Decision-making systems may treat individuals unfairly or unequally based on membership to a category or a minority, resulting in disparate treatment and impact. Thus, many automated tools must consider the fairness of its decisions.

Machine Learning (ML) is the use of algorithms that can improve through past experiences. ML uses data from these past experiences to train a model that can make future predictions and decisions.

Many real-world processes are too difficult to study directly (due to uncontrollable external factors, difficulty to observe, etc.). Simulations are imitations of these complex processes that help shed light on the real-world process itself.

The knapsack problem is as follows: given a set of items with some value and weight, put items in a knapsack such that it does not exceed the weight limit but has the highest total value. The knapsack problem provides a framework to model many types of problems.

Queues are systems where there are multiple customers waiting in line to be served by multiple servers. Queueing theory studies these systems in terms of how long customers must wait and how long the lines will be.

Linear/Integer Programming (LP/IP) is the selection of a best decision among a set of possible decisions based on some criteria. LPs and IPs mathematically take the form of an optimization over some function given a set of constraints.