Bias Audits
Audit AI models for gender, regional, tribal, and linguistic bias
Gender Bias Audit — Q2 2026
gendercompletedNo significant gender bias detected. AI scores correlated equally across gender groups (p=0.34). Communication competency scoring slightly favors structured responses.
Mitigation Actions
• Monitor communication scoring for gender-neutral phrasing
• Review question bank for gender-balanced scenarios
Regional Bias Assessment — Copperbelt vs Other Provinces
regionalcompletedMinor regional bias detected. Candidates from Northern and Muchinga provinces scored 4-6% lower on average, primarily due to English proficiency differences in scenario questions.
Mitigation Actions
• Add Bemba and Nyanja language options for all scenarios
• Adjust scoring rubric for non-native English speakers
• Review question phrasing for regional neutrality
Tribal/Linguistic Bias Screening
tribalcompletedNo tribal or linguistic bias detected. AI assessments showed equal scoring across major Zambian language groups. Multi-language interview support contributing to fairness.
Mitigation Actions
• Continue expanding local language support
• Monitor Lozi and Kaonde interview outcomes
Educational Background Bias Check
educationalremediation requiredModerate bias detected. Candidates from UNZA scored 8% higher on average than private university graduates. Diploma holders were disadvantaged in technical competency scoring.
Mitigation Actions
• Re-calibrate technical scoring weights
• Add diploma-equivalent competency benchmarks
• Review university-name detection in scoring model
Age Bias Analysis — Hiring Patterns
agecompletedNo significant age bias. Scoring was consistent across age groups (22-55). Slight advantage for mid-career professionals in leadership competency.
Mitigation Actions
• Continue monitoring age-related scoring patterns
• Ensure entry-level assessments dont disadvantage younger candidates