The focus of this Perspective article is on the comparison of two of the most popular initial applicant screening methods: Resumes and application forms. The viewpoint offered is that application forms are superior to resumes during the initial applicant screening stage of selection. This viewpoint is supported in part based on criterion-related validity evidence that favors application forms over resumes. For example, the biographical data inventory, which can contain similar questions to those used in application forms, is one of the (...) most valid predictors of job performance, whereas job experience and years of education, which are often inferred from resumes and cover letters, are two of the least valid predictors of job performance. In addition to validity evidence, making decisions based on application forms as opposed to resumes is likely to help organizations defend against claims of discriminatory hiring while enhancing their ability to hire in a more diverse, equitable, and inclusive manner. For example, applicant names on resumes can lead to screening bias against members of identifiable subgroups, whereas an applicant’s name can be easily and automatically hidden from decision-makers when reviewing application forms. Despite these convincing arguments focused on applicant quality and diversity, a substantial research–practice gap regarding the use of resumes and cover letters remains. (shrink)

In recent years, the work of organizations in the area of digitization has intensified significantly. This trend is also evident in the field of recruitment where job application tracking systems (ATS) have been developed to allow job advertisements to be published online. However, recent studies have shown that recruiting in most organizations is not inclusive, being subject to human biases and prejudices. Most discrimination activities appear early but subtly in the hiring process, for instance, exclusive phrasing in job advertisement discourages (...) qualified applicants from minority groups from applying. The existing works are limited to analyzing, categorizing and highlighting the occurrence of bias in the recruitment process. In this paper, we go beyond this and develop machine learning models for identifying and classifying biased and discriminatory language in job descriptions. We develop and evaluate a machine learning system for identifying five major categories of biased and discriminatory language in job advertisements, i.e., masculine-coded, feminine-coded, exclusive, LGBTQ-coded, demographic and racial language. We utilized the combination of linguistic features with recent state-of-the-art word embeddings representations as input features for various machine learning classifiers. Our results show that the machine learning classifiers were able to identify all the five categories of biased and discriminatory language with a decent accuracy. The Random Forest classifier with FastText word embeddings achieved the best performance with tenfolds cross-validation. Our system directly addresses the bias in the attraction phase of hiring by identifying and classifying biased and discriminatory language and thus encouraging recruiters to write more inclusive job advertisements. (shrink)