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An assistant professor of pc science and engineering at The College of Texas at Arlington has discovered that job candidates can enhance their place, on common, by at the very least 16 spots on a pool of 100 candidates by using an algorithm that makes use of job-specific key phrases.
Shirin Nilizadeh stated she was motivated to pursue this line of analysis after seeing buddies not be chosen for positions or second-round interviews.
“We discovered which you can tailor your resume for a particular job through the use of particular key phrases that might get you pushed towards the highest,” she stated. “It is a type of hack to the recruiting course of.”
Nilizadeh’s paper—”Assaults Towards Rating Algorithms with Textual content Embeddings: A Case Research on Recruitment Algorithms”—was accepted into the Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Deciphering Impartial Networks for NLP. Anahita Samadi, now a doctoral pupil at UTA who studied beneath Nilizadeh, led the undertaking and introduced it on the 2021 convention on Empirical Strategies in Pure Language Processing.
Textual content-embedding algorithms utilized in job recruiting match phrases and sentences in resumes to the job description to acquire similarity scores. Resumes are ranked primarily based on these scores. Few research till now have proven that rating algorithms that use textual content embeddings are susceptible to adversarial assaults.
“We thought recruitment algorithms had been the most effective instance of such rating algorithms and subsequently we determined to work on them,” Nilizadeh stated. “The purpose of our assault was to determine the key phrases from the job description that may enhance the rating of the resume.”
As anticipated, including extra key phrases improves the rating. The analysis additionally confirmed, nonetheless, that including too many comparable phrases or phrases won’t enhance the rating of a resume.
One of many major subjects that Nilizadeh research within the UTA Safety and Privateness Analysis Lab is adversarial robustness of synthetic intelligence (AI)-based, data-driven programs. In different phrases, she exams programs that use AI in opposition to attainable assaults and evaluates the robustness of these programs.
Hong Jiang, chair and professor within the Division of Laptop Science and Engineering, stated Nilizadeh’s work exhibits promise.
“It is perhaps a instrument prospective employees and employers may use within the job search course of,” Jiang stated.
University of Texas at Arlington
How key phrases can hack the hiring course of (2022, April 7)
retrieved 7 April 2022
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