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How Facial Recognition Technology Will Create More Bias Issues In Sports

Last Updated: July 5, 2023
Facial recognition could make stadiums more secure and easier to access. The tech’s inherent biases, however, cannot be ignored.

Deep machine learning, biometric technology, and artificial intelligence are all the rave these days. 

Whether it’s ChatGPT, Midjourney, Jasper.ai, or other popular tools, everyone is buzzing about the potential impact of machine learning and artificial intelligence on various industries. 

For years, some of the best developers and engineers in the world have quietly built these state-of-the-art applications that better analyze and process data as well as make more accurate predictions. But only recently have we begun to see it take real shape and reach consumers on a wider scale.

But as with any innovation that climbs to mass adoption, there can also be some apprehension and major challenges to overcome. One of those main hurdles when it comes to ML and AI is dealing with the inherent bias that’s already built into it. Especially when it comes to things such as facial recognition.

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The Issue With Facial Recognition

In a National Institute of Standards and Technology report, researchers studied 189 facial recognition algorithms and discovered that most exhibit some sort of bias. 

According to the researchers, facial recognition technologies falsely identified Black and Asian faces 10 to 100 times more often than they did white faces. The technologies also falsely identified women more than they did men ,  which makes Black women particularly vulnerable to algorithmic bias.

The way it works on a simple level is that facial recognition technology uses AI algorithms and ML to detect human faces from the background. Once all the facial features are captured, additional validations using large datasets containing images confirm that it is a human face.

If it falsely identifies minorities due to discrepancies within the algorithm, then it leaves a ton of room for error and misidentification.

How This Affects Sports 

A number of sports teams and organizations have started to explore ways to integrate ML and AI into the overall fan experience. For example, here are several stadiums across different sports that are already using facial recognition software:

  • Mercedes-Benz Stadium in Atlanta announced in August 2022 that it was testing facial recognition technology for gates and concession stands.
  • FirstEnergy Stadium in Cleveland offers “Express Access” with facial recognition technology.
  • Citi Field in New York City has face-ID ticket kiosks at stadium gates.
  • Pechanga Arena in San Diego installed facial recognition for entry scanning and payment verification.
  • Save Mart Center at Fresno State enables entry and payment with facial recognition tech.
  • Lower.com Field in Columbus, Ohio has express entry with face-ID ticketing.
  • FedEx Field in Landover, Maryland uses facial recognition for entry.
  • Caesars Superdome in New Orleans uses facial recognition tech for entry into training facilities.
  • Toyota Arena in Ontario, California announced in 2022 that it was installing facial recognition for ticketing and concessions.
  • Hard Rock Stadium in Miami Gardens, Florida uses facial recognition for ticketing.
  • BMO Stadium in Los Angeles began using facial recognition technology for entry into training facilities but wants to “move everything to face.”
  • The Rose Bowl in Pasadena, California used facial recognition on 30,000 attendees without their knowledge in 2020.

And this is only the beginning of what’s to come.

According to a 2021 National Center for Spectator Sports Safety and Security (NCS4) study at the University of Southern Mississippi, “40 venue directors representing teams from Major League Baseball, Major League Soccer, the National Basketball Association, the National Football League, and the National Hockey League indicated that facial recognition software was on the top of the wish lists for venues.”

Some of the potential benefits of this include quicker access and improved security, but it raises several serious concerns as well, which can’t be overlooked.

In some cases, we have seen facial recognition software lead to people being denied entry or access and at worst, wrongly accused of crimes due to matches within law enforcement databases, all because of their gender or the color of their skin.

Given that fans come from various backgrounds, genders, and ethnicities,  gender and racial bias will undoubtedly play a role in facial recognition in sports.. 

What is the trickle-down effect once teams in all sports begin to implement this technology as well? How does this affect fans or even the organizations for that matter?

Moreover, think about the number of other issues that arise in other ways such as companies using this software for housing, employment, health screening, and more.

The algorithms that are embedded into this technology are only as good as the people who are creating them. So in order to find a solution, we have to first pull the curtain back to take a closer look at who’s behind the scenes building and launching these applications. 

What’s The Solution?

Part of the issue is that white men have dominated the tech industry for years. When the innovators and people at the top come from a single demographic or genetic makeup, their perspective on how and who this technology can impact is skewed.

Diversity in technology is incredibly important for these reasons because the world is full of people who look, think, and act differently. It’s detrimental to society as a whole if these applications and software only work for certain people.

Furthermore, legislation is necessary for AI and ML, not only to protect people from issues like the example above but also for data and security violations.

Although we haven’t reached mainstream adoption with this technology, the writing is on the wall that it is here to stay. And as great as the potential and upside are , we can’t afford to disregard the downside. 

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