AI Fairness 360
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Overview
HEALTH SCORE
Concerning*
Partial score (2/3 categories)
22out of 65
Maintainer coverage is the lead risk. Development cadence is present but not enough to offset the structural risk. Security & Supply Chain is unavailable, so weights were redistributed.
LIFECYCLE
Active
Consistent commits, responsive maintainers, regular releases, and healthy issue triage.
Health breakdown
Concerning*(22/65)
Maintainer availability, known risks and supply chain posture, and whether the project is actively developed or appropriately stable.
Maintainer responsiveness
Median response time is 237 days, but most repositories have response times over 90 days.
Bus factor
Only 2 active maintainers currently have merge rights.
Organization Diversity
Contributors span 41 organizations.
Project Achievements
Leaderboards
About
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
First commit
Software value
$2.9M
Maturity
Incubating
Tags
ai
artificial-intelligence
bias
bias-correction
bias-detection
bias-finder
bias-reduction
codait
deep-learning
discrimination
fairness
fairness-ai
fairness-awareness-model
fairness-testing
ibm-research
ibm-research-ai
machine-learning
python
r
trusted-ai
License(s)
Apache-2.0
Data sources
Git
Hacker News
GitHub