AI Screening Criteria Best Practices
EasySource AI
EasySource AI has modernized the recruitment process by improving efficiency and accuracy in candidate screening. Here are essential best practices for establishing effective AI screening criteria:
1. Limit Criteria to 5-7
While there's flexibility in adding criteria, we advise users to aim for 5 to 7 unless the role is highly specialized. We recommend including at least one criterion related to location, job title, years of experience, and work experience/skills for optimal screening effectiveness.
2. Clear and Simple Language
We recommend users to use language that is clear and straightforward for both AI systems and humans to comprehend
3. Derivable Inferences
Ensure criteria are designed to derive deep insights from candidates' profiles, work history, and industry experience. This includes skills, experiences, and company backgrounds.
4. Non-Conflicting Criteria
Avoid contradictory criteria. Each criterion should contribute positively to the candidate's evaluation without creating confusion.
5. Individual Criteria
Refrain from combining multiple requirements into one criterion. Each criterion should address a specific skill, qualification, or experience.
6. Categorization
Properly categorize each criterion (e.g., Skills, Experience, Education) to guide EasySource AI in conducting accurate research and inference.
7. Must-Have Criteria
If there are certain criteria that is compulsory for a candidate to have, then it must be marked as a must-have criteria. Candidates failing any must-have criterion will be marked as unqualified.
By adhering to these best practices, EasySource AI can effectively filter and identify top candidates, streamlining your hiring process and ensuring alignment with your organization's needs. This approach not only enhances efficiency but also improves the quality of hires, ultimately driving organizational success.