Age and race biases found in autonomous vehicle pedestrian detection systems

In collaboration with colleagues, Dr Jie Zhang from the Department of Informatics at King’s College London, assessed eight artificial intelligence (AI) powered pedestrian detection systems used in autonomous vehicle research.

They found through testing over 8,000 images through these pieces of software that detection accuracy for adults was almost 20 per cent higher than it was for children, and just over 7.5 per cent more accurate for light-skinned pedestrians compared to their darker-skinned pedestrians.

According to Kings, a major cause of this discrepancy is that the main collections of pedestrian images used to train the AI systems used in pedestrian detection feature more people with light skin than dark skin. The result of this uneven data source is a lack of fairness in the AI system it is used to train.

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In a statement, Dr Jie Zhang said: “Fairness when it comes to AI is when an AI system treats privileged and under-privileged groups the same, which is not what is happening when it comes to autonomous vehicles. Car manufacturers don’t release the details of the software they use for pedestrian detection, but as they are usually built upon the same open-source systems we used in our research, we can be quite sure that they are running into the same issues of bias.