A person wanted to use an AI model to predict the price movement of stocks. However the person chose to use data only from the largest companies as a few examples even though they wanted to use it to predict stock price movements of smaller companies. What is the type of bias described in the scenario?

Algorithmic bias refers to systematic and unfair discrimination that occurs when an algorithm produces results that are prejudiced due to erroneous assumptions in the machine learning process. While the AI may exhibit algorithmic bias depending on its design and training, the primary issue here is not the algorithm itself but rather the data selection process that led to a non-representative sample.

Confirmation bias is the tendency to search for, interpret, and remember information in a way that confirms one’s pre-existing beliefs or hypotheses. In the given scenario, the individual is not necessarily seeking to confirm biases but is rather making a poor choice in selecting data. Thus, confirmation bias does not accurately describe the issue at hand.

Measurement bias occurs when the data collected is inaccurate or flawed, affecting the validity of the analysis. Although measurement bias can influence results, the core problem in this scenario is the selection of the data set itself, not the accuracy of the measurements taken from it.