employer implicit judgements to infer worker reputation

In online labor marketplaces two parties are involved; employers and workers. An employer posts a job in the marketplace to receive applications from interested workers. After evaluating the match to the job, the employer hires one (or more workers) to accomplish the job via an online contract. At the end of the contract, the employer can provide his worker with some rating that becomes visible in the worker online profile. This form of explicit feedback guides future hiring decisions, since it is indicative of worker true ability. In this paper, first we discuss some of the shortcomings of the existing reputation systems that are based on the end-of-contract ratings. Then we propose a new reputation mechanism that uses Bayesian updates to combine employer implicit feedback signals in a linkanalysis approach. The new system addresses the shortcomings of existing approaches, while yielding better signal for the worker quality towards hiring decision