Overview
What this challenge is about.
Train two models on a synthetic applicant dataset, measure fairness gaps, run two mitigations, and write a recommendation. Get a verifiable certificate.
The scenario
The IT services firm (around 180,000 employees, around 400,000 applicants per year) is under regulatory pressure to demonstrate its candidate-screening tooling does not produce adverse impact across protected groups.
The Brief
What you'll do, and what you'll demonstrate.
Audit a hiring-screening classifier for subgroup performance gaps and recommend whether to ship, mitigate, or escalate.
Earning criteria — what you'll demonstrate
- Compute and interpret common group-fairness metrics on a real classifier
- Apply simple bias-mitigation techniques and measure their effect
- Recognize when a model's performance gap is large enough to block deployment
- Communicate audit findings to a non-technical client stakeholder
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Machine Learning (Undergraduate)
Bachelor · Machine Learning
Strong alignment
This challenge maps to Machine Learning (Undergraduate) at the Bachelor level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Fairness Metrics
Apply fairness metrics to solve real industry problems and demonstrate production-level capability.
- Logistic Regression
Apply logistic regression to solve real industry problems and demonstrate production-level capability.
- Random Forest
Apply random forest to solve real industry problems and demonstrate production-level capability.
- Bias Mitigation
Apply bias mitigation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Safety Researcher
Running a fairness audit, comparing mitigations, and translating findings into a ship/mitigate/escalate call is exactly the work entry-level AI safety researchers do at consultancies and in-house responsible-AI teams.
This challenge sharpens
- fairness-metrics
- bias-mitigation
- model-evaluation
Data Scientist
Pairing model training with subgroup analysis and clear stakeholder communication is the modern data scientist's job description in any regulated industry.
This challenge sharpens
- logistic-regression
- random-forest
- model-evaluation
Applied AI Scientist
Quantifying the accuracy/fairness trade-off honestly and recommending a path forward mirrors the daily work of applied AI scientists supporting product teams.
This challenge sharpens
- bias-mitigation
- fairness-metrics
- python