What Does Machine Bias Mean?
Machine bias is the effect of an erroneous assumption in a machine learning (ML) model. This type of bias causes machine learning algorithms to produce sub-optimal outputs that have the potential to be harmful.
Machine bias is often the result of a data scientist or engineer overestimating or underestimating the importance of a particular hyperparameter during the algorithmic tuning process. A hyperparameter is a machine learning parameter whose value is chosen before the learning algorithm is trained. Tuning is the process of selecting which hyperparameters will minimize a learning algorithm's loss functions and provide the most accurate outputs.
Techopedia Explains Machine Bias
Machine learning algorithms use what they learn during training to make predictions about new input. When some types of information are mistakenly assigned more importance than they deserve, the algorithm's outputs will be sub-optimal.
For example, machine learning software is used by court systems in some parts of the world to recommend how long a convicted criminal should be incarcerated. Studies have found that when data about a criminal's race, education and marital status are weighted too highly, the algorithmic output is likely to be biased and the software will recommend significantly different sentences for criminals who have been convicted of the same crime.