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Input validation, output filtering, and content moderation classifiers.
Also in Non-Model
Content moderation systems enable detecting and filtering toxic content (hate speech, harassment, misinformation) in real-time on digital platforms, while maintaining transparency in moderation decisions.
Reasoning
Technical system filters toxic content in real-time through content moderation classifiers operating on platform inputs/outputs.
Reduce Hallucinations
Reduce hallucination refers to techniques and methods used to minimize AI systems' tendency to generate false or fabricated information, addressing a critical challenge where language models produce inaccurate facts or citations that could spread misinformation.
1 AI SystemMitigate Hallucinations
Technical approaches to reduce LLM hallucinations - instances where AI models generate false or unsupported information while appearing confident in their responses
1 AI SystemDetecting AI-Generated Content
Detecting AI-generated content involves technical methods and tools to identify whether content was created by artificial intelligence or humans, primarily through watermarking, linguistic analysis, and machine learning approaches.
1.2.5 Provenance & WatermarkingRisks from Persuasion
Risk that AI systems can systematically influence human beliefs and behaviors through sustained, personalized interactions by exploiting cognitive biases and adapting in real-time, enabling large-scale manipulation without human intervention.
99 OtherMake AI Manipulation Use Illegal
Legal framework to criminalize the malicious use of AI for manipulation of individuals or groups, including the creation and deployment of deepfakes and automated influence campaigns.
3.1.1 Legislation & PolicyAnonymizing Writing Style with LLM Rewrites
LLMs can be used to rewrite text to anonymize an author's writing style, helping protect against AI systems that can identify writers with high accuracy based on their linguistic patterns.
1.1.3 Capability ModificationGlobal Risk and AI Safety Preparedness (GRASP)
Hodes, Cyrus; Salem, Fadi; Corruble, Vincent; Ségerie, Charbel-Raphaël; Claybrough, Jonathan; Veron, Thibaud; Majid, Zainab; Fan, Jinyu; Lorin, Amaury (2025)
Project GRASP (Global Risk and AI Safety Preparedness) is a comprehensive database mapping AI risks and mitigation solutions. The initiative addresses both endogenous risk (autonomous AI systems that behave outside of human supervision) and exogenous risk (the human misuse of those AI systems). The platform serves policymakers, researchers, and industry leaders by providing tools required to identify risks, understand solutions, and find innovations.
Operate and Monitor
Running, maintaining, and monitoring the AI system post-deployment
Deployer
Entity that integrates and deploys the AI system for end users
Manage
Prioritising, responding to, and mitigating AI risks