AI-Assisted Resume Screening Tool
Description
This use case intends to solve the problem of human bias during resume reviews and the time-intensive process of reviewing candidate resumes.
Detailed example
The evaluation model compares each resume against the associated job requirements and provides a numerical score, a scoring group (red, yellow, green, or blue), related experience, and missing experience. The scoring group categorizes candidates based on the percentage of matching experience, with red indicating a weak candidate, yellow indicating moderate alignment, green indicating a strong candidate, and blue indicating that the system was unable to score the resume due to issues such as missing documents.
AI / analytics pattern
Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
This solution applies the same review criteria to every candidate’s resume. This reduces human cognitive bias and variability in how HR specialists evaluate candidate resumes. Additionally, this solution speeds up the time-to-hire by reducing the amount of time spent conducting manual candidate resume reviews.
Controls / human review
ATO: No; PIA: https://www.dhs.gov/sites/default/files/2025-03/25_0331_priv_pia-dhs-all-043a-talentacquisition-appendix-update.pdf
Data needed
OpenAI's GPT-4 is trained on common crawl and publicly available data. ICE does not provide any training data and uses the pre-trained base models as is. Pre-trained models used do not require training data. Human-evaluated resumes are compared to tool output for validation. Production data will be candidate resumes.