responsible-ai-guide
Resources for trustworthy, fair, and ethical AI research
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill responsible-ai-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Responsible AI Guide ## Overview A comprehensive collection of resources for building trustworthy, fair, and ethical AI systems. Covers fairness metrics, bias detection and mitigation, explainability methods, privacy-preserving techniques, robustness testing, and governance frameworks. Essential reading for researchers working on AI safety, alignment, and deploying models in high-stakes domains. ## Topic Taxonomy ``` Responsible AI ├── Fairness │ ├── Bias detection (data, model, outcome) │ ├── Fairness metrics (demographic parity, equalized odds) │ ├── Bias mitigation (pre/in/post-processing) │ └── Intersectional fairness ├── Explainability │ ├── Feature attribution (SHAP, LIME, IG) │ ├── Concept-based (TCAV, concept bottleneck) │ ├── Counterfactual explanations │ └── Mechanistic interpretability ├── Privacy │ ├── Differential privacy │ ├── Federated learning │ ├── Membership inference attacks │ └── Machine unlearning ├── Robustness │ ├── Adversarial attacks/defenses │ ├── Distribution shift │ ├── Uncertainty quantification │ └── Out-of-distribution detection ├── Safety & Alignment │ ├── RLHF and preference learning │ ├── Constitutional AI │ ├── Red teaming │ └── Guardrails and f
- Overview
- Topic Taxonomy
- Key Tools
- Fairness Assessment
- Reading Roadmap
- Use Cases
- References
What does the responsible-ai-guide skill do?
Resources for trustworthy, fair, and ethical AI research
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill responsible-ai-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.