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Conducting airport security missions can be a complex task, which sometimes results in more than a few missed threats. Now, machine learning might help reduce security guards’ mistakes.

The US Department of Homeland Security (DHS) has been hosting an online competition to build machine learning-powered tools that can improve threat recognition algorithms, ideally making the entire airport security system simultaneously more accurate and efficient.

The competition is hosted by Kaggle data science community, acquired by Google earlier this year. The online competition allows data scientists compete for money by developing novel approaches to complex machine learning problems.

With a top prize of $500,000 and a total of $1.5 million at stake, competitors have to accurately predict the location of threat objects on the passenger body. According to, the TSA is making its data set of images available to competitors so they can train on images of people carrying weapons. Importantly, these will be staged images created by the TSA rather than real-world examples — a necessary move to ensure privacy.

Possibly, some submissions to this competition could wind up in use on actual scanning machines. The DHS has promised to work closely with the winners to explore potential real-world applications.

TSA also faces other problems, such as expensive physical machines that are complicated to upgrade, and none feature the kinds of sophisticated GPUs found in modern data centers. Thankfully, Google, Facebook and others are heavily investing in lighter versions of machine learning frameworks, optimized to run locally, at the edge (without internet).

Another concern that Kaggle and the TSA had to account for was the risk of bias influencing the automated threat detection process — a potential nightmare for travelers that could be inappropriately segregated based on arbitrary factors. To mitigate this, the TSA put special effort into creating the data set of images that will ultimately be used to train the detectors.

“The TSA did a nice job in setting this up,” Anthony Goldbloom, Kaggle’s creator emphasized. “They recruited volunteers but made sure that they had a decent amount of diversity so models don’t fail on a certain type of person.”