Technology (Page 81)

Numerous government agencies, including the FBI, Department of Defense, National Security Agency, Treasury Department, Defense Intelligence Agency, Navy and Coast Guard, have purchased vast amounts of US citizens’ personal information from commercial data brokers.

The revelation was published in a partially declassified, internal Office of the Director of National Intelligence report released on June 9, 2023.

The report shows the breathtaking scale and invasive nature of the consumer data market and how that market directly enables wholesale surveillance of people. The data includes not only where you’ve been and who you’re connected to, but the nature of your beliefs and predictions about what you might do in the future.

The report underscores the grave risks the purchase of this data poses, and urges the intelligence community to adopt internal guidelines to address these problems.

As a privacy, electronic surveillance and technology law attorney, researcher and law professor, I have spent years researching, writing and advising about the legal issues the report highlights.

These issues are increasingly urgent. Today’s commercially available information, coupled with the now-ubiquitous decision-making artificial intelligence and generative AI like ChatGPT, significantly increases the threat to privacy and civil liberties by giving the government access to sensitive personal information beyond even what it could collect through court-authorized surveillance.

What is commercially available information?

The drafters of the report take the position that commercially available information is a subset of publicly available information. The distinction between the two is significant from a legal perspective. Publicly available information is information that is already in the public domain. You could find it by doing a little online searching.

Commercially available information is different. It is personal information collected from a dizzying array of sources by commercial data brokers that aggregate and analyze it, then make it available for purchase by others, including governments. Some of that information is private, confidential or otherwise legally protected.

The sources and types of data for commercially available information are mind-bogglingly vast. They include public records and other publicly available information. But far more information comes from the nearly ubiquitous internet-connected devices in people’s lives, like cellphones, smart home systems, cars and fitness trackers. These all harness data from sophisticated, embedded sensors, cameras and microphones. Sources also include data from apps, online activity, texts and emails, and even health care provider websites.

Types of data include location, gender and sexual orientation, religious and political views and affiliations, weight and blood pressure, speech patterns, emotional states, behavioral information about myriad activities, shopping patterns and family and friends.

This data provides companies and governments a window into the “Internet of Behaviors,” a combination of data collection and analysis aimed at understanding and predicting people’s behavior.

It pulls together a wide range of data, including location and activities, and uses scientific and technological approaches, including psychology and machine learning, to analyze that data. The Internet of Behaviors provides a map of what each person has done, is doing and is expected to do, and provides a means to influence a person’s behavior.

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Better, cheaper and unrestricted

The rich depths of commercially available information, analyzed with powerful AI, provide unprecedented power, intelligence and investigative insights. The information is a cost-effective way to surveil virtually everyone, plus it provides far more sophisticated data than traditional electronic surveillance tools or methods like wiretapping and location tracking.

Government use of electronic surveillance tools is extensively regulated by federal and state laws. The US Supreme Court has ruled that the Constitution’s Fourth Amendment, which prohibits unreasonable searches and seizures, requires a warrant for a wide range of digital searches. These include wiretapping or intercepting a person’s calls, texts or emails; using GPS or cellular location information to track a person; or searching a person’s cellphone.

Complying with these laws takes time and money, plus electronic surveillance law restricts what, when and how data can be collected. Commercially available information is cheaper to obtain, provides far richer data and analysis, and is subject to little oversight or restriction compared to when the same data is collected directly by the government.

The threats

Technology and the burgeoning volume of commercially available information allow various forms of the information to be combined and analyzed in new ways to understand all aspects of your life, including preferences and desires.

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The Office of the Director of National Intelligence report warns that the increasing volume and widespread availability of commercially available information poses “significant threats to privacy and civil liberties.”

It increases the power of the government to surveil its citizens outside the bounds of law, and it opens the door to the government using that data in potentially unlawful ways. This could include using location data obtained via commercially available information rather than a warrant to investigate and prosecute someone for abortion.

The report also captures both how widespread government purchases of commercially available information are and how haphazard government practices around the use of the information are.

The purchases are so pervasive and agencies’ practices so poorly documented that the Office of the Director of National Intelligence cannot even fully determine how much and what types of information agencies are purchasing, and what the various agencies are doing with the data.

Is it legal?

The question of whether it’s legal for government agencies to purchase commercially available information is complicated by the array of sources and complex mix of data it contains.

There is no legal prohibition on the government collecting information already disclosed to the public or otherwise publicly available. But the nonpublic information listed in the declassified report includes data that US law typically protects. The nonpublic information’s mix of private, sensitive, confidential or otherwise lawfully protected data makes collection a legal gray area.

Despite decades of increasingly sophisticated and invasive commercial data aggregation, Congress has not passed a federal data privacy law. The lack of federal regulation around data creates a loophole for government agencies to evade electronic surveillance law.

It also allows agencies to amass enormous databases that AI systems learn from and use in often unrestricted ways. The resulting erosion of privacy has been a concern for more than a decade.

Throttling the data pipeline

The Office of the Director of National Intelligence report acknowledges the stunning loophole that commercially available information provides for government surveillance:

“The government would never have been permitted to compel billions of people to carry location tracking devices on their persons at all times, to log and track most of their social interactions, or to keep flawless records of all their reading habits. Yet smartphones, connected cars, web tracking technologies, the Internet of Things, and other innovations have had this effect without government participation.”

However, it isn’t entirely correct to say “without government participation.” The legislative branch could have prevented this situation by enacting data privacy laws, more tightly regulating commercial data practices, and providing oversight in AI development.

Congress could yet address the problem. Representative Ted Lieu has introduced the a bipartisan proposal for a National AI Commission, and Senator Chuck Schumer has proposed an AI regulation framework.

Effective data privacy laws would keep your personal information safer from government agencies and corporations, and responsible AI regulation would block them from manipulating you.

Anne Toomey McKenna is Visiting Professor of Law, University of Richmond

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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NEW YORK – Microsoft has added about US$1.5 trillion to its market capitalization this year after the launch of ChatGPT. Nvidia has added about $640 billion. Overall, the market value of generative AI models has increased by several trillions of dollars. What is supposed to justify this kind of valuation?

Market research firms claim that the market for generative AI will reach $126.5 billion by 2031. That’s not a lot of revenue compared to the market valuation. Microsoft now sells at about 12 times sales, and that’s a natural monopoly.

Even if that estimate were correct, market valuations are three to five times higher than $126 billion of revenue would justify. And I don’t see how generative AI can throw off that level of revenue, not by an order of magnitude.

Just what is generative AI supposed to do? Supposedly it can answer customer queries, replace low-level programmers and produce better online search results. But how much revenue can that bring in?

Let’s do some back-of-envelope calculations.

Suppose that chatbots replaced every employee of every help desk in the United States. There are 38,808 help desk employees who earn on average $43,275 a year. Replace them all, and you save $1.68 billion a year. Of course, you can’t replace all of them, and chatbots cost something, so I’ll guess that potential savings are $1 billion a year, provided that the gizmo actually works.

Then there are computer programmers. ChatGPT can write basic code. There are 132,740 programmers in the US, so lets guesstimate that generative AI can replace the bottom quarter of them, or 33,185 programmers. The bottom quartile of programmers makes $34.84 an hour, according to the Bureau of Labor Statistics. That would save $2.312 billion, minus the cost of the AI program.

So far we’ve wiped out two major areas of employment, and saved a bit over $3 billion in paychecks. I don’t know where the marketing surveys came up with a $125 billion number, but it seems as if they are off by an order of magnitude.

There are plenty of other generative AI applications, for example, for medical diagnostics. But we’ve heard that story before. AI was supposed to read X-rays faster and more accurately than a human radiologist, but that turned out to be a bust.

“Radiologists have more reason than most to be disappointed, because CAD [computer-aided detection] in medical imaging was more than an unrealized promise. Almost uniquely across the world of technology, medical or otherwise, the hype and optimism around second-era AI led to the widespread utilization of CAD in clinical imaging. This use was most obvious in screening mammography, where it has been estimated that by 2010 more than 74% of mammograms in the United States were read with CAD assistance. Unfortunately, CAD’s benefit has been questionable. Several large trials came to the conclusion that CAD has at best delivered no benefit and at worst has actually reduced radiologist accuracy, resulting in higher recall and biopsy rates,” according to one study.

This isn’t the first instance of AI hype ballooning stock valuations.

Remember autonomous vehicles? Ford put $1 billion into an AV startup called Argo AI, valued at $12.4 billion in 2021, and at zero in October 2022, when it shut down. Driverless cars are the technology of a future that won’t come anytime soon, at least not in the United States.

The dirty little secret of AVs is that they require an enormous amount of data with a very short transmission time (low latency). America’s so-called 5G network doesn’t have the low latency required for AVs. It’s only a hyped-up 4G with a slow response time.

China already has autonomous taxi services in some cities. China’s 5G network is not only the world’s largest, but also offers a nearly instantaneous response time made possible by the new technology. AVs are a great idea when you have broad and straight roads (as in some Chinese cities), but not in the jumble of American urban landscapes.

Mark Zuckerberg’s Meta put $100 billion into a virtual reality world that couldn’t find enough visitors willing to wear heavy and expensive headsets. Meta’s stock price collapsed in the wake of the metaverse fiasco, but soared along with other tech companies in the AI bubble.

The hypothetical calculations above really are beside the point. Chat GPT interacts very badly with real human beings. By the standards of a human 10-year-old, it’s really, really dumb. It can’t make the kind of mental connections that make humor possible. In a recent essay for Law&Liberty, I reported some less-than-satisfactory exchanges with OpenAI’s chatbot on the subject of self-referential humor.

Joking aside, generative AI simply can’t fathom how human beings think and talk. Mental associations that come naturally to human beings are incomprehensible to generative AI models, unless the models happen to have been trained on an identical case in the past.

They won’t replace help desk representatives any time soon, let alone radiologists. And the trillions of stock market valuations that mushroomed in anticipation of generative AI will vanish like the other AI bubbles of the past few years.

AI, to be sure, works wonders when it is applied to routine tasks, for example, picking defective items off a conveyor belt, or manipulating autonomous cranes and trucks at a port. America’s largest port at Long Beach, California, is one of the world’s least efficient (it ranks number 300 on the World Bank list). It takes roughly 48 hours to unload a large container ship.

At China’s most modern port, Tianjin, a 5G network using AI systems designed by Huawei can unload the same ship in 15 minutes. Smart cranes find bar codes on containers, move them to autonomous trucks and go on to the next.

AI is there to free humans from mechanical tasks, not to make machines imitate humans. That’s where AI will add economic value.

Follow David P Goldman on Twitter at @davidpgoldman

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