When the candidate grading functionality is enabled, Dayforce uses artificial intelligence (AI) to assign a grade to candidates when they apply for jobs at your organization. These grades are displayed in the Recruiting feature, along with a report card with additional details.
Note: The candidate grading functionality is only used for external candidates. Internal candidates aren't assigned a grade when they apply. The system doesn't display candidate grades for candidates who reside in New York City, to comply with New York City legal requirements.
The grading AI uses your organization's hiring practices to provide more accurate grading, meaning that recruiting and hiring managers might spend less time needing to manually screen candidates.
To help the grading AI learn about past hiring practices, you can initiate a one-time learning process to analyze historical recruiting data, such as job application resumes, job requisition details, job posting details, and job description details. This process creates a pool of historical candidates against which candidates can be compared. To initiate this one-time process, click the Learn from Historical Data button in the Candidate Grade Enabled client property in Recruiting Setup > Client Properties. See Configure Candidate Grading.
Note: If your organization doesn't have historical recruiting data available, the AI grades candidates initially based solely on how closely their job application matches the job description. It then builds historical data going forward.
The candidate grading AI evaluates candidates on factors such as the following:
- How closely their job application matches the job description, using keyword matching. The accuracy of the grades will vary depending on the role and the quality of the job description.
- Years of experience:
- The candidate’s total work history is compared against that of historical candidates.
- The candidate's experience in similar roles is compared against that of historical candidates.
- The candidate’s background is evaluated for how it aligns with the experience defined in the job description.
Note: You can't modify the factors used by the candidate grading AI scoring system. This is by design, to ensure that the machine-learning models evaluate candidates consistently, based on merit and with reduced bias.
The AI evaluates how your organization uses candidate statuses to screen and move candidates through the recruiting process. To do this, it accesses the following data:
- The Progress Indicator column of the Candidate Status feature.
- The Decline Reason Type column of the Decline and Offer Rejection Reason feature.
It uses this data to adjust its scoring system. For example, it identifies the desired qualities of good candidates who are moved forward in the hiring process (positive training events). It also uses mapped statuses to identify the undesired qualities of candidates who are declined without having moved into the interview process (negative training events). Learning is activated when there's enough recruiting data for a job requisition, typically 20 positive training events and 20 negative training events.
The following steps are taken to avoid introducing bias during the grading process:
- Hiring Status: Historical recruiting data is considered for all candidates, not just for those who were hired.
- Name: Candidate names are removed from job applications before they are screened to help avoid implicit bias.
Grade Elements Displayed
The grade elements that are displayed in a candidate's report card depends on how much training data was available for the job requisition at the time of grading. If the 20 positive and 20 negative training events have yet to occur, some of the grading elements might not be displayed. The weight attributed to each element is also dependent on the recruiting data available at the time of grading and is based on the likelihood that a certain element will lead to an interview.