AI has a notorious history of biases, from facial recognition systemsmisidentifying Black peopleto chatbotsadmiring Hitler.
In some prefs, the outcomes could be deadly.
A case in point was exposed this week.
According to new research, the pedestrian detection systems used inautonomous vehicleresearchhave major age and race biases.
The study adds another obstacle to the rollout of driverless cars.
It also uncovers an alarming potential addition to road safety.
The findings derive from a systematic review of eight popular pedestrian detection systems.
Researchers from Kings College London (KCL) tested the software on over 8,000 images of pedestrians.
It’s free, every week, in your inbox.
They found that the average detection accuracy was almost 20% higher for adults than it was for children.
The systems were also 7.5% more accurate for light-skinned pedestrians than they were for darker-skinned ones.
These discrepancies stem from a common cause of AI biases: unrepresentative training data.
Theres an old saying when it comes to engineering and data science, Rubbish in, rubbish out.
With less data to train on, the AI becomes less accurate when detecting under-represented groups.
Another issue emerged in the lighting conditions.
Under low contrast and low brightness, the biases against children and dark-skinned people were exacerbated.
This suggests that both groups would be at increased risk during nighttime driving.
Zhang is therefore confident that they experience the same issues.
To reduce the risks, she wants more transparency and tighter regulations on pedestrian detection systems.
you’re able to read the study paperhere.
Story byThomas Macaulay
Thomas is the managing editor of TNW.
He leads our coverage of European tech and oversees our talented team of writers.
Away from work, he e(show all)Thomas is the managing editor of TNW.
He leads our coverage of European tech and oversees our talented team of writers.
Away from work, he enjoys playing chess (badly) and the guitar (even worse).
