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“ Humans and machine algorithms (算法) have complementary (互补的) strengths and weaknesses. Each uses different sources of information and strategies to make predictions and decisions, ” said Mark Steyvers, UCI professor of cognitive sciences. “ We show through experiments that humans can improve the predictions of AI even when human accuracy is below that of the AI, and vice versa (反之亦然). This accuracy is higher than combining predictions from two individuals or two AI algorithms. ”

To test the framework, researchers conducted an image classification experiment where human participants and computer algorithms worked separately to correctly identify disorderly pictures of animals and everyday items including chairs, bottles, bicycles and trucks. The human participants ranked their confidence in the accuracy of each image identification as low, medium or high, while the machine classifier generated a continuous score. The results showed large differences in confidence between humans and AI algorithms across images.

“ Human participants were confident that a particular picture contained a chair, for example, while the AI algorithm was confused about the image, ” said Padhraic Smyth, UCI Chancellor’s Professor of computer science. “ Similarly, the AI algorithm was able to confidently provide a label for the object shown, while human participants were unsure if the disorderly picture contained any recognizable object. ”

When predictions and confidence scores from both were combined using the researchers’ new Bayesian framework, the mixed model led to better performance than either human or machine predictions achieved alone.

“ While the past research has demonstrated the benefits of combining machine predictions or combining human predictions, this work shows a new direction in demonstrating the potential of combining human and machine predictions, pointing to new and improved approaches to human-AI cooperation, ” Smyth said.

“ The blend of cognitive science focusing on understanding how humans think and behave and computer science in which technologies are produced will provide further insight into how humans and machines can cooperate to build more accurate artificially intelligent systems, ” the researchers said.