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Airport Security

Airport Security

Mom says TSA made special needs daughter choose between …

DENVER — A Colorado mom says TSA needs to change its screening protocol for special needs children. Stephanie Griggs says her 13-year old daughter, Bella has been diagnosed with Fanconi anemia, a chromosome breakage disorder that can lead to bone marrow problems and early cancer.

“Doctors have told her to avoid any unnecessary radiation,” Griggs said. On Tuesday, the Griggs family began a cross-country journey to Maine, to attend Camp Sunshine, a retreat for kids with life-threatening diseases, and their families.

As they were going through security at Denver International Airport, Bella had to face something she d never experienced before.

I always let TSA know that we re traveling with medical liquids, Griggs said. I also request that Bella be allowed to go through metal detectors, as opposed to a full-body scanner. I reiterated that [the scanner] could be detrimental to her health. Griggs said the agent told her that if the daughter opted out of the full-body scan, she would be patted down. Griggs told Denver7 that she and her husband have always taught their kids that no one should touch them on private parts of their body, except a physician.

She asked to talk to a supervisor and said she was told that her daughter had three choices. She could go through the metal detector and be patted down, go through the full body scan or leave the airport and not go to camp.

I was understandably upset, she said. Bella was very, very upset. Griggs said they ve traveled through DIA and other airports multiple times and never ran into this issue.

It s unfair, she said. Bella s got enough crap in her life to deal with; she doesn t need this. To avoid the pat down from a stranger, Bella opted to go through the full-body scan.

It just kind of felt scary to me, Bella said. So, I just decided to go through the full-body scanner.

During the scan, there was an alert, so Bella ended up being patted down anyway.

Every other time she has flown, she s been deemed safe, Griggs said. Nothing has changed. Griggs said nothing was found during the pat-down, which makes her wonder if the machines weren t calibrated appropriately. She said she also wonders if the security officer was on a power trip.

A TSA spokeswoman provided Denver7 the following statement:

We regret that the passenger and her family found their screening experience stressful. After an internal review, we determined that screening protocols were followed. TSA s screening procedures have been developed to ensure that passengers can be screened regardless of their disability or medical condition. In this case, the passenger elected to go through Advanced Imaging Technology when presented with her options, and required further screening to clear an alarm.

Last March, TSA issued this statement about pat-downs, which applies to passengers 12 and over:

Effective March 2, 2017, TSA consolidated previous pat-down procedures into one standardized pat-down procedure at airport security checkpoints and at other locations within the airport. This standardized pat-down procedure continues to utilize enhanced security measures implemented several months ago, and does not involve any different areas of the body than were screened in the previous standard pat-down procedure. Individuals transiting the TSA security checkpoint who have opted out of technology screening, or have alarmed certain technology or a canine team, will undergo a pat-down. Passengers may also receive a pat-down as part of our unpredictable security measures. TSA continues to adjust and refine our systems and procedures to meet the evolving threat and to achieve the highest levels of transportation security.

The Kaggle data science community is competing to improve airport …

The Kaggle Data Science Community Is Competing To Improve Airport ...

Going through airport security is a universally painful experience. And despite being slow and invasive, the TSA doesn t have a great record at catching threats[1]. With the help of the Kaggle[2] data science community, the Department of Homeland Security (DHS) is hosting an online competition to build machine learning-powered tools that can augment agents, ideally making the entire system simultaneously more accurate and efficient. Kaggle, acquired by Google earlier this year[3], regularly hosts online competitions where data scientists compete for money by developing novel approaches to complex machine learning problems. Today s competition to improve threat recognition algorithms[4] will be Kaggle s third launch this year featuring more than a million dollars in prize money. With a top prize of $500,000 and a total of $1.5 million at stake, competitors will have to accurately predict the location of threat objects on the body. 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.

The outcome of the competition will be a good indicator for how well we can expect such systems to work, Reza Zadeh, founder and CEO of computer vision startup Matroid told me. At the very least, we should have such a system augmenting current security guards to ensure they don t miss dangerous items.

The Kaggle Data Science Community Is Competing To Improve Airport ...

Competitors will be competing to predict the likelihood that weapons are hidden in 17 body zones.

Of course, the problem the TSA faces isn t just a machine learning issue. Expensive physical machines 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).

This means that it s possible that some submissions to this competition could wind up in use on actual scanning machines it s just a matter of training beforehand and optimizing for the constrained conditions. The DHS has promised to work closely with the winners to explore potential real-world applications.

This is a really hard problem, machines do not have crazy GPUs, Anthony Goldbloom, Kaggle s creator, told me in an interview. But one thing that gets lost is that doing inference doesn t necessarily need such heavy compute. 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, Goldbloom 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. Google plans to make GCP available to competitors in the near future. And though Google owns Kaggle, it is thankfully not forcing people to use TensorFlow, its own open-source framework. You can check out additional details here[5]; the competition will draw to a close in December.

Featured Image: Andrew Harrer/Bloomberg via Getty Images/Getty Images

References

  1. ^ the TSA doesn t have a great record at catching threats (www.huffingtonpost.com)
  2. ^ Kaggle (www.kaggle.com)
  3. ^ acquired by Google earlier this year (techcrunch.com)
  4. ^ Today s competition to improve threat recognition algorithms (www.kaggle.com)
  5. ^ here (www.kaggle.com)

The Kaggle data science community is competing to improve airport security with AI

The Kaggle Data Science Community Is Competing To Improve Airport Security With AI

Going through airport security is a universally painful experience. And despite being slow and invasive, the TSA doesn t have a great record at catching threats[1]. With the help of the Kaggle[2] data science community, the Department of Homeland Security (DHS) is hosting an online competition to build machine learning-powered tools that can augment agents, ideally making the entire system simultaneously more accurate and efficient. Kaggle, acquired by Google earlier this year[3], regularly hosts online competitions where data scientists compete for money by developing novel approaches to complex machine learning problems. Today s competition to improve threat recognition algorithms[4] will be Kaggle s third launch this year featuring more than a million dollars in prize money. With a top prize of $500,000 and a total of $1.5 million at stake, competitors will have to accurately predict the location of threat objects on the body. 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.

The Kaggle Data Science Community Is Competing To Improve Airport Security With AI

Competitors will be competing to predict the likelihood that weapons are hidden in 17 body zones. Of course, the problem the TSA faces isn t just a machine learning issue. Expensive physical machines 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). This means that it s possible that some submissions to this competition could wind up in use on actual scanning machines it s just a matter of training beforehand and optimizing for the constrained conditions. The DHS has promised to work closely with the winners to explore potential real-world applications.

This is a really hard problem, machines do not have crazy GPUs, Anthony Goldbloom, Kaggle s creator, told me in an interview. But one thing that gets lost is that doing inference doesn t necessarily need such heavy compute.

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, Goldbloom 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.

Google plans to make GCP available to competitors in the near future. And though Google owns Kaggle, it is thankfully not forcing people to use TensorFlow, its own open-source framework. You can check out additional details here[5]; the competition will draw to a close in December.

Featured Image: Andrew Harrer/Bloomberg via Getty Images/Getty Images

References

  1. ^ the TSA doesn t have a great record at catching threats (www.huffingtonpost.com)
  2. ^ Kaggle (www.kaggle.com)
  3. ^ acquired by Google earlier this year (techcrunch.com)
  4. ^ Today s competition to improve threat recognition algorithms (www.kaggle.com)
  5. ^ here (www.kaggle.com)
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