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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.
Algorithmic fairness refers to ensuring AI systems and models do not perpetuate or amplify societal biases based on protected characteristics (like race, gender, class), through technical approaches (fairness metrics, bias mitigation) and policy measures (affirmative safety requirements, model cards).
Reasoning
Establishes fairness and ethics principles as design standards governing model development and organizational policies.
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 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 & PolicyGlobal 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.
Build and Use Model
Training, fine-tuning, and integrating the AI model
Developer
Entity that creates, trains, or modifies the AI system
Govern
Policies, processes, and accountability structures for AI risk management
