The credibility of a bias-accountability project depends on how its contributors treat each other.
Pledge · Standards · Scope · Enforcement · Attribution
Fair Code exists to expose and fix bias in AI systems - systems that make real decisions about real people's freedom, employment, and access to care. The credibility of that work depends on how we treat each other.
We pledge to make participation in this project a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
| ✅ We encourage | 🚫 We do not tolerate |
|---|---|
| Welcoming and inclusive language in issues, pull requests, and discussions | Harassment, personal attacks, or dismissive language of any kind |
| Respectful engagement with differing viewpoints - reasonable people disagree on fairness methodology | Sexualised language, imagery, or unwelcome advances |
| Honest, evidence-based criticism of technical approaches | Dismissing concerns about bias or harm as "just technical" or "not a real issue" - that is precisely the attitude this project exists to challenge |
| Humility about the limits of your own perspective; this work touches on race, criminal justice, and systemic harm | Doxxing or sharing anyone's private information without explicit consent |
| Full credit for datasets, prior research, and others' contributions | Claiming credit for others' work |
This Code of Conduct applies across all project spaces: GitHub issues, pull requests, discussions, and any public representation of this project.
Report violations to the project maintainer:
Yash Kewlani - @yakew7
All reports will be reviewed promptly and handled with discretion. Retaliation against anyone who raises a concern in good faith is itself a violation.
Consequences range from a warning to permanent removal from the project, depending on severity and pattern.
Adapted from the Contributor Covenant, version 2.1.