Artificial intelligence algorithms are the wave of the future.
They are being introduced into almost every aspect of our lives including the automatic estimation of age from a person’s face, a technology that could be used in the future to determine who can enter a bar or other venues where age is a factor – as well as in a range of other applications.
But what biases are there in AI processing?
Researchers from Israel and Canada tested a large sample of the major AI technologies available today and found that not only did they reproduce human biases in facial age recognition, but they exaggerated those biases.
Their findings were published recently in Scientific Reports.
“Our estimates of a person’s age from their facial appearance suffer from several well-known biases and inaccuracies. Typically, for example, we tend to overestimate the age of smiling faces compared to those with a neutral expression, and the accuracy of our estimates decreases for older faces.
The growing interest in age estimation using artificial intelligence (AI) technology raises the question of how AI compares to human performance and whether it suffers from the same biases.
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Here, we compared human performance with the performance of a large sample of the most prominent AI technology available today.
The results showed that AI is even less accurate and more biased than human observers when judging a person’s age—even though the overall pattern of errors and biases is similar.
“Thus, AI overestimated the age of smiling faces even more than human observers did. In addition, AI showed a sharper decrease in accuracy for faces of older adults compared to faces of younger age groups, for smiling compared to neutral faces, and for female compared to male faces.
These results suggest that our estimates of age from faces are largely driven by particular visual cues, rather than high-level preconceptions. Moreover, the pattern of errors and biases we observed could provide some insights for the design of more effective AI technology for age estimation from faces,” the researchers wrote.
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The research was conducted by Prof. Tzvi Ganel from the Department of Psychology and Prof. Carmel Sofer from the Department of Brain and Cognitive Sciences at Ben-Gurion University in collaboration with Prof. Melvyn A. Goodale from the Western Institute for Neuroscience at Western University.
The data about AI performance was collected over the years 2020–2022, using a representative set of 21 current commercial and non-commercial AI age estimation platforms. AI performance was compared with the performance of 30 undergraduate students from Ben-Gurion University of the Negev.
“The AIs tended to exaggerate the ageing effect of smiling for the faces of young adults, incorrectly estimating their age by as much as two and a half years. Interestingly, whereas in human observers, the ageing effect of smiling is missing for middle-aged adult female faces, it was present in the AI systems,” says Prof. Ganel.
At this stage, the researchers can only speculate about why these biases occur – perhaps because of the photo sets used to train the AIs or perhaps because of a statistical phenomenon called regression to the mean – which results in an overestimation of the ages of young people and an underestimation of the ages of older adults.