- 🧠 Machine unlearning for foundation models
- 🧭 Bias discovery and mitigation in large-scale foundation models
- 🛡️ Adversarial attacks and defenses in foundation models
- 📏 Robustness and generalization in trustworthy foundation models
- ⚖️ Fairness-aware training and evaluation
- 🔍 Explainability and interpretability for large/black-box models
- 🧪 Ethical concerns and responsible deployment
- 📊 Benchmarks for foundation model trustworthiness
- 📉 Uncertainty quantification
- 🔐 Data provenance and security