IFRI-AI-Classes/ifri_mini_ml_lib
Apr 2025 – May 2025
2 Months active
Languages Used
Python
Technical Skills
Data AnalysisData ScienceFairness MetricsFairness in AIMachine LearningPython
Ariane AGBOTON
PROFILE
Ariane Agboton
Worked on the IFRI-AI-Classes/ifri_mini_ml_lib repository to enhance fairness evaluation in machine learning pipelines by developing a custom suite of fairness metrics and expanding per-group analytics. Leveraged Python and data science techniques to implement Fairlearn-style metrics from scratch, including demographic parity and equalized odds, while improving typing and edge-case handling for robustness. Further strengthened the evaluation process by refining the test suite, introducing realistic data fixtures, and validating custom metrics against industry standards. The work focused on bias auditing, risk reduction, and reproducibility, resulting in a more reliable and maintainable toolkit for assessing fairness in AI models.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
1,020
Activity Months2
Your Network
11 peopleShared Repositories
11
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly performance summary for IFRI-AI-Classes/ifri_mini_ml_lib focused on strengthening model fairness evaluation through test-suite enhancements and robust fixtures. No major feature regressions observed; aligned testing with industry standards and improved reproducibility across datasets.
2 Commits • 1 Features
May 1, 2025May 2025 monthly performance summary for IFRI-AI-Classes/ifri_mini_ml_lib focused on strengthening model fairness evaluation through test-suite enhancements and robust fixtures. No major feature regressions observed; aligned testing with industry standards and improved reproducibility across datasets.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary: In IFRI-AI-Classes/ifri_mini_ml_lib, delivered a major enhancement to the fairness metrics toolkit for ML model evaluation, including a custom fairness metrics suite, per-group analytics, and improved typing/edge-case handling. The work strengthens bias auditing capabilities, supports governance and risk reduction for deployed models, and demonstrates strong execution in Python-based metric design and integration with evaluation pipelines.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary: In IFRI-AI-Classes/ifri_mini_ml_lib, delivered a major enhancement to the fairness metrics toolkit for ML model evaluation, including a custom fairness metrics suite, per-group analytics, and improved typing/edge-case handling. The work strengthens bias auditing capabilities, supports governance and risk reduction for deployed models, and demonstrates strong execution in Python-based metric design and integration with evaluation pipelines.
Activity
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Quality Metrics
Correctness87.6%
Maintainability87.6%
Architecture85.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data AnalysisData PreprocessingData ScienceFairness MetricsFairness in AIMachine LearningPythonTesting
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