A team of scientists from the Laboratory of Informatics and Artificial Intelligence MIT has published a paper dedicated to the fight against the misconceptions that arise in neural networks in the learning process. The main attention is paid to the problem of preserving the accuracy of predicted AI results.
Since scientists have been trying to cope with the problem of discriminatory misconceptions of AI for the first year, there are traditional methods of operations in this area. Usually, for correcting training, a certain amount of information is added to the data set, which allows the neural network to obtain more accurate data on a particular sample.
Thus, in one experiment, the AI should have noted the expected level of income of individuals in the presented selection. As a result of a discriminatory misconception that appeared in the process of learning, AI twice as often marked men as individuals with high incomes. Increasing the number of female profiles in a training dataset allowed us to reduce the error by 40%.
The problem with traditional methods is that the data sets prepared in this way do not reflect the actual distribution of the population. This increases the fallacy of predictions issued by AI.
In their work “Why Is My Classifier Discriminatory?” Scientists offer several possible solutions to the problem. They believe that increasing the size of the training dataset without changing the proportions of the represented gender, social and racial groups will allow the AI to cope with discriminatory errors independently. According to the researchers, the collection of additional information from the same source that provided the initial data packet will avoid covariant bias.
This method can be costly, as it will have to pay for the work of specialists marking up additional data. However, researchers are confident that in many cases such costs will be justified.
The second option is clustering groups of the population most vulnerable to discrimination and the subsequent separate processing of these clusters with the introduction of additional variables. Scientists suggest using this method when obtaining additional data is difficult or impossible.