Coded Bias Documentary: Unequal Representation in AI Training Data

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Coded Bias
Artificial intelligence is being inculcated in numerous spheres of human welfare. For instance, it is being used to create advanced systems in healthcare, banks, education and other sectors. The success of artificial intelligence systems greatly depends on training data sets (Coeckelbergh, 21). If the training data is biased or does not represent people equally, the AI systems may make inaccurate predictions concerning the underrepresented populations. There is no doubt that such an outcome is quite desirable, especially in modern societies. Consequently, equal representation in AI training is essential.
Ensuring that training datasets are diverse and equally represent as many population groups as possible would help create equal representation. The algorithms in artificial intelligence depend on the training data sets to make accurate predictions. Therefore, if the dataset is made to be diverse, there is a higher likelihood that bias will be significantly reduced. Consequently, it is correct to conclude that equal representation in AI training datasets is a probable solution. Such representation would help reduce the kind of marginalization caused by using an inadequate training data set. For instance, the coded bias documentary identified cases where AI facial recognition systems could not correctly identify dark-skinned people (Public Broadcasting Service, 2022). There were also cases where the systems could not correctly identify the faces of women. These problems can potentially be solved by ensuring that the systems have more faces of dark-skinned persons and women in their training dataset.
Equal representation could help increase the level of trust that people have in AI systems. The reason for increased trust is that people would be convinced about the accuracy levels of the systems...