Results for "tracked risks"

Privacy risk analysis under GDPR-like laws.

Central log of AI-related risks.

Framework for identifying, measuring, and mitigating model risks.

Categorizing AI applications by impact and regulatory risk.

AI used in sensitive domains requiring compliance.

US framework for AI risk governance.

Restricting distribution of powerful models.

Risk threatening humanity’s survival.

Ensuring model behavior matches human goals, norms, and constraints, including reducing harmful or deceptive outputs.

Protecting data during network transfer and while stored; essential for ML pipelines handling sensitive data.

Rules and controls around generation (filters, validators, structured outputs) to reduce unsafe or invalid behavior.

Policies and practices for approving, monitoring, auditing, and documenting models in production.

Systematic review of model/data processes to ensure performance, fairness, security, and policy compliance.

Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.

Central catalog of deployed and experimental models.

Required human review for high-risk decisions.

Legal or policy requirement to explain AI decisions.

Incrementally deploying new models to reduce risk.

Compromising AI systems via libraries, models, or datasets.

Model exploits poorly specified objectives.

International AI risk standard.

Classifying models by impact level.

AI used without governance approval.

Artificial environment for training/testing agents.

Patient agreement to AI-assisted care.

AI-driven buying/selling of financial assets.

Stored compute or algorithms enabling rapid jumps.

Intelligence and goals are independent.

Isolating AI systems.

International agreements on AI.