In the wake of the Black Lives Matter protests; companies that had previously defended their facial recognition products began to reconsider their approach. IBM announced that it would no longer offer facial recognition, Amazon said it would pause selling its facial recognition to police for a year, and Microsoft announced Thursday that it would not sell its facial recognition technology to police "until there is a federal law regulating the technology," the Washington Post reported. The message is now clear: Even tech giants like Amazon, IBM, and Google, which had earlier decided not to offer a facial recognition API, do not think this technology is ready for use by police. It's important to remember, though, that companies like IBM and Amazon are the tip of the facial recognition iceberg. Many U.S. police departments with facial recognition tools didn't buy them from big tech companies but rather from smaller contractors that claim to use "forensic" facial recognition algorithms developed by companies like NEC, Rank One, and Cognitec.

Without radar or sonar, the Allies struggled to locate and attack the submarines in the stormy and foggy North Atlantic. To confuse and deceive the enemy, the Allies painted their ships to camouflage them on the ocean. These paint schemes, often called dazzle camouflage, were designed not only to conceal a ship's presence, but also to complicate the submarine's fire-control solution by making it more difficult to determine the aspect of the ship. Paint schemes remained in use through World War II and still find occasional use today. In renewed great power competition, the paint scheme deception tactic should not be retired but, instead, scaled for the 21st century.

Facial recognition was once a hallmark of sci-fi, but we're now years past its introduction in many facets of contemporary society. Social networks use it to tag people in photos, it's the way that millions of people unlock their devices, and it's playing an ever-greater role in helping law enforcement around the world identify criminals and missing children in equal measure. It's an amazing technology that is only possible because of the rise of machine learning algorithms, the sheer mountains of data that modern society collates every day, and ever-faster computers to process it all. But despite all of these advances, facial recognition technology has a dark side. It's often wrong, which can create a myriad of potentially quite serious problems for those who fall foul of it.

Three days ago, a TikTok account going by @deeptomcruise began posting video clips of the Hollywood actor Tom Cruise doing everything from golfing, to tripping and telling a joke in what appears to be a men's clothing store in Italy, to performing a magic trick with a coin. In each of the three videos, Cruise delivers his signature maniacal laugh--you know, the one he repeatedly unleashed in that batty Scientology recruitment video years back--before launching into some sort of bit, and in all of them, it looks just like Cruise. There are a few giveaways, of course. Also, his voice is hollow and scratchy, a la that scene in Face/Off where John Travolta-as-Nicolas Cage is trying to adjust his vocals to that Cage-ian timbre. Still, the Cruise TikToks managed to bewilder and horrify a number of people.

Back in 2013, Microsoft came up with the exciting idea of introducing the general public to Artificial Intelligence. Tay, the teenage chatbot, was launched into the Twittersphere to interact with the platform's audience. Being a young and hip chatbot, it was programmed to use modern slang language instead of formal English. Tay was to mimic those that interacted with her so she could learn the human ways. But this experiment didn't turn out the way Microsoft expected it would. Trolls on the social media site took advantage of Tay's "repeat after me" function and turned her into one of the most bigoted profiles on the forum.

Once a dominion of science fiction (e.g., Star Trek,) facial recognition technology has not only caught up to us in reality this century, but awareness around its benefits and pitfalls has also risen with its heightened presence in the news over the last few months. We hope to shine some light on the reasons for this ascent and the myriad thoughts and actions it has raised. To be sure, all the complex issues, implications, and ethics surrounding facial recognition technology are far too important and expansive to cover in this piece. We also recognize there is much more worth exploring, and a variety of valid and informed views on the subject. Our aim is for this piece to be informative, unbiased, and thought-provoking as the topic of facial recognition technology continues to gain attention and relevance.

Every few months, the U.S. National Institute of Standards and Technology (NIST) releases the results of benchmark tests it conducts on facial recognition algorithms submitted by companies, universities, and independent labs. A portion of these tests focus on demographic performance -- that is, how often the algorithms misidentify a Black man as a white man, a Black woman as a Black man, and so on. Stakeholders are quick to say that the algorithms are constantly improving with regard to bias, but a VentureBeat analysis reveals a different story. In fact, our findings cast doubt on the notion that facial recognition algorithms are becoming better at recognizing people of color. That isn't surprising, as numerous studies have shown facial recognition algorithms are susceptible to bias.

The future of facial recognition technology may depend on one very specific part of the face: the area around the eyes. Before the global pandemic, facial recognition systems typically worked by comparing measurements between different facial features in one image to those in another picture. But when you're wearing a mask over your nose, mouth, and cheeks, you're offering up a fraction of the information normally used to figure out your identity. Now, numerous facial recognition companies say they are focusing on better identifying people based on the portion of the face above the nose and, in particular, the eye region. The stakes are high to get it right, and soon.

Algorithms designed specifically for face masks are getting stumped, researchers found. Many facial recognition companies have claimed they can identify people with pinpoint accuracy even while they're wearing face masks, but the latest results from a study show that the coverings are dramatically increasing error rates. In an update Tuesday, the US National Institute of Standards and Technology looked at 41 facial recognition algorithms submitted after the COVID-19 pandemic was declared in mid-March. Many of these algorithms were designed with face masks in mind, and claimed that they were still able to accurately identify people, even when half of their face was covered. Keep track of the coronavirus pandemic.

The future of facial recognition technology may depend on one very specific part of the face: the area around the eyes. Before the global pandemic, facial recognition systems typically worked by comparing measurements between different facial features in one image to those in another picture. But when you're wearing a mask over your nose, mouth, and cheeks, you're offering up a fraction of the information normally used to figure out your identity. Now, numerous facial recognition companies say they are focusing on better identifying people based on the portion of the face above the nose and, in particular, the eye region. The stakes are high to get it right, and soon.