Data Bias in Machine Learning: Implications for Social Justice

Machine learning and artificial intelligence have taken organizations to new heights of innovation, growth, and profits thanks to their ability to analyze data efficiently and with extreme accuracy. However, the inherent nature of some algorithms such as black-box models have been proven, at times, to be unfair and lack transparency, leading to multiplicated bias and detrimental impact on minorities. There are several key issues presented by black-box models, and they all work together to further bias data. The most prominent are models fed with data that is historically biased to begin with, and fed by humans who are biased by nature. In addition, because data analysts can only see the inputs and outputs but not the internal workings of how results are determined, machine learning is constantly aggregating this data, including personal data.