Garbage in, garbage out
"Garbage in, garbage out" (GIGO) is a concept that states poor-quality or flawed input will produce a poor-quality or flawed output. This principle applies to computer science, mathematics, and decision-making systems, emphasizing that the validity of a result is dependent on the quality of the data or information it's based on.
The GIGO principle is applicable to any system that relies on input data, including decision-making processes, analytical programs, and even large language models (LLMs). For example, inaccurate sales data will lead to flawed demand forecasts, and poor-quality training data will result in a less reliable AI.
The expression was popular in the early days of computing. The first known use is in a 1957 syndicated newspaper article about US Army mathematicians and their work with early computers, in which an Army Specialist named William D. Mellin explained that computers cannot think for themselves, and that "sloppily programmed" inputs inevitably lead to incorrect outputs. The underlying principle was noted by the inventor of the first programmable computing device design:
On two occasions I have been asked, "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" ... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.
This phrase can be used as an explanation for the poor quality of a digitized audio or video file. Although digitizing can be the first step in cleaning up a signal, it does not, by itself, improve the quality. Defects in the original analog signal will be faithfully recorded, but might be identified and removed by a subsequent step by digital signal processing.
The phrase may also be used in the context of machine learning, where poor-quality training data will inevitably lead to a poor-quality model.
