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Technical mechanisms and engineering interventions that directly modify how an AI system processes inputs, generates outputs, or operates, including changes to models, training procedures, runtime behaviors, and supporting hardware.
Technical approaches to reduce LLM hallucinations - instances where AI models generate false or unsupported information while appearing confident in their responses
Reasoning
Generic technical approaches to reduce hallucinations span both model and non-model mechanisms; insufficient specificity to distinguish L2 subcategory.
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 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 OtherContent Moderation
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.
1.2.1 Guardrails & FilteringMake 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.
Verify and Validate
Testing, evaluating, auditing, and red-teaming the AI system
Developer
Entity that creates, trains, or modifies the AI system
Manage
Prioritising, responding to, and mitigating AI risks
