Challenge Area:
Workplace gender bias in hiring.

Problem Statement:
Unconscious bias in recruitment processes limits opportunities for women in STEM fields.

Solution Concept:
Develop an AI tool that anonymizes resumes and uses fairness-aware algorithms to rank candidates based on skills, reducing bias in hiring decisions.

Expected Impact:
Increase female hires in STEM by 15% across 50 companies.

Next Steps:
Train models on diverse job application datasets, integrate with HR platforms, and conduct bias audits.

Challenge Area:
Workplace Bias

Problem Statement:
Gender biases in hiring processes persist due to subjective resume evaluations, limiting opportunities for women.

Solution Concept:
Design an AI-based resume screening tool that anonymizes applications and uses NLP to evaluate qualifications objectively, with fairness metrics to detect bias. The PoC includes data preprocessing for unstructured resume data and modular code for bias auditing.

Expected Impact:
Increases female hiring rates by 25% in tech industries, promoting workplace equity.

Next Steps:
Expand to other underrepresented groups, integrate with HR platforms, and enhance bias detection algorithms.