Artificial Intelligence

Artificial intelligence (AI) is the broad name for computer systems that turn data into predictions, recommendations, or decisions. Under the National Artificial Intelligence Initiative Act, the term "means a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments" (15 U.S.C. 9401(3)). The statute adds that such systems use machine and human-based inputs to "perceive real and virtual environments," to "abstract such perceptions into models through analysis in an automated manner," and to "use model inference to formulate options for information or action."

The NIST framing

NIST's AI Risk Management Framework (NIST AI 100-1, 2023) describes an AI system as "an engineered or machine-based system that can, for a given set of objectives, generate outputs such as predictions, recommendations, or decisions influencing real or virtual environments." It adds: "AI systems are designed to operate with varying levels of autonomy."

Families of AI

The term covers several technologies, each with its own entry:

• Machine Learning: systems that learn from data.
• Deep Learning: machine learning built on many-layered neural networks.
• Natural Language Processing and Large Language Model: systems that work with text.
• Computer Vision and Optical Character Recognition: systems that work with images.

GAO separates generative AI, which creates new content, from "conventional AI," which "does tasks of classification and prediction, such as identifying objects in a photograph or forecasting a storm" (GAO-24-106946, 2024).

How AI risk differs from software risk

NIST lists risks that are "new or increased" compared with traditional software (AI 100-1, Appendix B), among them:

• "The data used for building an AI system may not be a true or appropriate representation of the context or intended use of the AI system, and the ground truth may either not exist or not be available."
• "Increased opacity and concerns about reproducibility."

NIST also warns that deploying AI systems "which are inaccurate, unreliable, or poorly generalized to data and settings beyond their training creates and increases negative AI risks and reduces trustworthiness."

Human roles

NIST notes that "Human-AI configurations can span from fully autonomous to fully manual." Some AI systems "may not require human oversight," while "Other systems may specifically require human oversight."

Why it matters for land and mining claim records

Land and mining claim records are legal records: a recorded document, a filing date, a case status. An AI output about them is a prediction about the record, not the record. The answer that counts sits in the recorded document and the official case file, and an AI result is useful only when a person can check it against that source.