Discrimination, Exclusion and Toxicity

Mitigation strategy

- Systematically curate and balance all training and fine-tuning datasets by employing data augmentation, filtering, and sensitive data masking techniques to mitigate the encoding of societal and cultural biases - Integrate alignment-aware optimization, such as Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO), to cultivate a preference for respectful and non-discriminatory language generation during the model training process - Implement robust post-deployment safety layers, including real-time toxicity classifiers, automated adversarial red-teaming, and controlled text generation guardrails, to detect and prevent the propagation of biased or exclusionary outputs at inference time