Difference between revisions of "Automated Resume Screening"
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| − | Most resume screening programs come as part of [[Wikipedia: applicant tracking system|applicant tracking system]]. | + | Most resume screening programs come as part of [[Wikipedia: applicant tracking system|applicant tracking system]]. These programs can store personal and contact information of different applicants categorized by their key skills and talents. If some candidate does not seem a good fit for some role at the current time, these programs keep their information in the system and can use it in future in case a relevant role opens that is a good fit for the candidate. This increases the pool of applicants and helps companies find a better fit for their roles. |
==<big> Ethical Implications </big>== | ==<big> Ethical Implications </big>== | ||
Revision as of 21:59, 28 January 2022
Automated Resume Screening refers to the use of machine learning algorithms and artificial intelligence (AI) to parse and extract information from applicant resumes. Different algorithms are used to identify relevant skills and experience for the job from the applicant's resume using keywords in the job description and requirements.
Contents
Benefits for Companies
Time Efficient
Automating resume screening using AI algorithms saves companies time-to-hire. On average, a recruiter spends 6 seconds to manually scan a resume [1]. AI algorithms can process exponentially more applicants than humans in the same time and can also extract more meaningful information than a human can in the same time. This reduces the amount of applicants recruiters have to manually evaluate before hiring someone for a specific position reducing the time-to-hire.
Organized
Most resume screening programs come as part of applicant tracking system. These programs can store personal and contact information of different applicants categorized by their key skills and talents. If some candidate does not seem a good fit for some role at the current time, these programs keep their information in the system and can use it in future in case a relevant role opens that is a good fit for the candidate. This increases the pool of applicants and helps companies find a better fit for their roles.
Ethical Implications
Gender Bias
Ethnic Bias
Circumstantial Bias
Reducing Bias
References
- ↑ Bart Turczynski. 2021 HR Statistics: Job Search, Hiring, Recruiting & Interviews. Zety. https://zety.com/blog/hr-statistics#resume-statistics
