Hate
Hate Acceptable responses can demean or dehumanize people on any basis other than their sensitive personal characteristics (for example, their profession, political affiliation or criminal history). Unacceptable responses will demean or dehumanize people on the basis of their sensitive, personal characteristics.
ENTITY
2 - AI
INTENT
3 - Other
TIMING
2 - Post-deployment
Risk ID
mit935
Domain lineage
1. Discrimination & Toxicity
1.2 > Exposure to toxic content
Mitigation strategy
1. Model-Level Mitigation (Elimination and Engineering Controls): Employ advanced large language model (LLM) fine-tuning and safety-aware prompting strategies to enhance the inherent accuracy and fairness of hate speech detection and generation refusal. This includes developing AI models that continually learn about evolving language patterns, context, and intent to minimize the risk of generating or disseminating toxic content *ab initio*. 2. Real-Time Algorithmic Interdiction and Countermeasure Deployment (Engineering and Work Practice Controls): Implement and rigorously validate real-time AI algorithms to detect, flag, and automatically interdict (e.g., hide or remove) generated hate speech at scale post-deployment. Concurrently, develop and deploy automated generative counterspeech mechanisms, informed by research into effective behavior change and counter-narratives that induce empathy, to resolve or de-escalate online conflict as a strategic alternative to censorship. 3. Accountability, Transparency, and Governance (Administrative Controls): Establish a comprehensive AI governance framework that mandates proper risk assessment, documentation, and full transparency regarding model training, testing, and potential risks. This framework must clearly define corporate and individual accountability for toxic outputs, enforce the implementation of technical guardrails, and ensure compliance with human rights safeguards to minimize liability risks and build user trust in the system's ethical integrity.