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It is known that algorithms often reproduce the mistakes and prejudices of their creators, but the opposite can also occur: a biased AI can influence human decision-making even when the AI is no longer present. This is revealed by experiments carried out at the University of Deusto.<\/strong><\/p>\n <\/p>\n Enrique Sacrist\u00e1n\u00a0<\/a>\/ SINC<\/a><\/p>\n A study conducted by psychologists Luc\u00eda Vicente<\/strong> and Helena Matute<\/strong> from the University of Deusto, in Bilbao, shows that we humans can inherit biases from artificial intelligence (AI). The study is published in the open access journal Scientific Reports.<\/em> The impressive results achieved by these new technologies reflect their high reliability and have become very popular. However, bias in an algorithm is defined as a systematic error.<\/p>\n These programs are trained with data that are the product of human decisions. If the data contain biases, artificial intelligence models trained with these data will learn and reproduce those biases. In fact, there is already sufficient evidence that AI indeed inherits and amplifies human biases.<\/p>\n This situation was known, but now the new study shows that the opposite effect can also occur: we humans could inherit biases from AI, which means there is a risk of entering a rather dangerous loop.<\/p>\n For their research, the authors set up a series of experiments where volunteers had to perform a fictitious medical diagnosis task<\/strong> using a matrix with two colours, dark and light, which represented human tissue samples<\/strong> obtained from patients affected by a fictitious syndrome.<\/p>\n <\/p>\n Thus, they created a classification criterion, so that a greater proportion of dark cells meant that the tissue sample was affected by the disease. It was described as positive<\/strong> whereas if it had a greater proportion of light cells, then it should be classified as negative<\/strong>. Participants had to choose between these two options.<\/p>\n <\/p>\n Half of the volunteers constituted a control group<\/strong> that did not have any AI assistance. On the other hand, the other half performed the task with the recommendations of an artificial intelligence that showed a systematic error<\/strong>. Those for the tissue samples with the dark\/light cells ratio of 40\/60 were always wrong.<\/p>\n In these samples, there was a contradiction between the classification criterion and the recommendation given by the AI indicating that it was positive when, attending to the proportions of the two colours, it would be classified as negative.<\/p>\n \u201cThe participants reproduced this systematic error of the AI, although it was very simple to discriminate the images, as demonstrated by the control group, which was unassisted from this biased artificial intelligence and hardly made mistakes in the task,\u201d explain to SINC Luc\u00eda Vicente and Helena Matute.<\/p>\n <\/p>\nPositive or negative to the disease<\/strong><\/h3>\n
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Same error without the AI<\/strong><\/h3>\n