Tools and Weapons: The Promise and The Peril of the Digital Age

by Brad Smith

Paul Scharre, a former US defense official working at a think tank, brings to life increasingly pertinent questions in his book, Army of None: Autonomous Weapons and the Future of War.

Many other dynamics also seem familiar to the security issues involving cyberweapons. UN Secretary-General António Guterres did not mince words in 2018 when he called for a ban on “killer robots,” saying, “Let’s call it as it is: The prospect of machines with the discretion and power to take human life is morally repugnant.”26 But, as is the case of cyberweapons, the world’s leading military powers have resisted new international rules that would limit their technology development.

We can be optimistic that a new generation of students will embrace this cause with enthusiasm. In early 2018, I posed publicly a question together with Harry Shum, a Microsoft executive vice president responsible for much of our AI work, who earned his PhD in robotics. “Could we see a Hippocratic oath for coders like we have for doctors?” We joined others in suggesting that such an oath could make sense.30 Within a matter of weeks, a computer science professor at the University of Washington had taken a stab at editing the traditional Hippocratic oath to suggest a new code for those who create artificial intelligence.31 As Harry and I each spoke on college campuses around the world, we found that it was a question the next generation cared about.

In other scenarios, the benefits are more far-reaching. In Washington, DC, the National Human Genome Research Institute is using facial recognition to help physicians diagnose a disease known as DiGeorge syndrome, or 22q11.2 deletion syndrome. It’s a disease that more often afflicts people who are African, Asian, or Latin American. It can lead to a variety of severe health problems, including damage to the heart and kidneys. But it also often manifests itself in subtle facial characteristics that can be identified by computers using facial-recognition systems, which can help a doctor diagnose a patient in need.3

We drew insights from the historical regulation of other technologies. There are many markets in which a balanced approach to regulation has created a healthier dynamic for consumers and producers alike. The auto industry spent decades in the twentieth century resisting calls for regulation, but today there is broad appreciation of the essential role that laws have played in ensuring ubiquitous seat belts and air bags and greater fuel efficiency. The same is true for air safety, food, and pharmaceuticals.

The answer, in our view, was for legislation to permit law enforcement agencies to use facial recognition to engage in ongoing surveillance of specific individuals only when it obtains a court order such as a search warrant for this monitoring or when there is an emergency involving imminent danger to human life. This would create rules for facial-recognition services that are comparable to those now in place in the United States for the tracking of individuals through the GPS locations generated by their cell phones. As the Supreme Court had decided in 2018, the police cannot obtain without a search warrant the cell phone records that show the cell sites, and hence the physical locations, where someone has traveled.22 As we put it, “Do our faces deserve the same protection as our phones? From our perspective, the answer is a resounding yes.”

If anything, people tend to be too optimistic, predicting that change will arrive faster than is the case, but as Bill Gates has famously remarked, “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.”15 This is not a recent phenomenon.

But just a month later, in early July, the news broke that Chinese hackers had copied from the US Office of Personnel, or OPM, the social security numbers and other personal information of more than twenty-one million Americans.4 The hackers had penetrated the database containing the details on all Americans with national security clearances. The incident shined a light on both Chinese cybertheft capabilities and the OPM’s horrendous lack of security protection.

As Nisbett points out, the Greek philosophy that remains the foundation of Western political thought shared a strong sense of curiosity with Confucius’s devotion to learning, but was grounded in a different sense of personal agency—a sense that people “were in charge of their own lives and free to act as they chose.”14 As developed by Aristotle and Socrates, the very definition of happiness for the ancient Greeks “consisted of being able to exercise their powers in pursuit of excellence in a life free from constraints.”

As Master Xianxin explained to us, the monastery had devoted itself to merging Buddhist teachings and traditions with the modern world. He was a graduate of the Beijing University of Technology. Yes, a Buddhist monk with a computer science degree. He showed off thousands of volumes of ancient Buddhist literature the temple was digitizing with the help of AI. The master went on to share how the monks were using machine-based translation techniques to share their work in sixteen languages with people around the world. Modern technology was advancing some of the world’s most ancient teachings.

We talked about the ethical and philosophical issues presented by AI and how they might be viewed differently in various parts of the world. It was striking when one of Professor He’s first comments echoed some of the opening words in Nisbett’s book written fifteen years before. “In the West, there’s more of a belief in progress as a straight line, with technology moving forward and optimism about constant improvement,” he said. As Nisbett had noted, people in the West tended to focus on a specific goal and believed that if you could pour yourself into advancing it, you could change the world around you. It was part of the entrepreneurialism that made Silicon Valley not just a location but an attitude that fueled innovation. “In China,” Professor He said, “we see everything moving in cycles. Like the signs of the zodiac, we believe that life is a circle and that everything will come back to its original point at some time in the future.” It led people in China to look backward as well as forward and to focus more on the whole picture rather than an individual piece.

Increasingly, in Washington, DC, both political parties have viewed the rise of Chinese influence with concern. While President Trump has pushed China hard to increase its purchase of almost every American product, one category has had more qualified support: information technology. Believing that this technology will be increasingly fundamental to both economic strength and military power, American policy makers have expressed increasing concern about the prospects of ongoing technology transfers to China.

The risk here is that US officials will fail to appreciate that technology success almost always requires success on a global scale. The economics of information technology turn on spreading R&D and infrastructure costs over the largest number of users possible. This is what drives down prices and creates the network effects needed to turn new applications into market leaders. As LinkedIn cofounder (and Microsoft board member) Reid Hoffman has shown, the ability to “blitzscale” quickly to global leadership is fundamental to technology success.20 But it’s impossible to pursue global leadership if products can’t leave the United States.

It was this ability to share data that inspired Matthew Trunnell. He recognized that the best way to accelerate the race to cure cancer is to enable multiple research organizations to share their data in new ways. While this sounds simple in theory, its execution is complicated. To begin, even in a single organization data is often stashed in silos that must be connected, a challenge made even greater when the silos sit in different institutions. The data may not be stored in a form that is readable by machines. Even if it is, different data sets are likely to be formatted, labeled, and structured in different ways that make it harder to share and use in common. If the data came from individuals, legal issues around privacy will need to be worked through. And even if the data doesn’t involve personal information, other big questions need to be hammered out, such as the governance process among organizations and the ownership in data as it grows and is improved.

Shortly after Barack Obama’s reelection in 2012, Reince Priebus was reelected to a second term heading the Republican National Committee, or RNC. He and his new chief of staff, Mike Shields, undertook a top-to-bottom review of the RNC’s operations in the wake of the 2012 defeat, including its technology strategy. And as often happens in the fast-paced world of technology, there emerged an opportunity to leapfrog the competition. Priebus and Shields utilized data models from three Republican technology consulting firms and embedded them in-house at the RNC. While they lacked easy access to the Democratic-leaning talent pool in Silicon Valley, they brought in a new CTO from the University of Michigan and a young technologist from the Virginia Department of Transportation to build new algorithms for the world of politics. The two RNC leaders believed—and proved—that there was top data science talent everywhere.

Most important to the Republican tech strategists that morning was what Preibus and his team did next. They succeeded in establishing a data-sharing model that convinced not only Republican candidates across the country but also a variety of super PACs and other conservative organizations to contribute their information to a large, federated file of foundational data. Shields believed it was important to assemble as much data as possible from as many sources as possible in part because the RNC had no idea who the ultimate presidential nominee would be. Until then, they couldn’t know what types of issues or voters the candidate would find most important. So the RNC team worked to connect with as many organizations and to federate as much diverse data as possible. It created a much richer total data set than anything the DNC or the Clinton campaign possessed.

When Donald Trump secured the Republican nomination in the spring of 2016, his operation lacked the deep technology infrastructure of the Clinton campaign. To make up for this deficit, Trump’s son-in-law, Jared Kushner, worked with the campaign’s digital director, Brad Parscale, on a digital strategy that would build on what the RNC already had rather than create their own. Based on the RNC’s data sets, they had identified a group of fourteen million Republicans who said they did not like Donald Trump. To turn this group of skeptics into supporters, the Trump team created Project Alamo in Parscale’s hometown of San Antonio to consolidate fund-raising, messaging, and targeting, especially on Facebook. They communicated to these voters repeatedly with messages on topics that the data said were likely to be important to them, like the opioid epidemic and the Affordable Care Act. The Republican team described what their data operation revealed as the election approached. Ten days before the election, they estimated that they were down two points to Clinton in key battleground states. But they had identified 7 percent of the populat...

The three companies announced the Open Data Initiative, launched a month later, designed to provide a technology platform and tools to enable organizations to federate data while continuing to own and maintain control of the data they share. It will include tech tools that organizations can use to identify and assess the useful data they already possess and put it into a machine-readable and structured format suitable for sharing.

By December, this work had borne fruit and we announced a $4 million Microsoft commitment to support the Hutch’s project. Formally called the Cascadia Data Discovery Initiative, the work is designed to help identify and facilitate the sharing of data in privacy-protected ways among the Hutch, the University of Washington, and the University of British Columbia and the BC Cancer Agency, both based in Vancouver. It is an early example of what is starting to spread, including the California Data Collaborative, where cities, water retailers, and land planning agencies are federating data to enable analytics-driven solutions to address water shortages.

All of this provides cause for optimism about the future of open data, at least if we seize the moment. While some technologies benefit some companies and countries more than others, that is not always the case. For example, nations have never been forced to grapple with hard questions about who would be the world leader in electricity. Any country could put the invention to use, and the question was who would have the foresight to apply it as broadly as possible. Societally we should aim to make the effective use of data as accessible as electricity. It is not an easy task. But with the right approach to sharing data and the right support from governments, it is more than possible for the world to create a model that will ensure that data does not become the province of a few large companies and countries. Instead it can become what the world needs it to be—an important engine everywhere for a new generation of economic growth.

In 2016, Anne was the second hacker recruited for a project using AI, computer vision, and a smartphone camera. One of her tasks was testing the app by walking around Microsoft’s campus with a phone strapped to her forehead. The life of an inventor is sometimes not for the fashion conscious. But when it comes to fashion, almost anything seems to go at Microsoft. The team’s work led to a breakthrough, an AI-powered app that helps people who are blind “see” the world as it’s described through their smartphones. With Seeing AI, Anne, who is blind herself, can now read a handwritten note from her family, independently. As she says, “That probably seems simple to you guys, since you’ve been able to do this for a long time. But when someone writes something to me that’s personal or private in nature, I’ve always had to ask someone else to read it to me. Now I don’t. That means something.”1

Microsoft’s AI for Earth team is working with researchers at Carnegie Mellon University to help park rangers in the Uganda Wildlife Authority stay one step ahead of poachers. Using an algorithm to sift through fourteen years of historical national park patrol data, the Protection Assistant for Wildlife Security application, or PAWS, uses computational game theory to learn and predict poaching behavior, enabling authorities to proactively identify poaching hotspots and modify their patrols.

Things came to a head in the 1880s. There was virtually no tradition of regulating the economy at the federal level except during times of war, and proposals in Washington, DC, to regulate railroads, repeatedly went down in defeat. The state governments responded by passing laws to regulate railroad rates that impacted trips beyond their borders. In 1886, the Supreme Court struck these down, ruling that the federal government alone had this power.11 Suddenly the public confronted a stark reality: The states “could not, and the federal government would not, regulate railroads.”12 This new political dynamic broke the impasse, and the next year Congress created the Interstate Commerce Commission to regulate railroads.13 The modern federal government was born.

How can governments regulate a technology that is bigger than themselves? This is perhaps the single greatest conundrum confronting technology’s regulatory future. But once you ask the question, one part of the answer becomes clear: Governments will need to work together.

To start with, we will need to continue to build coalitions of the willing. Six governments and two companies came together publicly to address WannaCry. A group of thirty-four companies launched the tech accord, and an initial group of fifty-one governments were part of the multi-stakeholder support for the Paris Call. In each instance, there were important and even critical omissions. But progress came not by dwelling on who was missing but on who could be persuaded to join. This in turn led to continued momentum and additional expansion later on.