A Hybrid NLP-Expert System Framework for Heuristic Scoring and Fairness-Driven Resume Evaluations
Bibliographic record
Abstract
Automated resume screening tools have become popular because they save time and make the hiring process more efficient. However, these tools have sparked concerns around fairness and transparency, as there is a risk that they could reinforce existing biases. To address this issue, we are proposing a hybrid framework that combines straightforward content evaluation with sentiment analysis and explicit fairness rules. Our approach uniquely integrates a well-known sentiment analysis tool called VADER, which helps gauge the positivity of language used in resumes, alongside a simple measure of information each resume provides. Additionally, the framework incorporates an expert system that implements specific fairness rules to identify and adjust for potential biases. In the experiment with a real set of resumes, the framework confirmed its ability to effectively rank candidates based on their qualifications while also identifying and reducing bias in the evaluation process. The results revealed that most resumes received high fairness scores, showing minimal bias issues. Overall, our hybrid framework demonstrates a practical way to make automated resume evaluations more transparent, fair, and balanced by combining NLP tools with human-inspired fairness rules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".