Staying safer while recording police activity requires different tactics depending on the situation. Bystanders witnessing police violence in a public space should keep a distance, Kelley-Chung advises—that way you can’t be accused of being a participant. If you get pulled over? Get a passenger to start filming right away, before the officer approaches your window (reaching into your pocket for your phone can also be extremely dangerous, particularly for people of color). If it’s legal in your area, a dash cam might be an alternative, Wandt suggests.
As much as a cell-phone camera offers protection, Wandt says, it’s also important to keep in mind that “once somebody takes out a camera and starts filming an arrest, it absolutely changes the nature of the situation for everybody, from the victim to the suspect to the police officer.”
“There’s the law, there’s the Constitution, and then there’s what you do when you’re face to face with the police,” says Sykes, the ACLU attorney. Figuring out exactly how much to push back against a police officer who is giving an unlawful order is “tough,” he says, especially in certain circumstances—for example, at a protest.
“There is a special flavor of risk when you’re protesting the police and the police are armed and standing feet away from you,” Sykes says.
On-the-ground experience is really the only way to read whether a situation at a protest is safe. But one thing Kelley-Chung has observed is that the presence of a camera filming an officer can protect others from misconduct.
“When you see people in a verbal dispute with police, get as close as possible,” he says. “That camera can be more protection than a tactical vest.”
In any situation, everyone we spoke to had the same caveats: Do not interfere in police operations. Comply when police tell you that you need to move, but you do not have to stop filming from a new location, even if they claim you must, as long as you are recording an officer in a public space carrying out their duties.
Cop watchers generally advise others to collect identifying information on police at the scene, and to note the time and location. You could ask for a badge number; Parriott says most officers actually just carry business cards.
A mine of misinformation
No single video is going to change how police act, and experts argue that even large numbers of videos cannot change the culture of many police departments. On the contrary, police have found ways to use video, especially body camera footage, to reinforce and control their own narrative in cases of possible violence or misconduct.
People like to think that video is simply a neutral tool for capturing information, says Jennifer Grygiel, an assistant professor of communications at Syracuse University—but it’s not, and how it’s released, and in what context, needs additional vetting.
“They get to set the narrative when it’s released, which controls the initial public sentiment around it and opinion. They also push it out on their social media, and their accounts are just like everybody else’s in that they grow their audience. So then they get people following them there because they’re the first to publish information,” Grygiel says. Their own research deals with how police departments use social media to bypass fact-checking by journalists: it started after they noticed how police were pushing out mugshots on local Facebook pages. “People were going in there, like an old public square, and harassing people who had been arrested,” Grygiel says.
As police become better at producing their own media, finding an audience outside of journalism, and making the most of accountability measures like body cameras, Grygiel argues, independent documentation of police officers working in public can serve as a counter to that messaging. Sometimes, as was in the case with the Floyd murder, that documentation happens spontaneously, and often amid great distress, when clear instances of police violence or misconduct are unfolding in real time.
But the capacity for police and police-affiliated organizations to spread misinformation was obvious during the protests in the summer of 2020, when police departments repeatedly promoted inaccurate information. Some of that misinformation went viral, aided by sympathetic media coverage and the right-wing internet, hell-bent on reinforcing the belief that anti-racism protests are merely a conduit for a violent war on cops.
Police unions promoted an alarming claim that Shake Shack employees had “intentionally poisoned” a group of police officers in Manhattan. The story had been dispelled by the next morning: NYPD investigators said the foul-tasting substance in the three officers’ milkshakes wasn’t “bleach,” as the unions speculated, and it wasn’t added to the drinks on purpose. Although the Police Benevolent Association and the Detectives’ Endowment Association both eventually deleted their tweets making the accusation, they had tens of thousands of retweets, and triggered a wave of credulous coverage in conservative and mainstream press. Media write-ups about the tweets got tens of thousands of shares on Facebook and continued to circulate even after the story was debunked.
And this was just one example. Last summer, NYPD Commissioner Dermot Shea reposted a video of police removing bins of bricks from a South Brooklyn sidewalk, claiming they were the work of “organized looters” offering protesters materials to use for violence, despite little evidence that this was actually true. The NYPD also circulated an alert to officers with images of coffee cups filled with concrete, which closely resemble concrete samples used on construction sites. In Columbus, Ohio, the police tweeted out a photo of a colorful bus that they said was supplying dangerous equipment to “rioters,” fueling already rampant national rumors of “antifa buses” descending on cities. In fact, the bus belonged to a group of circus performers, who said the equipment police cited as riot supplies included juggling clubs and kitchen utensils.
In short, police still lie despite being watched more closely than ever. There are hundreds of videos of police misconduct at the summer protests alone, some from the body cams introduced in reforms meant to hold them more accountable. But Kelley-Chung thinks there’s only so much difference any one video can make.
“I’ve seen people filming officers with their cameras out in the moment and then get tackled by police,” he says. “They know they’re on camera … and yet they still continue to abuse.”
And even after he reached his settlement with the DC police, there’s an aspect of that day he can’t stop thinking about. Kelley-Chung is Black, and his filming partner, Andrew Jasiura, is white. They were both dressed in the same T-shirt, carrying the same sort of camera equipment. Officers saw Jasiura too: “They pulled him out so they could talk to him,” says Kelley-Chung.
That’s when Jasiura told police that his partner was a journalist too. They continued to arrest him anyway.
Rediscover trust in cybersecurity
The world has changed dramatically in a short amount of time—changing the world of work along with it. The new hybrid remote and in-office work world has ramifications for tech—specifically cybersecurity—and signals that it’s time to acknowledge just how intertwined humans and technology truly are.
Enabling a fast-paced, cloud-powered collaboration culture is critical to rapidly growing companies, positioning them to out innovate, outperform, and outsmart their competitors. Achieving this level of digital velocity, however, comes with a rapidly growing cybersecurity challenge that is often overlooked or deprioritized : insider risk, when a team member accidentally—or not—shares data or files outside of trusted parties. Ignoring the intrinsic link between employee productivity and insider risk can impact both an organizations’ competitive position and its bottom line.
You can’t treat employees the same way you treat nation-state hackers
Insider risk includes any user-driven data exposure event—security, compliance or competitive in nature—that jeopardizes the financial, reputational or operational well-being of a company and its employees, customers, and partners. Thousands of user-driven data exposure and exfiltration events occur daily, stemming from accidental user error, employee negligence, or malicious users intending to do harm to the organization. Many users create insider risk accidentally, simply by making decisions based on time and reward, sharing and collaborating with the goal of increasing their productivity. Other users create risk due to negligence, and some have malicious intentions, like an employee stealing company data to bring to a competitor.
From a cybersecurity perspective, organizations need to treat insider risk differently than external threats. With threats like hackers, malware, and nation-state threat actors, the intent is clear—it’s malicious. But the intent of employees creating insider risk is not always clear—even if the impact is the same. Employees can leak data by accident or due to negligence. Fully accepting this truth requires a mindset shift for security teams that have historically operated with a bunker mentality—under siege from the outside, holding their cards close to the vest so the enemy doesn’t gain insight into their defenses to use against them. Employees are not the adversaries of a security team or a company—in fact, they should be seen as allies in combating insider risk.
Transparency feeds trust: Building a foundation for training
All companies want to keep their crown jewels—source code, product designs, customer lists—from ending up in the wrong hands. Imagine the financial, reputational, and operational risk that could come from material data being leaked before an IPO, acquisition, or earnings call. Employees play a pivotal role in preventing data leaks, and there are two crucial elements to turning employees into insider risk allies: transparency and training.
Transparency may feel at odds with cybersecurity. For cybersecurity teams that operate with an adversarial mindset appropriate for external threats, it can be challenging to approach internal threats differently. Transparency is all about building trust on both sides. Employees want to feel that their organization trusts them to use data wisely. Security teams should always start from a place of trust, assuming the majority of employees’ actions have positive intent. But, as the saying goes in cybersecurity, it’s important to “trust, but verify.”
Monitoring is a critical part of managing insider risk, and organizations should be transparent about this. CCTV cameras are not hidden in public spaces. In fact, they are often accompanied by signs announcing surveillance in the area. Leadership should make it clear to employees that their data movements are being monitored—but that their privacy is still respected. There is a big difference between monitoring data movement and reading all employee emails.
Transparency builds trust—and with that foundation, an organization can focus on mitigating risk by changing user behavior through training. At the moment, security education and awareness programs are niche. Phishing training is likely the first thing that comes to mind due to the success it’s had moving the needle and getting employees to think before they click. Outside of phishing, there is not much training for users to understand what, exactly, they should and shouldn’t be doing.
For a start, many employees don’t even know where their organizations stand. What applications are they allowed to use? What are the rules of engagement for those apps if they want to use them to share files? What data can they use? Are they entitled to that data? Does the organization even care? Cybersecurity teams deal with a lot of noise made by employees doing things they shouldn’t. What if you could cut down that noise just by answering these questions?
Training employees should be both proactive and responsive. Proactively, in order to change employee behavior, organizations should provide both long- and short-form training modules to instruct and remind users of best behaviors. Additionally, organizations should respond with a micro-learning approach using bite-sized videos designed to address highly specific situations. The security team needs to take a page from marketing, focusing on repetitive messages delivered to the right people at the right time.
Once business leaders understand that insider risk is not just a cybersecurity issue, but one that is intimately intertwined with an organization’s culture and has a significant impact on the business, they will be in a better position to out-innovate, outperform, and outsmart their competitors. In today’s hybrid remote and in-office work world, the human element that exists within technology has never been more significant.That’s why transparency and training are essential to keep data from leaking outside the organization.
This content was produced by Code42. It was not written by MIT Technology Review’s editorial staff.
How AI is reinventing what computers are
Fall 2021: the season of pumpkins, pecan pies, and peachy new phones. Every year, right on cue, Apple, Samsung, Google, and others drop their latest releases. These fixtures in the consumer tech calendar no longer inspire the surprise and wonder of those heady early days. But behind all the marketing glitz, there’s something remarkable going on.
Google’s latest offering, the Pixel 6, is the first phone to have a separate chip dedicated to AI that sits alongside its standard processor. And the chip that runs the iPhone has for the last couple of years contained what Apple calls a “neural engine,” also dedicated to AI. Both chips are better suited to the types of computations involved in training and running machine-learning models on our devices, such as the AI that powers your camera. Almost without our noticing, AI has become part of our day-to-day lives. And it’s changing how we think about computing.
What does that mean? Well, computers haven’t changed much in 40 or 50 years. They’re smaller and faster, but they’re still boxes with processors that run instructions from humans. AI changes that on at least three fronts: how computers are made, how they’re programmed, and how they’re used. Ultimately, it will change what they are for.
“The core of computing is changing from number-crunching to decision-making,” says Pradeep Dubey, director of the parallel computing lab at Intel. Or, as MIT CSAIL director Daniela Rus puts it, AI is freeing computers from their boxes.
More haste, less speed
The first change concerns how computers—and the chips that control them—are made. Traditional computing gains came as machines got faster at carrying out one calculation after another. For decades the world benefited from chip speed-ups that came with metronomic regularity as chipmakers kept up with Moore’s Law.
But the deep-learning models that make current AI applications work require a different approach: they need vast numbers of less precise calculations to be carried out all at the same time. That means a new type of chip is required: one that can move data around as quickly as possible, making sure it’s available when and where it’s needed. When deep learning exploded onto the scene a decade or so ago, there were already specialty computer chips available that were pretty good at this: graphics processing units, or GPUs, which were designed to display an entire screenful of pixels dozens of times a second.
Anything can become a computer. Indeed, most household objects, from toothbrushes to light switches to doorbells, already come in a smart version.
Now chipmakers like Intel and Arm and Nvidia, which supplied many of the first GPUs, are pivoting to make hardware tailored specifically for AI. Google and Facebook are also forcing their way into this industry for the first time, in a race to find an AI edge through hardware.
For example, the chip inside the Pixel 6 is a new mobile version of Google’s tensor processing unit, or TPU. Unlike traditional chips, which are geared toward ultrafast, precise calculations, TPUs are designed for the high-volume but low-precision calculations required by neural networks. Google has used these chips in-house since 2015: they process people’s photos and natural-language search queries. Google’s sister company DeepMind uses them to train its AIs.
In the last couple of years, Google has made TPUs available to other companies, and these chips—as well as similar ones being developed by others—are becoming the default inside the world’s data centers.
AI is even helping to design its own computing infrastructure. In 2020, Google used a reinforcement-learning algorithm—a type of AI that learns how to solve a task through trial and error—to design the layout of a new TPU. The AI eventually came up with strange new designs that no human would think of—but they worked. This kind of AI could one day develop better, more efficient chips.
Show, don’t tell
The second change concerns how computers are told what to do. For the past 40 years we have been programming computers; for the next 40 we will be training them, says Chris Bishop, head of Microsoft Research in the UK.
Traditionally, to get a computer to do something like recognize speech or identify objects in an image, programmers first had to come up with rules for the computer.
With machine learning, programmers no longer write rules. Instead, they create a neural network that learns those rules for itself. It’s a fundamentally different way of thinking.
Decarbonizing industries with connectivity and 5G
The United Nations Intergovernmental Panel on Climate Change’s sixth climate change report—an aggregated assessment of scientific research prepared by some 300 scientists across 66 countries—has served as the loudest and clearest wake-up call to date on the global warming crisis. The panel unequivocally attributes the increase in the earth’s temperature—it has risen by 1.1 °C since the Industrial Revolution—to human activity. Without substantial and immediate reductions in carbon dioxide and other greenhouse gas emissions, temperatures will rise between 1.5 °C and 2 °C before the end of the century. That, the panel posits, will lead all of humanity to a “greater risk of passing through ‘tipping points,’ thresholds beyond which certain impacts can no longer be avoided even if temperatures are brought back down later on.”
Corporations and industries must therefore redouble their greenhouse gas emissions reduction and removal efforts with speed and precision—but to do this, they must also commit to deep operational and organizational transformation. Cellular infrastructure, particularly 5G, is one of the many digital tools and technology-enabled processes organizations have at their disposal to accelerate decarbonization efforts.
5G and other cellular technology can enable increasingly interconnected supply chains and networks, improve data sharing, optimize systems, and increase operational efficiency. These capabilities could soon contribute to an exponential acceleration of global efforts to reduce carbon emissions.
Industries such as energy, manufacturing, and transportation could have the biggest impact on decarbonization efforts through the use of 5G, as they are some of the biggest greenhouse-gas-emitting industries, and all rely on connectivity to link to one another through communications network infrastructure.
The higher performance and improved efficiency of 5G—which delivers higher multi-gigabit peak data speeds, ultra-low latency, increased reliability, and increased network capacity—could help businesses and public infrastructure providers focus on business transformation and reduction of harmful emissions. This requires effective digital management and monitoring of distributed operations with resilience and analytic insight. 5G will help factories, logistics networks, power companies, and others operate more efficiently, more consciously, and more purposely in line with their explicit sustainability objectives through better insight and more powerful network configurations.
This report, “Decarbonizing industries with connectivity & 5G,” argues that the capabilities enabled by broadband cellular connectivity primarily, though not exclusively, through 5G network infrastructure are a unique, powerful, and immediate enabler of carbon reduction efforts. They have the potential to create a transformational acceleration of decarbonization efforts, as increasingly interconnected supply chains, transportation, and energy networks share data to increase efficiency and productivity, hence optimizing systems for lower carbon emissions.