Bias in Machine Learning

Bias in Machine Learning ?

Tom Mitchell first introduced the term bias in 1980 in his paper titled, “The need for biases in learning generalizations”. Machine learning bias, also known as algorithm bias or AI bias occurs when an algorithm gives results and observations that are systematically prejudiced due to incorrect and wrong assumptions in the machine learning process.

Algorithms can have inherent, built-in and deep-rooted biases as they have been created, potentially amplified and multiplied by individuals who have subjective and conscious or unconscious preferences which are only discovered when the algorithms are put to use.

The system’s inclusion and drawing of an improper collection of data sets result in high bias. Biases which can be accidentally or inadvertently applied to algorithms are bandwagon effects, confirmation bias, and stereotyping, priming and selective perception.

Shortage and lack of complete or truly random data can also cause bias because machine learning algorithms rely on the accuracy of data and pattern recognition. Careful removal and outcasting of harmful biases is crucial machine learning also involves business implications and operations like loan approvals, selection of candidates for a job interview as well as personal implications, such as a routine health check-up or a medical diagnosis.

A prime example of bias was the facial recognition feature of Google where it totally failed to recognize some faces based on racial characteristics tagging them either as inhuman or ignoring them completely. Bias can be present at any level because machine learning is a process that has many layers to it. Data scientists should always be in a surge to minimize the underlying biases present and it would still be possible that there are biases.

An extensive and comprehensive approach is needed to deal with it. The various levels at which biases could present themselves are information assortment, evaluation, modelling, preparation and data preparation.

Assumptions are also another source that bias stems from. It cannot be denied that avoiding bias and eliminating them can help in the generalization of the data set and giving it a more objective outlook.

Fair research with an objective approach can have great and grave implications in the shaping up and development of policies and also in the way machine learning grows in the coming future.