Beginner
What is AI safety?
AI Safety is the interdisciplinary field and set of practices focused on preventing harm from AI systems. It includes reliability, misuse prevention, bias and error mitigation, alignment with human values, and analysis of both near-term and long-term risks.
How is risk different from harm?
Use risk for potential adverse outcomes before or during assessment; use harm for the adverse effect that has occurred or may be concretely described.
What is a safety case for AI?
An AI safety case is a documented argument, supported by evidence, that an AI system is acceptably safe for a defined use and operating context. It is especially relevant for higher-risk sectors where assurance must be explicit, reviewable, and maintained over time.
What is model drift?
Model drift is the degradation or change in model performance over time as real-world data, user behavior, or operating conditions diverge from the data used during training or evaluation. Detecting drift is important for deciding when to retrain, recalibrate, or replace a model.
Intermediate
How is data drift different from concept drift?
Use Data Drift when input data distribution changes; use Concept Drift when the relationship between inputs and the target outcome changes.
What is alignment in AI safety?
Alignment is the practice of designing, training, and governing an AI system so that its goals, outputs, and behavior remain consistent with human intentions, values, and acceptable use constraints.
What is a serious AI incident?
An AI Incident is an event or series of events in which the development, deployment, use, or malfunction of an AI system causes or could cause harm. Such harm may involve safety, discrimination, privacy, legal compliance, critical infrastructure, property, communities, or the environment.
What is human-in-the-loop control?
HITL, or human-in-the-loop, is an approach in which people remain involved in shaping, reviewing, approving, or improving AI system outputs or decisions. It is used to combine machine efficiency with human judgment, especially in higher-risk or context-sensitive tasks.
What is AI toxicity?
AI Toxicity is harmful, offensive, false, biased, or otherwise inappropriate output generated or amplified by an AI system. It may arise from training data, system design, prompt context, misalignment, or inadequate safeguards.
Why does post-market monitoring matter?
A post-market monitoring system consists of activities by AI system providers to collect and review experience from systems placed on the market or put into service. Its purpose is to identify whether corrective or preventive action is needed after deployment.
Advanced
What is anomaly detection in AI monitoring?
Anomaly detection is the process of identifying observations, events, or patterns that differ significantly from expected behavior. In AI and data analysis, it is used to flag outliers that may indicate errors, fraud, security incidents, system faults, or other unusual conditions.
What is security by design for AI systems?
Security by design is an approach in which security requirements and safeguards are integrated from the start of system development. For AI systems, it includes anticipating misuse, protecting data and models, and embedding access controls, monitoring, and resilience throughout the lifecycle.