Algorithm

Full definition

In AI, the term "algorithm" usually refers to a learning algorithm (e.g., gradient descent, k-means, random forest) — the procedure that produces a model from data. The "algorithm" is the recipe; the "model" is the trained artifact. Confusing the two leads to imprecise governance: regulators audit specific deployed models, not abstract algorithms.

Why it matters

Most public debate about "algorithmic bias" or "algorithmic accountability" is really about deployed models and the data they were trained on, not the math itself. Distinguishing algorithm from model sharpens both engineering and regulatory work.

Example

XGBoost is an algorithm; the credit-risk model trained on a bank's loan history using XGBoost is a specific model that gets audited and deployed.

Related terms

• Model
  An AI model is the trained artifact — a specific set of numerical weights plus an architecture — produced when a learning algorithm runs against training data, and the thing that actually gets deployed and audited.
• Machine Learning (ML)
  Machine Learning (ML) is the subfield of AI in which algorithms improve their performance on a task by learning statistical patterns from data, rather than following rules a human wrote by hand.
• Training Data
  Training data is the curated dataset used to teach a machine-learning model — every pattern the model can recognize, every bias it inherits, and every limit to its accuracy ultimately traces back to this data.
• Algorithmic Accountability
  Algorithmic accountability is the principle that a specific person, role, or organization is identifiable and answerable for the design, deployment, outcomes, and harms of an automated decision system — and that the mechanisms to enforce that answerability exist.