1 / 5
What is an AI system, really?
In this lesson
What is an AI system, really?
Classify AI system components according to the NIST AI RMF lifecycle phases (Application Context, Data and Input, AI Model, Task and Output, Operate and Monitor) and identify which
You'll be able to
- Classify AI system components according to the NIST AI RMF lifecycle phases (Application Context, Data and Input, AI Model, Task and Output, Operate and Monitor) and identify which AI actors perform design, development, deployment, and monitoring tasks at each phase[^4][^6].
- Explain how AI systems differ from traditional software by articulating the interdependencies between lifecycle activities, the limited visibility AI actors have across phases, and how early design decisions alter system behavior in ways that cannot be fully anticipated at deployment[^6].
- Apply the functional AI system definition (input → trained model → inference → output) to evaluate whether a given technology qualifies as an AI system, distinguishing pre-trained models subject to monitoring[^1] from conventional rule-based software.
- Evaluate AI system documentation for completeness by verifying it addresses knowledge limits, human oversight mechanisms, and sufficient information for AI actors to make informed decisions during operation[^5], and assesses whether the system mitigates social biases and sets appropriate user expectations about capabilities and error rates[^7].
- Create a risk communication plan for an AI system deployment that specifies how incidents and errors will be reported to relevant stakeholders[^2], incorporates contingency processes for third-party AI system failures[^6], and accounts for AI-specific risks (harmful bias, generative AI challenges, machine learning attacks) that existing cybersecurity frameworks do not comprehensively address[^3].