Biases in human perception of facial age are present and more exaggerated in current AI technology
Abstract 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 fa...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-27009-w |
| _version_ | 1797973641574481920 |
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| author | Tzvi Ganel Carmel Sofer Melvyn A. Goodale |
| author_facet | Tzvi Ganel Carmel Sofer Melvyn A. Goodale |
| author_sort | Tzvi Ganel |
| collection | DOAJ |
| description | Abstract 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. 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. |
| first_indexed | 2024-04-11T04:07:30Z |
| format | Article |
| id | doaj.art-86ff98d22d0d4f3988683a306b982120 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T04:07:30Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-86ff98d22d0d4f3988683a306b9821202023-01-01T12:19:31ZengNature PortfolioScientific Reports2045-23222022-12-0112111010.1038/s41598-022-27009-wBiases in human perception of facial age are present and more exaggerated in current AI technologyTzvi Ganel0Carmel Sofer1Melvyn A. Goodale2Department of Psychology, Ben-Gurion University of the NegevDepartment of Cognitive and Brain Sciences, Ben-Gurion University of the NegevThe Western Institute for Neuroscience, The University of Western OntarioAbstract 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. 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.https://doi.org/10.1038/s41598-022-27009-w |
| spellingShingle | Tzvi Ganel Carmel Sofer Melvyn A. Goodale Biases in human perception of facial age are present and more exaggerated in current AI technology Scientific Reports |
| title | Biases in human perception of facial age are present and more exaggerated in current AI technology |
| title_full | Biases in human perception of facial age are present and more exaggerated in current AI technology |
| title_fullStr | Biases in human perception of facial age are present and more exaggerated in current AI technology |
| title_full_unstemmed | Biases in human perception of facial age are present and more exaggerated in current AI technology |
| title_short | Biases in human perception of facial age are present and more exaggerated in current AI technology |
| title_sort | biases in human perception of facial age are present and more exaggerated in current ai technology |
| url | https://doi.org/10.1038/s41598-022-27009-w |
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