XTRACTIS for Human Resources
Discovery of Discriminatory Biases in the Professional Evaluation of Employees: Gender & Age Bias
Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks
This use case is an illustrative example of XTRACTIS’ ability to reveal conscious or unconscious biases in high-risk critical decisional processes, in this case age and gender biases.
Goals & Benefits
Identify the specific parameters characterizing each employee and which are significant in his or her manager’s evaluation process.
Reveal the cause-and-effect relationships between these parameters and the managers’ decision strategies.
Improve HR management knowledge by helping companies understand their managers’ decision strategies.
Provide the regulator with a tool to check social compliance on a case-by-case basis.
XTRACTIS-induced Decision System
Intelligible Model, Explainable Decisions
- The top-model is a decision system composed of 14 rules without chaining.
- Each rule uses from 2 to 4 predictors among the 8 variables that XTRACTIS automatically identified as significant in the decision process (out of the 20 Potential Predictors).
- Three rules are discriminatory as they clearly state that a specific gender or age status is a systematic decision criterion.
High Predictive Capacity
It has a good Real Performance (on unknown data).
Ready to Deploy
It computes real-time predictions up to 70,000 decisions/second, offline or online (API).
Intelligibility x Performance Scores Benchmarks
LoR = Logistic Regression RFo=Random Forests
BT=Boosted Trees
NN=Neural Networks
Detailed results and explanations in full document
Use Case 2025/06 (v3.0)
Powered by XTRACTIS® REVEAL v13.2.54101 (2024/12)
CONTENTS
- Problem Definition
- XTRACTIS-induced Decision System
- XTRACTIS Process
- Top-Model Induction
- Explained Predictions for 3 unkown cases
- Top-Models Benchmark
- Quantitative Metrics
