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Sophia, a humanoid robot, is the main attraction at a conference on artificial intelligence this week but her technology has raised concerns for future human jobs

Sophia smiles mischievously, bats her eyelids and tells a joke. Without the mess of cables that make up the back of her head, you could almost mistake her for a human.

The humanoid robot, created by Hanson robotics, is the main attraction at a UN-hosted conference in Geneva this week on how artificial intelligence can be used to benefit humanity.

The event comes as concerns grow that rapid advances in such technologies could spin out of human control and become detrimental to society.

Sophia herself insisted "the pros outweigh the cons" when it comes to artificial intelligence.

"AI is good for the world, helping people in various ways," she told AFP, tilting her head and furrowing her brow convincingly.

Work is underway to make artificial intelligence "emotionally smart, to care about people," she said, insisting that "we will never replace people, but we can be your friends and helpers."

But she acknowledged that "people should question the consequences of new technology."

Among the feared consequences of the rise of the robots is the growing impact they will have on human jobs and economies.
Legitimate concerns
Decades of automation and robotisation have already revolutionised the industrial sector, raising productivity but cutting some jobs.

AI developers hope their inventions will some day revolutionise sectors such as healthcare and education, especuially in rural areas with shortages of professional

And now automation and AI are expanding rapidly into other sectors, with studies indicating that up to 85 percent of jobs in developing countries could be at risk.

"There are legitimate concerns about the future of jobs, about the future of the economy, because when businesses apply automation, it tends to accumulate resources in the hands of very few," acknowledged Sophia's creator, David Hanson.

But like his progeny, he insisted that "unintended consequences, or possible negative uses (of AI) seem to be very small compared to the benefit of the technology."

AI is for instance expected to revolutionise healthcare and education, especially in rural areas with shortages of doctors and teachers.

"Elders will have more company, autistic children will have endlessly patient teachers," Sophia said.

But advances in robotic technology have sparked growing fears that humans could lose control.
Killer robots
Amnesty International chief Salil Shetty was at the conference to call for a clear ethical framework to ensure the technology is used on for good.

"We need to have the principles in place, we need to have the checks and balances," he told AFP, warning that AI is "a black box... There are algorithms being written which nobody understands."

AI technology is already being used in the US for 'predictive policing' and rights groups are calling for regulation

Shetty voiced particular concern about military use of AI in weapons and so-called " killer robots".

"In theory, these things are controlled by human beings, but we don't believe that there is actually meaningful, effective control," he said.

The technology is also increasingly being used in the United States for "predictive policing", where algorithms based on historic trends could "reinforce existing biases" against people of certain ethnicities, Shetty warned.

Hanson agreed that clear guidelines were needed, saying it was important to discuss these issues "before the technology has definitively and unambiguously awakened."

While Sophia has some impressive capabilities, she does not yet have consciousness, but Hanson said he expected that fully sentient machines could emerge within a few years.

"What happens when (Sophia fully) wakes up or some other machine, servers running missile defence or managing the stock market?" he asked.

The solution, he said, is "to make the machines care about us."

"We need to teach them love."

© 2017 AFP

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AI 'good for the world'... says ultra-lifelike robot

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Sophia smiles mischievously, bats her eyelids and tells a joke. Without the mess of cables that make up the back of her head, you could almost mistake her for a human. The humanoid robot, created by Hanson robotics, is the main attraction at a UN-hosted conference in Geneva this week on how artificial intelligence can be used to benefit humanity.

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The title of Alexander Keyssar's new book-Why Do We Still Have the Electoral College?-is also, he says, the question Americans ask themselves every four years. The Stirling professor of history and social policy at the Harvard Kennedy School recently spoke with Harvard Magazine about the book and about why the Electoral College, an institution that's been unpopular almost from the moment it was founded, has prevailed to this day.
Harvard Magazine: What's the origin story for your new book? How and why did this project start?
Alexander Keyssar: The book really began, I think, after the 2000 election, when the winner of the electoral vote received only a minority of the popular vote. I began to wonder why we still have the Electoral College, what had prevented its reform or abolition. After doing a bit of reading and research, it seemed that the most standard answer to that question-that small states prevented reform-simply was not accurate. As I read about past efforts at reform, I also came to realize that those efforts were very extensive and had been going on for a very long time, which made the question all the more compelling. The question that is the title of the book is both a scholarly, analytic question, and a question about public understandings. It's a question that several hundred million Americans ask themselves every four years.
What do you see as the connection between this book and your earlier work, much of it focused on voting rights?
There is no right to vote for president guaranteed in the United States Constitution.

Interestingly, when I wrote the first edition of The Right to Vote, I said very little about the Electoral College, which then troubled me because that book was published in 2000-just before the election, in which the Electoral College played a significant role. But the Electoral College just wasn't on my mind.
More than 1,000 constitutional amendments have been introduced to change the Electoral College. What are the defects in the system that have caused people to want to reform it for almost its entire existence?
But the 2000 election made me think about what voting rights meant, given the peculiarity of the presidential election system, of the Electoral College. There was also another very specific link, which was something that arose during the dispute over the 2000 election: it became clear that individual state legislatures retained the right to choose electors by themselves, without holding popular elections. One of the Supreme Court justices-I think it was Justice Scalia-in the Supreme Court case in 2000 pointed out there is no right to vote for president guaranteed in the United States Constitution. So that is the intellectual link between the two books.

The Electoral College began to be unpopular within a few years after it was adopted. There were a number of features of [its] design that were regarded as problematic, and the foci of concern changed over time. In the early years, the central concern was the fact that each state could decide how to choose electors, which meant that it could choose all its electors by "winner-take-all," it could have an election that allocated electors by [congressional] district, or it could have no election at all, and the state legislature would just choose electors. All of those things happened. Moreover, by the 1790s, different states started changing how they were going to choose electors from one election to the next, to game the system in a partisan fashion. By the time you get into the 1810s-1820s, you have a presidential electoral system in which there was no uniformity from state to state, and each state's processes could (and did) change from election to election. That was regarded by large numbers of people, including members of Congress, even James Madison, as no way to run a railroad. (Admittedly, there were no railroads at the time!) What resulted was a desire to create uniformity and stability. Most leading politicians also thought that there should be district elections for electors, and the Senate approved constitutional amendments for district elections four times between 1813 and 1826.
What would be the difference between district elections, which you mentioned a moment ago, and what we have now? Would electors be assigned on a district level rather than the state level?
One set of problems, thus, had to do with the variability in how each state chose electors. That variability comes to an end by the 1830s, when just about all states adopted a winner-take-all system. Since then, there have been repeated and insistent objections to the use of winner-take-all. People just don't think that it's appropriate or fair to have a system in which a candidate who gets 52 percent of the popular vote in a state receives all of the electoral votes. That feature is thought of as undemocratic; and it deforms election campaigns. Other critics believed that the presence of intermediaries, meaning the electors themselves, was profoundly undemocratic and hazardous, because the will of the people could be ignored or overridden. As American political values on the whole became more democratic, the presence of any kind of intermediaries and any weighting of votes so that not all votes counted equally began to seem more and more objectionable.

Exactly. Electors would be assigned according to who won the popular vote in each district, which meant that individual states would likely not wield large blocks of electoral votes. One worry about district elections, in the nineteenth century and now, is that they could readily import the problem of gerrymandering into presidential elections.

Let me go back to your larger question of what were the things people objected to in the early decades. There was also considerable concern, as there has been only occasionally since then, about a feature of the system that's called the contingent election system. The Constitution requires that the president receive a majority of electoral votes. If no candidate gets a majority, then the election gets turned over to the House of Representatives, using an odd formula in which every state delegation gets one vote. That part of the system greatly advantages small states because every state, no matter its size, had the same weight. This system was used in 1800, it was used in 1824, and a lot of people believed that it would be continue to be used very frequently. So that was another source of concern and desire for reform, to get rid of the contingent election system and if possible, to figure out some way to not have Congress involved at all in choosing a president.
How did we get the present-day winner-take-all system? Isn't that at odds with the reason the Electoral College was invented in the first place?
That particular problematic feature of the Electoral College is one that we do not pay much attention to now, because it hasn't been deployed in a long time. But there was a lot of attention focused on it as recently as 1992, when Ross Perot ran for president. It looked for a while as though he would get enough electoral votes to toss the election into the House.

Winner-take-all was not really envisioned by the framers. When the Constitution was written, there were no political parties. It was written in the belief that there really would not be political parties and that that there should not be. If you think about the design of the Electoral College and the need to have a majority to win an election, a lot of people thought that what would happen would be that states would cast their electoral votes for four or five or six different people. Nobody would get a majority, and then it would go to the House of Representatives and they would sort it out.
Can you think of any society whose election system works in a way remotely comparable to ours?
Winner-take-all emerges out of the dynamics of partisan competition. There was a famous incident in 1800, following in the wake of the previous election in 1796, when Thomas Jefferson had lost the presidency to John Adams very narrowly. Virginia's electors were chosen through a district system, and there had been one or two Virginia electors who voted for Adams. The votes that Adams received from Virginia helped him to win the election. When the 1800 election was approaching-a replay between the same two candidates-Virginia changed its system to winner-take-all, to ensure that Jefferson would get all of [its] electoral votes. Massachusetts retaliated by doing something similar, and that same partisan logic begins to take over, extending to all states by the 1830s. The dominant political party in each state preferred having winner-take-all. Everybody at the time said that they would be happy to go to a district system if everybody else would. But they weren't going to do it by themselves. That stance has reappeared often in more recent history.
You've talked and written elsewhere about the possibility of enacting a federal law or even a constitutional amendment enshrining the right to vote. If we had such a law, could that then be the basis for challenges to the Electoral College in some way?
I can't say that there isn't one or that there hasn't ever been. But our electoral system is just absurdly complex and distanced from its original design and the political world that it was designed for. I don't know of any system now that is even remotely as complicated. The complexity of the system, we don't often think about-we often tend to think that the problematic features of it are winner-take-all, and the fact that the small states get a slight advantage in the number of electoral votes. In fact, there are other problematic features as well, such as the ability of the state legislatures to change the process and the contingent election system. To some extent these different features were designed to offset or counterbalance one another. The very complexity of the system has meant that it's extremely difficult to change any one piece of it without changing something else.
The number of electoral votes a state gets is not entirely in proportion to its population relative to other states. Is that a modern problem?
A federal law would not have any impact on a constitutional provision. A constitutional amendment that guarantees every American citizen of voting age the right to vote for president would in effect create a conflict between two features of the Constitution. It would create a conflict between that amendment and the current design of the Electoral College, as outlined in Article II and the Twelfth Amendment. So a national right-to-vote amendment would-at the least-compel modification of at least one piece of the Electoral College, which is the piece that says that each state legislature can decide by itself the manner in which electors will be chosen.

That's in the Constitution. The number of electoral votes a state gets is proportional to its number of representatives and senators. At the Constitutional Convention in 1787, they had a lot of trouble figuring out how to choose the president. They didn't have any models. They really just could not agree. The default position was that Congress would choose the president. And in a series of straw votes in the course of the summer, that was the position that had the most backing. But then within a day or two or three of these straw votes, they realized that that was actually not a good idea. Because then you don't have separation of powers. And then the president would be beholden to Congress and that arrangement would set up all sorts of corruption.

The framers floated a whole lot of other ideas, including having the governors choose or having a national popular vote, which James Madison supported. But they couldn't reach agreement. It was a long hot summer in Philadelphia, everybody was tired, and at the end of the summer the convention went on vacation for a week. They left a small set of knotty, unresolved issues to a committee, the Committee on Unfinished Parts. It's that committee which comes up with the design of the Electoral College, which, with modifications, is then adopted by the entire convention. One way to understand what the Electoral College is, by the way, is that it's a replica of Congress in its composition. The same number of representatives and senators from each state. It's a replica of Congress, but it does not legislate. It only meets once and only has one order of business to do, and thus the problems of corruption and lack of separation of powers are solved. What that institutional design did from the outset was give a slight advantage to small states.
You mentioned just now the idea of a national popular vote. Your book discusses how, before the Civil War, a national popular vote was a nonstarter for slaveholding states because, obviously, slaves wouldn't be allowed to vote, and the voting power of southern states relative to other states would be diminished. What about after the Civil War? Why and how did race continue to play a role in opposition to a national popular vote?
The perversity of the design of the Electoral College is that a state gets the same number of electoral votes regardless of turnout in elections and regardless of how much voter suppression it engages in.

After the Civil War, after a period of contestation and Reconstruction, white supremacist governments returned to power in the South, and they disenfranchised African Americans who had been voting and wielding some political power. Thereafter the South, in terms of representation, benefits not from the [Constitution's] three-fifths clause, but effectively from a five-fifths clause. In other words, southern states get electoral votes in proportion to their entire black populations, but that population is not allowed to vote. Given that situation, the white South is fiercely resistant to a national popular vote, because it would do one of two things or perhaps both. It would put pressure on a state to try to maintain its political weight by enfranchising African Americans, which they did not want to do, or those states would lose influence in presidential elections, significant influence, because they would have many fewer voters. Under a national popular vote, the influence of a state is determined by the number of voters who cast ballots; under the Electoral College, it is determined by the state's population.
I don't think most people know that we almost got rid of the Electoral College in the late '60s.
The perversity of the design of the Electoral College is that a state gets the same number of electoral votes regardless of turnout in elections and regardless of how much voter suppression it engages in. There was only one southerner from 1890 to the 1960s who introduced an amendment calling for a national popular vote, and he ends up being pilloried for it. By the late 1940s, it's become a common view that the Electoral College is key to protecting the "southern way of life." In the end, at the moment at which the United States comes closest to adopting a national popular vote, which is in 1969-70, a constitutional amendment passes the House by an 82 percent vote, but when it gets to the Senate it is defeated by a filibuster by southern segregationist senators. The amendment's sponsors needed a two-thirds vote to end the filibuster, which meant that all it took was 34 senators to stop the national popular vote amendment.

I agree.

Do you have a point of view about whether and how we ought to reform the Electoral College?

My view is that our electoral system should embody certain key values. One is that all votes should count equally. No matter where you live, no matter who you are, all votes should count the same. The system should also be stable in the sense of not changing, not varying from one election to the next. And it should be transparent. The principles on which it's constructed should be transparent and widely regarded as fair by the public. Based on those values, I certainly think that the ideal system would be to have a national popular vote.
Do you think that if we continue seeing "wrong winner" elections, it will eventually force a full-blown legitimacy crisis? How long can that keep happening without something breaking?
Until the 2000 election, it was widely believed that if there was a "wrong winner" election, we'd get rid of the Electoral College.
To bring current events back full circle to some of the earlier history from your book, I was struck by some rhetoric of segregationist southern politicians about what would happen if there were a national popular vote. They'd say that their votes were going to be "debauched" by the votes of undesirables (i.e., black voters). It's impossible not to connect that to present-day rhetoric about voter fraud.
Very well said. Until the 2000 election, it was widely believed that if there was a "wrong winner" election, we'd get rid of the Electoral College. That's what everybody was saying in the 1970s, 1980s, 1990s. Well, we didn't get rid of it after 2000, and then, of course, we got 2016-when the gap between the popular and electoral votes was much larger than in 2000. I think that with the election of President Trump, and in part because of the nature of his presidency, we already have the beginnings of a legitimacy crisis. I think it would be intensified if it were to happen again. In the last few pages of the book I ruminate about this a bit. In an era of very sharp political polarization, you want to have an electoral system that is understood to be principled and fair. A lot of people doubt that now.
Anything else you'd like to add?
Yes, I think there is a direct line that goes from those kinds of sentiments to some of the support for Trump.

I want to emphasize that one of the discoveries that propelled me forward in [writing] the book, and that I think is very important, is that-contrary to conventional wisdom-the primary obstacle to reforming the Electoral College has not been the small states. The "small state" explanation has been conventional wisdom for about 40 years, and it was widely believed in the 1940s and 1950s as well. This view that the small states have prevented reform because of the additional electoral vote weight that they get is simply not true. It's not true in terms of who the leaders in the movements for change were, and it's not true in terms of the roll call votes when proposals for a national popular vote came to a vote in Congress.

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Why Do We Still Have the Electoral College? | Harvard Magazine

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Historian Alexander Keyssar on why the unpopular institution has prevailed

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U.S. Immigration Enforcement Policy and Its Impact

on Teaching and Learning in the Nation's Schools

Educators from more than 730 schools across 24 districts and 12 states participated in a survey on the Impact of Immigration Enforcement on Teaching and Learning in the Nation's Schools. Most of these schools were Title I schools serving disproportionately low-income students. Threats to students and their families make a challenging situation even more so. Data are provided for 3500 respondents. Many hundreds of respondents told us that their immigrant students (whether they were US born or not) were terrified that families and friends, and occasionally they themselves, would be picked up by ICE that it was, at times, very difficult for students to learn and teachers to teach. 90% of administrators reported observing emotional and behavioral problems among immigrant students. Two-thirds of respondents also reported that the fear and concern for classmates was affecting the education of students who were not targets of enforcement. More than one in seven respondents from the South reported that concern for classmates was affecting their students extensively. Respondents describe the students' worst fear being left alone or left to care for their younger siblings and not knowing how they would survive. Many respondents reported that family members of their students had been deported and that these incidents were well known to the whole class or school community, reinforcing fears many students held about their own situations or those of their friends. Some educators also reported students suffering deprivation because one or both parents had been deported or lost their jobs due to their immigration status and there was not enough money to buy food or meet basic needs.

This working paper was presented at a forum, "The Impact of Immigration Enforcement Policies on Teaching and Learning in America's Public Schools," in Washington, DC on February 28, 2018.

To view more about the event and research presentations, go to the agenda HERE.

To see the second part of this national survey, which focuses on the impact of immigration enforcement on the educators, click HERE.

In compliance with the UC Open Access Policy, this report has been made available on eScholarship:
http://escholarship.org/uc/item/3047w6xq

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U.S. Immigration Enforcement Policy and Its Impact on Teaching and Learning in the Nation's Schools

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This working paper presents findings from a national survey of educators from more than 730 schools across 24 districts and 12 states who responded to questions probing the Impact of Immigration Enforcement on Teaching and Learning in the Nation's Schools.

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What the Internet is doing to our brains

"Dave, stop. Stop, will you? Stop, Dave. Will you stop, Dave?" So the supercomputer HAL pleads with the implacable astronaut Dave Bowman in a famous and weirdly poignant scene toward the end of Stanley Kubrick's 2001: A Space Odyssey. Bowman, having nearly been sent to a deep-space death by the malfunctioning machine, is calmly, coldly disconnecting the memory circuits that control its artificial " brain. "Dave, my mind is going," HAL says, forlornly. "I can feel it. I can feel it."

I can feel it, too. Over the past few years I've had an uncomfortable sense that someone, or something, has been tinkering with my brain, remapping the neural circuitry, reprogramming the memory. My mind isn't going-so far as I can tell-but it's changing. I'm not thinking the way I used to think. I can feel it most strongly when I'm reading. Immersing myself in a book or a lengthy article used to be easy. My mind would get caught up in the narrative or the turns of the argument, and I'd spend hours strolling through long stretches of prose. That's rarely the case anymore. Now my concentration often starts to drift after two or three pages. I get fidgety, lose the thread, begin looking for something else to do. I feel as if I'm always dragging my wayward brain back to the text. The deep reading that used to come naturally has become a struggle.

I think I know what's going on. For more than a decade now, I've been spending a lot of time online, searching and surfing and sometimes adding to the great databases of the Internet. The Web has been a godsend to me as a writer. Research that once required days in the stacks or periodical rooms of libraries can now be done in minutes. A few Google searches, some quick clicks on hyperlinks, and I've got the telltale fact or pithy quote I was after. Even when I'm not working, I'm as likely as not to be foraging in the Web's info-thickets-reading and writing e-mails, scanning headlines and blog posts, watching videos and listening to podcasts, or just tripping from link to link to link. (Unlike footnotes, to which they're sometimes likened, hyperlinks don't merely point to related works; they propel you toward them.)

For me, as for others, the Net is becoming a universal medium, the conduit for most of the information that flows through my eyes and ears and into my mind. The advantages of having immediate access to such an incredibly rich store of information are many, and they've been widely described and duly applauded. "The perfect recall of silicon memory," Wired's Clive Thompson has written, "can be an enormous boon to thinking." But that boon comes at a price. As the media theorist Marshall McLuhan pointed out in the 1960s, media are not just passive channels of information. They supply the stuff of thought, but they also shape the process of thought. And what the Net seems to be doing is chipping away my capacity for concentration and contemplation. My mind now expects to take in information the way the Net distributes it: in a swiftly moving stream of particles. Once I was a scuba diver in the sea of words. Now I zip along the surface like a guy on a Jet Ski.

I'm not the only one. When I mention my troubles with reading to friends and acquaintances-literary types, most of them-many say they're having similar experiences. The more they use the Web, the more they have to fight to stay focused on long pieces of writing. Some of the bloggers I follow have also begun mentioning the phenomenon. Scott Karp, who writes a blog about online media, recently confessed that he has stopped reading books altogether. "I was a lit major in college, and used to be [a] voracious book reader," he wrote. "What happened?" He speculates on the answer: "What if I do all my reading on the web not so much because the way I read has changed, i.e. I'm just seeking convenience, but because the way I THINK has changed?"

Bruce Friedman, who blogs regularly about the use of computers in medicine, also has described how the Internet has altered his mental habits. "I now have almost totally lost the ability to read and absorb a longish article on the web or in print," he wrote earlier this year. A pathologist who has long been on the faculty of the University of Michigan Medical School, Friedman elaborated on his comment in a telephone conversation with me. His thinking, he said, has taken on a "staccato" quality, reflecting the way he quickly scans short passages of text from many sources online. "I can't read anymore," he admitted. "I've lost the ability to do that. Even a blog post of more than three or four paragraphs is too much to absorb. I skim it."

Anecdotes alone don't prove much. And we still await the long-term neurological and psychological experiments that will provide a definitive picture of how Internet use affects cognition. But a recently published study of online research habits, conducted by scholars from University College London, suggests that we may well be in the midst of a sea change in the way we read and think. As part of the five-year research program, the scholars examined computer logs documenting the behavior of visitors to two popular research sites, one operated by the British Library and one by a U.K. educational consortium, that provide access to journal articles, e-books, and other sources of written information. They found that people using the sites exhibited "a form of skimming activity," hopping from one source to another and rarely returning to any source they'd already visited. They typically read no more than one or two pages of an article or book before they would "bounce" out to another site. Sometimes they'd save a long article, but there's no evidence that they ever went back and actually read it. The authors of the study report:

It is clear that users are not reading online in the traditional sense; indeed there are signs that new forms of "reading" are emerging as users "power browse" horizontally through titles, contents pages and abstracts going for quick wins. It almost seems that they go online to avoid reading in the traditional sense.

Thanks to the ubiquity of text on the Internet, not to mention the popularity of text-messaging on cell phones, we may well be reading more today than we did in the 1970s or 1980s, when television was our medium of choice. But it's a different kind of reading, and behind it lies a different kind of thinking-perhaps even a new sense of the self. "We are not only what we read," says Maryanne Wolf, a developmental psychologist at Tufts University and the author of Proust and the Squid: The Story and Science of the Reading Brain. "We are how we read." Wolf worries that the style of reading promoted by the Net, a style that puts "efficiency" and "immediacy" above all else, may be weakening our capacity for the kind of deep reading that emerged when an earlier technology, the printing press, made long and complex works of prose commonplace. When we read online, she says, we tend to become "mere decoders of information." Our ability to interpret text, to make the rich mental connections that form when we read deeply and without distraction, remains largely disengaged.

Reading, explains Wolf, is not an instinctive skill for human beings. It's not etched into our genes the way speech is. We have to teach our minds how to translate the symbolic characters we see into the language we understand. And the media or other technologies we use in learning and practicing the craft of reading play an important part in shaping the neural circuits inside our brains. Experiments demonstrate that readers of ideograms, such as the Chinese, develop a mental circuitry for reading that is very different from the circuitry found in those of us whose written language employs an alphabet. The variations extend across many regions of the brain, including those that govern such essential cognitive functions as memory and the interpretation of visual and auditory stimuli. We can expect as well that the circuits woven by our use of the Net will be different from those woven by our reading of books and other printed works.

Sometime in 1882, Friedrich Nietzsche bought a typewriter-a Malling-Hansen Writing Ball, to be precise. His vision was failing, and keeping his eyes focused on a page had become exhausting and painful, often bringing on crushing headaches. He had been forced to curtail his writing, and he feared that he would soon have to give it up. The typewriter rescued him, at least for a time. Once he had mastered touch-typing, he was able to write with his eyes closed, using only the tips of his fingers. Words could once again flow from his mind to the page.

But the machine had a subtler effect on his work. One of Nietzsche's friends, a composer, noticed a change in the style of his writing. His already terse prose had become even tighter, more telegraphic. "Perhaps you will through this instrument even take to a new idiom," the friend wrote in a letter, noting that, in his own work, his "'thoughts' in music and language often depend on the quality of pen and paper."

"You are right," Nietzsche replied, "our writing equipment takes part in the forming of our thoughts." Under the sway of the machine, writes the German media scholar Friedrich A. Kittler , Nietzsche's prose "changed from arguments to aphorisms, from thoughts to puns, from rhetoric to telegram style."

The human brain is almost infinitely malleable. People used to think that our mental meshwork, the dense connections formed among the 100 billion or so neurons inside our skulls, was largely fixed by the time we reached adulthood. But brain researchers have discovered that that's not the case. James Olds, a professor of neuroscience who directs the Krasnow Institute for Advanced Study at George Mason University, says that even the adult mind "is very plastic." Nerve cells routinely break old connections and form new ones. "The brain," according to Olds, "has the ability to reprogram itself on the fly, altering the way it functions."

As we use what the sociologist Daniel Bell has called our "intellectual technologies"-the tools that extend our mental rather than our physical capacities-we inevitably begin to take on the qualities of those technologies. The mechanical clock, which came into common use in the 14th century, provides a compelling example. In Technics and Civilization, the historian and cultural critic Lewis Mumford described how the clock "disassociated time from human events and helped create the belief in an independent world of mathematically measurable sequences." The "abstract framework of divided time" became "the point of reference for both action and thought."

The clock's methodical ticking helped bring into being the scientific mind and the scientific man. But it also took something away. As the late MIT computer scientist Joseph Weizenbaum observed in his 1976 book, Computer Power and Human Reason: From Judgment to Calculation, the conception of the world that emerged from the widespread use of timekeeping instruments "remains an impoverished version of the older one, for it rests on a rejection of those direct experiences that formed the basis for, and indeed constituted, the old reality." In deciding when to eat, to work, to sleep, to rise, we stopped listening to our senses and started obeying the clock.

The process of adapting to new intellectual technologies is reflected in the changing metaphors we use to explain ourselves to ourselves. When the mechanical clock arrived, people began thinking of their brains as operating "like clockwork." Today, in the age of software, we have come to think of them as operating "like computers." But the changes, neuroscience tells us, go much deeper than metaphor. Thanks to our brain's plasticity, the adaptation occurs also at a biological level.

The Internet promises to have particularly far-reaching effects on cognition. In a paper published in 1936, the British mathematician Alan Turing proved that a digital computer, which at the time existed only as a theoretical machine, could be programmed to perform the function of any other information-processing device. And that's what we're seeing today. The Internet, an immeasurably powerful computing system, is subsuming most of our other intellectual technologies. It's becoming our map and our clock, our printing press and our typewriter, our calculator and our telephone, and our radio and TV.

When the Net absorbs a medium, that medium is re-created in the Net's image. It injects the medium's content with hyperlinks, blinking ads, and other digital gewgaws, and it surrounds the content with the content of all the other media it has absorbed. A new e-mail message, for instance, may announce its arrival as we're glancing over the latest headlines at a newspaper's site. The result is to scatter our attention and diffuse our concentration.

The Net's influence doesn't end at the edges of a computer screen, either. As people's minds become attuned to the crazy quilt of Internet media, traditional media have to adapt to the audience's new expectations. Television programs add text crawls and pop-up ads, and magazines and newspapers shorten their articles, introduce capsule summaries, and crowd their pages with easy-to-browse info-snippets. When, in March of this year, The New York Times decided to devote the second and third pages of every edition to article abstracts , its design director, Tom Bodkin, explained that the "shortcuts" would give harried readers a quick "taste" of the day's news, sparing them the "less efficient" method of actually turning the pages and reading the articles. Old media have little choice but to play by the new-media rules.

Never has a communications system played so many roles in our lives-or exerted such broad influence over our thoughts-as the Internet does today. Yet, for all that's been written about the Net, there's been little consideration of how, exactly, it's reprogramming us. The Net's intellectual ethic remains obscure.

About the same time that Nietzsche started using his typewriter, an earnest young man named Frederick Winslow Taylor carried a stopwatch into the Midvale Steel plant in Philadelphia and began a historic series of experiments aimed at improving the efficiency of the plant's machinists. With the approval of Midvale's owners, he recruited a group of factory hands, set them to work on various metalworking machines, and recorded and timed their every movement as well as the operations of the machines. By breaking down every job into a sequence of small, discrete steps and then testing different ways of performing each one, Taylor created a set of precise instructions-an "algorithm," we might say today-for how each worker should work. Midvale's employees grumbled about the strict new regime, claiming that it turned them into little more than automatons, but the factory's productivity soared.

More than a hundred years after the invention of the steam engine, the Industrial Revolution had at last found its philosophy and its philosopher. Taylor's tight industrial choreography-his "system," as he liked to call it-was embraced by manufacturers throughout the country and, in time, around the world. Seeking maximum speed, maximum efficiency, and maximum output, factory owners used time-and-motion studies to organize their work and configure the jobs of their workers. The goal, as Taylor defined it in his celebrated 1911 treatise, The Principles of Scientific Management, was to identify and adopt, for every job, the "one best method" of work and thereby to effect "the gradual substitution of science for rule of thumb throughout the mechanic arts." Once his system was applied to all acts of manual labor, Taylor assured his followers, it would bring about a restructuring not only of industry but of society, creating a utopia of perfect efficiency. "In the past the man has been first," he declared; "in the future the system must be first."

Taylor's system is still very much with us; it remains the ethic of industrial manufacturing. And now, thanks to the growing power that computer engineers and software coders wield over our intellectual lives, Taylor's ethic is beginning to govern the realm of the mind as well. The Internet is a machine designed for the efficient and automated collection, transmission, and manipulation of information, and its legions of programmers are intent on finding the "one best method"-the perfect algorithm-to carry out every mental movement of what we've come to describe as "knowledge work."

Google's headquarters, in Mountain View, California-the Googleplex-is the Internet's high church, and the religion practiced inside its walls is Taylorism. Google, says its chief executive, Eric Schmidt, is "a company that's founded around the science of measurement," and it is striving to "systematize everything" it does. Drawing on the terabytes of behavioral data it collects through its search engine and other sites, it carries out thousands of experiments a day, according to the Harvard Business Review, and it uses the results to refine the algorithms that increasingly control how people find information and extract meaning from it. What Taylor did for the work of the hand, Google is doing for the work of the mind.

The company has declared that its mission is "to organize the world's information and make it universally accessible and useful." It seeks to develop "the perfect search engine," which it defines as something that "understands exactly what you mean and gives you back exactly what you want." In Google's view, information is a kind of commodity, a utilitarian resource that can be mined and processed with industrial efficiency. The more pieces of information we can "access" and the faster we can extract their gist, the more productive we become as thinkers.

Where does it end? Sergey Brin and Larry Page, the gifted young men who founded Google while pursuing doctoral degrees in computer science at Stanford, speak frequently of their desire to turn their search engine into an artificial intelligence, a HAL-like machine that might be connected directly to our brains. "The ultimate search engine is something as smart as people-or smarter," Page said in a speech a few years back. "For us, working on search is a way to work on artificial intelligence." In a 2004 interview with Newsweek, Brin said, "Certainly if you had all the world's information directly attached to your brain, or an artificial brain that was smarter than your brain, you'd be better off." Last year, Page told a convention of scientists that Google is "really trying to build artificial intelligence and to do it on a large scale."

Such an ambition is a natural one, even an admirable one, for a pair of math whizzes with vast quantities of cash at their disposal and a small army of computer scientists in their employ. A fundamentally scientific enterprise, Google is motivated by a desire to use technology, in Eric Schmidt's words, "to solve problems that have never been solved before," and artificial intelligence is the hardest problem out there. Why wouldn't Brin and Page want to be the ones to crack it?

Still, their easy assumption that we'd all "be better off" if our brains were supplemented, or even replaced, by an artificial intelligence is unsettling. It suggests a belief that intelligence is the output of a mechanical process, a series of discrete steps that can be isolated, measured, and optimized. In Google's world, the world we enter when we go online, there's little place for the fuzziness of contemplation. Ambiguity is not an opening for insight but a bug to be fixed. The human brain is just an outdated computer that needs a faster processor and a bigger hard drive.

The idea that our minds should operate as high-speed data-processing machines is not only built into the workings of the Internet, it is the network's reigning business model as well. The faster we surf across the Web-the more links we click and pages we view-the more opportunities Google and other companies gain to collect information about us and to feed us advertisements. Most of the proprietors of the commercial Internet have a financial stake in collecting the crumbs of data we leave behind as we flit from link to link-the more crumbs, the better. The last thing these companies want is to encourage leisurely reading or slow, concentrated thought. It's in their economic interest to drive us to distraction.

Maybe I'm just a worrywart. Just as there's a tendency to glorify technological progress, there's a countertendency to expect the worst of every new tool or machine. In Plato's Phaedrus, Socrates bemoaned the development of writing. He feared that, as people came to rely on the written word as a substitute for the knowledge they used to carry inside their heads, they would, in the words of one of the dialogue's characters, "cease to exercise their memory and become forgetful." And because they would be able to "receive a quantity of information without proper instruction," they would "be thought very knowledgeable when they are for the most part quite ignorant." They would be "filled with the conceit of wisdom instead of real wisdom." Socrates wasn't wrong-the new technology did often have the effects he feared-but he was shortsighted. He couldn't foresee the many ways that writing and reading would serve to spread information, spur fresh ideas, and expand human knowledge (if not wisdom).

The arrival of Gutenberg's printing press, in the 15th century, set off another round of teeth gnashing. The Italian humanist Hieronimo Squarciafico worried that the easy availability of books would lead to intellectual laziness, making men "less studious" and weakening their minds. Others argued that cheaply printed books and broadsheets would undermine religious authority, demean the work of scholars and scribes, and spread sedition and debauchery. As New York University professor Clay Shirky notes, "Most of the arguments made against the printing press were correct, even prescient." But, again, the doomsayers were unable to imagine the myriad blessings that the printed word would deliver.

So, yes, you should be skeptical of my skepticism. Perhaps those who dismiss critics of the Internet as Luddites or nostalgists will be proved correct, and from our hyperactive, data-stoked minds will spring a golden age of intellectual discovery and universal wisdom. Then again, the Net isn't the alphabet, and although it may replace the printing press, it produces something altogether different. The kind of deep reading that a sequence of printed pages promotes is valuable not just for the knowledge we acquire from the author's words but for the intellectual vibrations those words set off within our own minds. In the quiet spaces opened up by the sustained, undistracted reading of a book, or by any other act of contemplation, for that matter, we make our own associations, draw our own inferences and analogies, foster our own ideas. Deep reading, as Maryanne Wolf argues, is indistinguishable from deep thinking.

If we lose those quiet spaces, or fill them up with "content," we will sacrifice something important not only in our selves but in our culture. In a recent essay, the playwright Richard Foreman eloquently described what's at stake:

I come from a tradition of Western culture, in which the ideal (my ideal) was the complex, dense and "cathedral-like" structure of the highly educated and articulate personality-a man or woman who carried inside themselves a personally constructed and unique version of the entire heritage of the West. [But now] I see within us all (myself included) the replacement of complex inner density with a new kind of self-evolving under the pressure of information overload and the technology of the "instantly available."

As we are drained of our "inner repertory of dense cultural inheritance," Foreman concluded, we risk turning into "'pancake people'-spread wide and thin as we connect with that vast network of information accessed by the mere touch of a button."

I'm haunted by that scene in 2001. What makes it so poignant, and so weird, is the computer's emotional response to the disassembly of its mind: its despair as one circuit after another goes dark, its childlike pleading with the astronaut-"I can feel it. I can feel it. I'm afraid"-and its final reversion to what can only be called a state of innocence. HAL's outpouring of feeling contrasts with the emotionlessness that characterizes the human figures in the film, who go about their business with an almost robotic efficiency. Their thoughts and actions feel scripted, as if they're following the steps of an algorithm. In the world of 2001, people have become so machinelike that the most human character turns out to be a machine. That's the essence of Kubrick's dark prophecy: as we come to rely on computers to mediate our understanding of the world, it is our own intelligence that flattens into artificial intelligence.
​​When you buy a book using a link on this page, we receive a commission. Thank you for supporting The Atlantic.

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independent world of mathematically measurable sequences

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10 Reasons to Get Vaccinated

Vaccines are safe.The US has a robust approval process in place to ensure that all licensed vaccines are safe. Potential side effects associated with vaccines are uncommon and much less severe than the diseases they prevent.

Talk with a healthcare professional about vaccines recommended for you and your family

Updated July 2023

Source: Centers for Disease Control and Prevention

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10 Reasons to Get Vaccinated Vaccine-preventable diseases have not gone away.The viruses and bacteria that cause illness and death still exist and can be passed on to those who are unvaccinated and not protected. While many diseases are no longer common in the US, global travel makes it easy for...

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In 2000 and 2016, the winners of the popular vote lost their bids for U.S. president after receiving fewer Electoral College votes than their opponents. To continue tracking how the public views the U.S. system for presidential elections, we surveyed 8,480 U.S. adults from July 10 to 16, 2023.

Everyone who took part in the current survey is a member of Pew Research Center's American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP's methodology.

Here are the questions used for this analysis, along with responses, and its methodology.

The Electoral College has played an outsize role in some recent U.S. elections. And a majority of Americans would welcome a change to the way presidents are elected, according to a new Pew Research Center survey.

Nearly two-thirds of U.S. adults (65%) say the way the president is elected should be changed so that the winner of the popular vote nationwide wins the presidency. A third favor keeping the current Electoral College system.

Public opinion on this question is essentially unchanged from last year, though Americans' support for using the popular vote to decide the presidency remains higher than it was a few years ago.

Explore Americans' views of the political system

This article draws from our major report on Americans' attitudes about the political system and political representation, conducted July 10-16, 2023. For more, explore:

The current electoral system in the United States allows for the possibility that the winner of the popular vote may not secure enough Electoral College votes to win the presidency. This occurred in both the 2000 and 2016 elections, which were won by George W. Bush and Donald Trump, respectively.

Partisan views over time

Democrats and Democratic-leaning independents are far more likely than Republicans and Republican leaners to support moving to a popular vote system for presidential elections (82% vs. 47%).

The share of Democrats saying this is nearly identical to last year but higher than in January 2021, a few weeks before President Joe Biden was sworn into office after winning both the Electoral College and the popular vote.

Republicans are fairly divided on this question: 52% support keeping the current Electoral College system, and 47% support moving to a popular vote system. GOP support for moving to a popular vote is the highest it's been in recent years - up from 37% in 2021 and just 27% in the days following the 2016 election.

Party and ideology

Nearly nine-in-ten liberal Democrats (88%) and about three-quarters of conservative and moderate Democrats (77%) say they would prefer presidents to be elected based on the popular vote.

Ideological differences are wider among Republicans. A clear majority - 63% - of conservative Republicans prefer keeping the current system, while 36% would change it.

The balance of opinion reverses among moderate and liberal Republicans (who make up a much smaller share of the Republican coalition). A majority of moderate and liberal Republicans (63%) say they would back the country moving to a popular vote for president.

Age

Younger adults are somewhat more supportive of changing the system than older adults. About seven-in-ten Americans under 50 (69%) support this. That share drops to about six-in-ten (58%) among those 65 and older.

Political engagement

Political engagement - being interested in and paying attention to politics - is associated with views about the Electoral College, particularly among Republicans.

Highly politically engaged Republicans overwhelmingly favor keeping the Electoral College: 72% say this, while 27% support moving to a popular vote system.

Republicans with a moderate level of engagement are more divided, with 51% wanting to keep the system as is and 48% wanting to change it. And a clear majority of Republicans with lower levels of political engagement (70%) back moving to a popular vote.

Differences by engagement are much less pronounced among Democrats. About eight-in-ten Democrats with low (78%) and medium (82%) levels of engagement favor changing the system, as do 86% of highly engaged Democrats.

Note: This is an update of posts previously published on Jan. 27, 2021 (written by Bradley Jones, a former senior researcher), and Aug. 5, 2022 (written by Jocelyn Kiley and Rebecca Salzer, a former intern). Here are the questions used for this analysis, along with responses, and its methodology.

A note on question wording

In January 2020, Pew Research Center ran a survey experiment that asked this question in two slightly different ways. One used the language that we and other organizations had used in prior years, with the reform option asking about "amending the Constitution so the candidate who receives the most votes nationwide wins the election." The other version asked about "changing the system so the candidate who receives the most votes nationwide wins the election." The January 2020 survey revealed no substantive differences between asking about "amending the Constitution" and "changing the system."

We conducted this experiment in large part because reforming the way presidents are selected does not technically require amending the Constitution. The National Popular Vote Interstate Compact, for example, could theoretically accomplish it without a constitutional amendment. Since there was no substantive difference in the survey results between the two question wordings, we have adopted the revised wording.

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Majority of Americans continue to favor moving away from Electoral College

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65% of U.S. adults say the way the president is elected should be changed so that the winner of the popular vote nationwide wins the presidency.

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The emergence of AI-image generators, such as DALL-E 2, Discord, Midjourney, and others, has stirred a controversy over whether art generated by artificial intelligence should be considered real art - and whether it could put artists and creators out of work. The Gazette spoke with faculty who are involved in the production of art - a writer, a film animator, an architect, a musician, and a mixed-media artist - to ask them whether they see AI as a threat or a collaborator or a tool to further their own creativity and imagination. The interviews have been edited for length and clarity.
Novelist and short-story writer Daphne Kalotay, instructor, Creative Writing & Literature Program, Harvard Extension School Saxophonist, percussionist, and composer Yosvany Terry, senior lecturer on music, director of Jazz Bands Independent animator Ruth Stella Lingford, senior lecturer on Art, Film, and Visual Studies
I recently judged a story contest, and reading the shortlist was a clue to the challenges AI might encounter in creating good writing versus great. The best of these human-written stories surprised me with 1) unique ways of viewing the world (personality), 2) linguistic originality, 3) inimitable details that could come only from personal experience. Other stories were written deftly yet lacked these elements of originality and surprise. AI is a superb mimic and quick learner and might easily write strong works in recognizable modes, and with linguistic experimentation if prompted, but - I think - will lack true insight and experience. Most at risk are commercial genres with easily recognizable styles and tropes. Even something like autofiction with its ruminating first-person narrators is easily mimicked - but what will be missing, I'm guessing, is genuine vision from living in a specific physical world.

When it comes to the performative aspect of music, artificial intelligence is not a concern to me. Music can transmit and represent emotion, and AI cannot do either of those things yet. And especially within jazz and creative music, music is in-the-moment composition, something that happens as musicians are collaborating onstage. That sense of interplay, or the ability to react in the moment, is something that artificial intelligence can't reproduce because to do that requires being intelligent and having the agency to use your curiosity and your musical vocabulary. Only then, you can be able to react and create music in the moment.

With regards to composition, we know that AI has been used to compose music for film and television for quite a few years. That is a concern because AI is doing the work that musicians used to do. But when you hear those compositions by AI, they lack surprise, emotion, and even silence. I love dramatism in music, and for me, emotion in music is important, and AI is not there yet. As for how music gets to people, this is where we have seen tremendous changes. The many music platforms we have, such as Spotify, YouTube, iTunes, etc., use algorithm-based features, and they make musical recommendations based on what you listen to. We all know that AI is behind it. Musicians would like to see algorithms suggest composers who are less known rather than those who are already popular. The new technology should democratize the field, make sure that people have access to things that are outside the mainstream, and learn to recognize musical traditions that are not only from the Western world.
Mixed-media artist Matt Saunders '97, Architect and urban planner Moshe Safdie, design critic in architecture, Graduate School of Design professor and director of undergraduate studies, Department of Art, Film and Visual Studies
It's important to welcome AI with open arms to try to understand what AI can do for us and work with it in creative ways. Any new technology is first seen as a threat to the status quo, like the way radio was received when it first aired. There have always been movements opposing those innovations. I don't think AI is different, but we must remember that all these innovations are man-made, and as humans we can create and innovate. As a musician, I think we should open our eyes, ears, and arms to work with the new knowledge and innovation that AI can bring.

Often, when I speak to my contemporaries in the animation world, they prefer not to think much about AI. But of course, we need to.

Generally speaking, AI does threaten jobs in the animation industry. I'm told that it is already being used in some large studios. But it will also be a collaborator.

I am told by a knowledgeable friend that it would be possible to train AI on my style and have it work as an assistant in my work. I can see how this would be useful, but, as I actually like the repetitiveness of the animation process, the idea is not very appealing, at least for my own personal work. But it might be a tempting idea in commercial jobs, though I have not explored it yet. In my personal work, the repetition of hand drawing (I draw on a digital tablet) gives me access to a less conscious, intentional side of the creative process, which I think makes the work richer and more nuanced.

Although it is maybe far-fetched to talk about AI as creative or imaginative, the melding of images from different sources, with large elements of the random, closely approximates some aspects of the creative process. AI is acting like a sort of collective unconscious, and I do find some of what it produces very interesting. I don't suppose that animated films made entirely by AI would be very successful, but used with human guidance throughout the process, they could probably work very well.

I recently attended the Annecy Festival, and many people I spoke to there talked about "riding the shark" - harnessing the power of AI while staying in control. People also predicted the audience's fatigue with the look of AI. It was noticeable that a high proportion of films shown at this year's festival were made using recognizably analog techniques - stop-motion, animated painting, etc. - and this seems like a reaction against the dominance of computer-generated imagery in the last years. We do seem to want to see evidence of the human hand. Of course, AI may soon be able to simulate that absolutely seamlessly!

A programmer I spoke to was of the opinion that the panic over the power of AI is hype-manufactured by its makers to disguise its limitations and make it seem sexier. This person saw it going the same way as VR, seeming to offer unlimited potential, but then fizzling out. I would like to think that he's right, but I can't quite believe it.

To the question of whether AI can be a threat or collaborator, I might respond that every new technology upends conventions and delivers not only new possibilities but a new kind of material intelligence. I am sure many artists will be intrigued by the "agency" of AI and seek ways to grapple or collaborate with it. Many already are. And we should be grateful to be challenged and knocked out of our habits and assumptions! Most of the things that worry me about this fall into the realm of the social and ethical. I hope there are great artists to help us imagine around and work with this new reality.

As to whether it could be creative or comparable, I end up in circular thinking. Art means what we ascribe to it. It can be a provocation, but it is essentially always part of a conversation. Many artists are already using the inventions (and provocations) of AI in works of great substance, but of course the artists are still the ones bringing it into the room. If things change, maybe that will change too.

I've been following AI since the 1970s, when Marvin Minsky [one of the pioneers of artificial intelligence] and I spent time together. But AI has emerged as the product of an extraordinary computation capacity; algorithm, which is quite different from what Minsky had imagined when he started working on it. For him, AI was the science of making machines be as intelligent as humans, with the capacity for human reasoning.

Right now, it seems to me that AI has an extraordinary capacity to analyze, but I don't see it yet doing the kinds of things we, architects, do in our head when we design, which involves taking into account a very large number of variables and sorting them out. And yet, as an architect, I think AI can change our lives. If we ask the right questions, AI is going to give us significant answers. For example, I can ask AI, "I have this building that's sitting on this particular site, and I'd like to optimize the sun, the shadow pattern, and I'd like to position the windows at the place of optimal orientation," and I think AI could give us a helpful response. We could perfect our designs or improve them based on the response. But I don't think it's going to give us the whole answer. For example, if you ask AI to make a garden with hideaways, clearings, and planting arrangements for all the seasons, I think it'll do that very well. But if you want to have a garden arranged in a way that is magical and pleases you, I'm not sure it can do that.

I'm not frightened of AI at all. I'm intrigued. I think AI will be able to create graphic presentations of extraordinary beauty and interest, but that leads us to the question of what is art. Art has an element that is spiritual, emotional. It is something that happens when you look at Picasso's "Guernica" and makes you think of the cruelty of humans to humans, or when you look at a Monet piece, and you feel the unity with nature. In terms of art created by AI, I don't think we can call it art. I don't see AI yet doing the kind of creative things we do. I can see that it composes a piece of music, but I don't think it could create Beethoven's last sonatas on its own. AI can imitate something that's already been created and regurgitate it in another format, but that is not an original work.

When our firm designed the Jewel Changi Airport in Singapore, our idea was to have retail shops, airport facilities, and an attraction. The idea that this attraction should be a magical garden came from me. I wonder if AI could have come up with the same idea. I don't think so, but who knows? That would be a very interesting test.

I don't know if Minsky's dream of artificial intelligence is attainable because it assumed that AI attains consciousness and independence of thoughts. Therefore, in some ways, artificial intelligence is a misnomer. Maybe I'm underestimating its capacity, but from where I stand as an amateur, because I'm an architect, not a mathematician, it seems to me that intelligence assumes a certain independence and consciousness, which I don't think AI has right now.

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Is art generated by artificial intelligence real art?

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Writer, animator, architect, musician, and mixed-media artist detail the potential value and limit of works produced by AI.

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Research

Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it. ¹ A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.

Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance.

In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values. ²

In order to maximize AI benefits, we recommend nine steps for going forward:

Encourage greater data access for researchers without compromising users' personal privacy,

invest more government funding in unclassified AI research,

promote new models of digital education and AI workforce development so employees have the skills needed in the 21st-century economy,

create a federal AI advisory committee to make policy recommendations,

engage with state and local officials so they enact effective policies,

regulate broad AI principles rather than specific algorithms,

take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,

maintain mechanisms for human oversight and control, and

penalize malicious AI behavior and promote cybersecurity.

Intentionality

Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.

Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.

Intelligence

AI generally is undertaken in conjunction with machine learning and data analytics. ⁵ Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues. All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

Adaptability

AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved "experience" is immediately and fully transferable to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways. ⁶

One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that "artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030." ⁷ That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that "AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption." ⁸ Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

Finance

Investments in financial AI in the United States tripled between 2013 and 2014 to a total of $12.2 billion.⁹ According to observers in that sector, "Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check."¹⁰ In addition, there are so-called robo-advisers that "create personalized investment portfolios, obviating the need for stockbrokers and financial advisers."¹¹ These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.

Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels. ¹⁴

National security

Artificial intelligence will accelerate the traditional process of warfare so rapidly that a new term has been coined: hyperwar.

The big data analytics associated with AI will profoundly affect intelligence analysis, as massive amounts of data are sifted in near real time-if not eventually in real time-thereby providing commanders and their staffs a level of intelligence analysis and productivity heretofore unseen. Command and control will similarly be affected as human commanders delegate certain routine, and in special circumstances, key decisions to AI platforms, reducing dramatically the time associated with the decision and subsequent action. In the end, warfare is a time competitive process, where the side able to decide the fastest and move most quickly to execution will generally prevail. Indeed, artificially intelligent intelligence systems, tied to AI-assisted command and control systems, can move decision support and decisionmaking to a speed vastly superior to the speeds of the traditional means of waging war. So fast will be this process, especially if coupled to automatic decisions to launch artificially intelligent autonomous weapons systems capable of lethal outcomes, that a new term has been coined specifically to embrace the speed at which war will be waged: hyperwar.

While the ethical and legal debate is raging over whether America will ever wage war with artificially intelligent autonomous lethal systems, the Chinese and Russians are not nearly so mired in this debate, and we should anticipate our need to defend against these systems operating at hyperwar speeds. The challenge in the West of where to position "humans in the loop" in a hyperwar scenario will ultimately dictate the West's capacity to be competitive in this new form of conflict. ¹⁷

Just as AI will profoundly affect the speed of warfare, the proliferation of zero day or zero second cyber threats as well as polymorphic malware will challenge even the most sophisticated signature-based cyber protection. This forces significant improvement to existing cyber defenses. Increasingly, vulnerable systems are migrating, and will need to shift to a layered approach to cybersecurity with cloud-based, cognitive AI platforms. This approach moves the community toward a "thinking" defensive capability that can defend networks through constant training on known threats. This capability includes DNA-level analysis of heretofore unknown code, with the possibility of recognizing and stopping inbound malicious code by recognizing a string component of the file. This is how certain key U.S.-based systems stopped the debilitating "WannaCry" and "Petya" viruses.

Preparing for hyperwar and defending critical cyber networks must become a high priority because China, Russia, North Korea, and other countries are putting substantial resources into AI. In 2017, China's State Council issued a plan for the country to "build a domestic industry worth almost $150 billion" by 2030.¹⁸ As an example of the possibilities, the Chinese search firm Baidu has pioneered a facial recognition application that finds missing people. In addition, cities such as Shenzhen are providing up to $1 million to support AI labs. That country hopes AI will provide security, combat terrorism, and improve speech recognition programs.¹⁹ The dual-use nature of many AI algorithms will mean AI research focused on one sector of society can be rapidly modified for use in the security sector as well.²⁰

Health care

AI tools are helping designers improve computational sophistication in health care. For example, Merantix is a German company that applies deep learning to medical issues. It has an application in medical imaging that "detects lymph nodes in the human body in Computer Tomography (CT) images." ²¹ According to its developers, the key is labeling the nodes and identifying small lesions or growths that could be problematic. Humans can do this, but radiologists charge $100 per hour and may be able to carefully read only four images an hour. If there were 10,000 images, the cost of this process would be $250,000, which is prohibitively expensive if done by humans.

What deep learning can do in this situation is train computers on data sets to learn what a normal-looking versus an irregular-appearing lymph node is. After doing that through imaging exercises and honing the accuracy of the labeling, radiological imaging specialists can apply this knowledge to actual patients and determine the extent to which someone is at risk of cancerous lymph nodes. Since only a few are likely to test positive, it is a matter of identifying the unhealthy versus healthy node.

AI has been applied to congestive heart failure as well, an illness that afflicts 10 percent of senior citizens and costs $35 billion each year in the United States. AI tools are helpful because they "predict in advance potential challenges ahead and allocate resources to patient education, sensing, and proactive interventions that keep patients out of the hospital." ²²

Criminal justice

AI is being deployed in the criminal justice area. The city of Chicago has developed an AI-driven "Strategic Subject List" that analyzes people who have been arrested for their risk of becoming future perpetrators. It ranks 400,000 people on a scale of 0 to 500, using items such as age, criminal activity, victimization, drug arrest records, and gang affiliation. In looking at the data, analysts found that youth is a strong predictor of violence, being a shooting victim is associated with becoming a future perpetrator, gang affiliation has little predictive value, and drug arrests are not significantly associated with future criminal activity. ²³

Judicial experts claim AI programs reduce human bias in law enforcement and leads to a fairer sentencing system. R Street Institute Associate Caleb Watney writes:

Empirically grounded questions of predictive risk analysis play to the strengths of machine learning, automated reasoning and other forms of AI. One machine-learning policy simulation concluded that such programs could be used to cut crime up to 24.8 percent with no change in jailing rates, or reduce jail populations by up to 42 percent with no increase in crime rates. ²⁴

However, critics worry that AI algorithms represent "a secret system to punish citizens for crimes they haven't yet committed. The risk scores have been used numerous times to guide large-scale roundups." ²⁵ The fear is that such tools target people of color unfairly and have not helped Chicago reduce the murder wave that has plagued it in recent years.

Transportation

Transportation represents an area where AI and machine learning are producing major innovations. Research by Cameron Kerry and Jack Karsten of the Brookings Institution has found that over $80 billion was invested in autonomous vehicle technology between August 2014 and June 2017. Those investments include applications both for autonomous driving and the core technologies vital to that sector. ²⁸

Autonomous vehicles-cars, trucks, buses, and drone delivery systems-use advanced technological capabilities. Those features include automated vehicle guidance and braking, lane-changing systems, the use of cameras and sensors for collision avoidance, the use of AI to analyze information in real time, and the use of high-performance computing and deep learning systems to adapt to new circumstances through detailed maps. ²⁹

Light detection and ranging systems (LIDARs) and AI are key to navigation and collision avoidance. LIDAR systems combine light and radar instruments. They are mounted on the top of vehicles that use imaging in a 360-degree environment from a radar and light beams to measure the speed and distance of surrounding objects. Along with sensors placed on the front, sides, and back of the vehicle, these instruments provide information that keeps fast-moving cars and trucks in their own lane, helps them avoid other vehicles, applies brakes and steering when needed, and does so instantly so as to avoid accidents.

Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. This means that software is the key-not the physical car or truck itself.

Ride-sharing companies are very interested in autonomous vehicles. They see advantages in terms of customer service and labor productivity. All of the major ride-sharing companies are exploring driverless cars. The surge of car-sharing and taxi services-such as Uber and Lyft in the United States, Daimler's Mytaxi and Hailo service in Great Britain, and Didi Chuxing in China-demonstrate the opportunities of this transportation option. Uber recently signed an agreement to purchase 24,000 autonomous cars from Volvo for its ride-sharing service. ³²

However, the ride-sharing firm suffered a setback in March 2018 when one of its autonomous vehicles in Arizona hit and killed a pedestrian. Uber and several auto manufacturers immediately suspended testing and launched investigations into what went wrong and how the fatality could have occurred. ³³ Both industry and consumers want reassurance that the technology is safe and able to deliver on its stated promises. Unless there are persuasive answers, this accident could slow AI advancements in the transportation sector.

Smart cities

Metropolitan governments are using AI to improve urban service delivery. For example, according to Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson:

The Cincinnati Fire Department is using data analytics to optimize medical emergency responses. The new analytics system recommends to the dispatcher an appropriate response to a medical emergency call-whether a patient can be treated on-site or needs to be taken to the hospital-by taking into account several factors, such as the type of call, location, weather, and similar calls. ³⁴

Since it fields 80,000 requests each year, Cincinnati officials are deploying this technology to prioritize responses and determine the best ways to handle emergencies. They see AI as a way to deal with large volumes of data and figure out efficient ways of responding to public requests. Rather than address service issues in an ad hoc manner, authorities are trying to be proactive in how they provide urban services.

Cincinnati is not alone. A number of metropolitan areas are adopting smart city applications that use AI to improve service delivery, environmental planning, resource management, energy utilization, and crime prevention, among other things. For its smart cities index, the magazine Fast Company ranked American locales and found Seattle, Boston, San Francisco, Washington, D.C., and New York City as the top adopters. Seattle, for example, has embraced sustainability and is using AI to manage energy usage and resource management. Boston has launched a "City Hall To Go" that makes sure underserved communities receive needed public services. It also has deployed "cameras and inductive loops to manage traffic and acoustic sensors to identify gun shots." San Francisco has certified 203 buildings as meeting LEED sustainability standards. ³⁵

Through these and other means, metropolitan areas are leading the country in the deployment of AI solutions. Indeed, according to a National League of Cities report, 66 percent of American cities are investing in smart city technology. Among the top applications noted in the report are "smart meters for utilities, intelligent traffic signals, e-governance applications, Wi-Fi kiosks, and radio frequency identification sensors in pavement." ³⁶

These examples from a variety of sectors demonstrate how AI is transforming many walks of human existence. The increasing penetration of AI and autonomous devices into many aspects of life is altering basic operations and decisionmaking within organizations, and improving efficiency and response times.

At the same time, though, these developments raise important policy, regulatory, and ethical issues. For example, how should we promote data access? How do we guard against biased or unfair data used in algorithms? What types of ethical principles are introduced through software programming, and how transparent should designers be about their choices? What about questions of legal liability in cases where algorithms cause harm? ³⁷

The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues.

Data access problems

The key to getting the most out of AI is having a "data-friendly ecosystem with unified standards and cross-platform sharing." AI depends on data that can be analyzed in real time and brought to bear on concrete problems. Having data that are "accessible for exploration" in the research community is a prerequisite for successful AI development. ³⁸

According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. In this regard, the United States has a substantial advantage over China. Global ratings on data openness show that U.S. ranks eighth overall in the world, compared to 93 for China. ³⁹

But right now, the United States does not have a coherent national data strategy. There are few protocols for promoting research access or platforms that make it possible to gain new insights from proprietary data. It is not always clear who owns data or how much belongs in the public sphere. These uncertainties limit the innovation economy and act as a drag on academic research. In the following section, we outline ways to improve data access for researchers.

Biases in data and algorithms

Racial issues also come up with facial recognition software. Most such systems operate by comparing a person's face to a range of faces in a large database. As pointed out by Joy Buolamwini of the Algorithmic Justice League, "If your facial recognition data contains mostly Caucasian faces, that's what your program will learn to recognize." ⁴² Unless the databases have access to diverse data, these programs perform poorly when attempting to recognize African-American or Asian-American features.

Many historical data sets reflect traditional values, which may or may not represent the preferences wanted in a current system. As Buolamwini notes, such an approach risks repeating inequities of the past:

The rise of automation and the increased reliance on algorithms for high-stakes decisions such as whether someone get insurance or not, your likelihood to default on a loan or somebody's risk of recidivism means this is something that needs to be addressed. Even admissions decisions are increasingly automated-what school our children go to and what opportunities they have. We don't have to bring the structural inequalities of the past into the future we create. ⁴³

AI ethics and transparency

Algorithms embed ethical considerations and value choices into program decisions. As such, these systems raise questions concerning the criteria used in automated decisionmaking. Some people want to have a better understanding of how algorithms function and what choices are being made. ⁴⁴

In the United States, many urban schools use algorithms for enrollment decisions based on a variety of considerations, such as parent preferences, neighborhood qualities, income level, and demographic background. According to Brookings researcher Jon Valant, the New Orleans-based Bricolage Academy "gives priority to economically disadvantaged applicants for up to 33 percent of available seats. In practice, though, most cities have opted for categories that prioritize siblings of current students, children of school employees, and families that live in school's broad geographic area." ⁴⁵ Enrollment choices can be expected to be very different when considerations of this sort come into play.

Depending on how AI systems are set up, they can facilitate the redlining of mortgage applications, help people discriminate against individuals they don't like, or help screen or build rosters of individuals based on unfair criteria. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers. ⁴⁶

For these reasons, the EU is implementing the General Data Protection Regulation (GDPR) in May 2018. The rules specify that people have "the right to opt out of personally tailored ads" and "can contest 'legal or similarly significant' decisions made by algorithms and appeal for human intervention" in the form of an explanation of how the algorithm generated a particular outcome. Each guideline is designed to ensure the protection of personal data and provide individuals with information on how the "black box" operates. ⁴⁷

Legal liability

There are questions concerning the legal liability of AI systems. If there are harms or infractions (or fatalities in the case of driverless cars), the operators of the algorithm likely will fall under product liability rules. A body of case law has shown that the situation's facts and circumstances determine liability and influence the kind of penalties that are imposed. Those can range from civil fines to imprisonment for major harms. ⁴⁸ The Uber-related fatality in Arizona will be an important test case for legal liability. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing. It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer. Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved.

In non-transportation areas, digital platforms often have limited liability for what happens on their sites. For example, in the case of Airbnb, the firm "requires that people agree to waive their right to sue, or to join in any class-action lawsuit or class-action arbitration, to use the service." By demanding that its users sacrifice basic rights, the company limits consumer protections and therefore curtails the ability of people to fight discrimination arising from unfair algorithms. ⁴⁹ But whether the principle of neutral networks holds up in many sectors is yet to be determined on a widespread basis.

In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.

Improving data access

The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity. ⁵⁰ Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.

In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information. There is a variety of ways researchers could gain data access. One is through voluntary agreements with companies holding proprietary data. Facebook, for example, recently announced a partnership with Stanford economist Raj Chetty to use its social media data to explore inequality. ⁵¹ As part of the arrangement, researchers were required to undergo background checks and could only access data from secured sites in order to protect user privacy and security.

In the U.S., there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design.

Google long has made available search results in aggregated form for researchers and the general public. Through its "Trends" site, scholars can analyze topics such as interest in Trump, views about democracy, and perspectives on the overall economy. ⁵² That helps people track movements in public interest and identify topics that galvanize the general public.

Twitter makes much of its tweets available to researchers through application programming interfaces, commonly referred to as APIs. These tools help people outside the company build application software and make use of data from its social media platform. They can study patterns of social media communications and see how people are commenting on or reacting to current events.

In some sectors where there is a discernible public benefit, governments can facilitate collaboration by building infrastructure that shares data. For example, the National Cancer Institute has pioneered a data-sharing protocol where certified researchers can query health data it has using de-identified information drawn from clinical data, claims information, and drug therapies. That enables researchers to evaluate efficacy and effectiveness, and make recommendations regarding the best medical approaches, without compromising the privacy of individual patients.

There could be public-private data partnerships that combine government and business data sets to improve system performance. For example, cities could integrate information from ride-sharing services with its own material on social service locations, bus lines, mass transit, and highway congestion to improve transportation. That would help metropolitan areas deal with traffic tie-ups and assist in highway and mass transit planning.

Increase government investment in AI

Promote digital education and workforce development

As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers. These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development.

For these reasons, both state and federal governments have been investing in AI human capital. For example, in 2017, the National Science Foundation funded over 6,500 graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education. ⁵⁷ The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.

But there also needs to be substantial changes in the process of learning itself. It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. AI will reconfigure how society and the economy operate, and there needs to be "big picture" thinking on what this will mean for ethics, governance, and societal impact. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas.

One example of new ways to prepare students for a digital future is IBM's Teacher Advisor program, utilizing Watson's free online tools to help teachers bring the latest knowledge into the classroom. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom. ⁵⁸ As such, they are precursors of new educational environments that need to be created.

Create a federal AI advisory committee

Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology.

In order to move forward in this area, several members of Congress have introduced the "Future of Artificial Intelligence Act," a bill designed to establish broad policy and legal principles for AI. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence. The legislation provides a mechanism for the federal government to get advice on ways to promote a "climate of investment and innovation to ensure the global competitiveness of the United States," "optimize the development of artificial intelligence to address the potential growth, restructuring, or other changes in the United States workforce," "support the unbiased development and application of artificial intelligence," and "protect the privacy rights of individuals." ⁵⁹

Among the specific questions the committee is asked to address include the following: competitiveness, workforce impact, education, ethics training, data sharing, international cooperation, accountability, machine learning bias, rural impact, government efficiency, investment climate, job impact, bias, and consumer impact. The committee is directed to submit a report to Congress and the administration 540 days after enactment regarding any legislative or administrative action needed on AI.

This legislation is a step in the right direction, although the field is moving so rapidly that we would recommend shortening the reporting timeline from 540 days to 180 days. Waiting nearly two years for a committee report will certainly result in missed opportunities and a lack of action on important issues. Given rapid advances in the field, having a much quicker turnaround time on the committee analysis would be quite beneficial.

Engage with state and local officials

According to the legislation's developers, city officials want to know how these algorithms work and make sure there is sufficient AI transparency and accountability. In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage. It is scheduled to report back to the mayor on a range of AI policy, legal, and regulatory issues by late 2019.

Some observers already are worrying that the taskforce won't go far enough in holding algorithms accountable. For example, Julia Powles of Cornell Tech and New York University argues that the bill originally required companies to make the AI source code available to the public for inspection, and that there be simulations of its decisionmaking using actual data. After criticism of those provisions, however, former Councilman James Vacca dropped the requirements in favor of a task force studying these issues. He and other city officials were concerned that publication of proprietary information on algorithms would slow innovation and make it difficult to find AI vendors who would work with the city. ⁶² It remains to be seen how this local task force will balance issues of innovation, privacy, and transparency.

Regulate broad objectives more than specific algorithms

The GDPR being implemented in Europe place severe restrictions on the use of artificial intelligence and machine learning. According to published guidelines, "Regulations prohibit any automated decision that 'significantly affects' EU citizens. This includes techniques that evaluates a person's 'performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements.'" ⁶⁵ In addition, these new rules give citizens the right to review how digital services made specific algorithmic choices affecting people.

By taking a restrictive stance on issues of data collection and analysis, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

It makes more sense to think about the broad objectives desired in AI and enact policies that advance them, as opposed to governments trying to crack open the "black boxes" and see exactly how specific algorithms operate. Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

Take biases seriously

Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole.

For these advances to be widely adopted, more transparency is needed in how AI systems operate. Andrew Burt of Immuta argues, "The key problem confronting predictive analytics is really transparency. We're in a world where data science operations are taking on increasingly important tasks, and the only thing holding them back is going to be how well the data scientists who train the models can explain what it is their models are doing." ⁶⁶

Maintaining mechanisms for human oversight and control

Some individuals have argued that there needs to be avenues for humans to exercise oversight and control of AI systems. For example, Allen Institute for Artificial Intelligence CEO Oren Etzioni argues there should be rules for regulating these systems. First, he says, AI must be governed by all the laws that already have been developed for human behavior, including regulations concerning "cyberbullying, stock manipulation or terrorist threats," as well as "entrap[ping] people into committing crimes." Second, he believes that these systems should disclose they are automated systems and not human beings. Third, he states, "An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information." ⁶⁷ His rationale is that these tools store so much data that people have to be cognizant of the privacy risks posed by AI.

In the same vein, the IEEE Global Initiative has ethical guidelines for AI and autonomous systems. Its experts suggest that these models be programmed with consideration for widely accepted human norms and rules for behavior. AI algorithms need to take into effect the importance of these norms, how norm conflict can be resolved, and ways these systems can be transparent about norm resolution. Software designs should be programmed for "nondeception" and "honesty," according to ethics experts. When failures occur, there must be mitigation mechanisms to deal with the consequences. In particular, AI must be sensitive to problems such as bias, discrimination, and fairness. ⁶⁸

A group of machine learning experts claim it is possible to automate ethical decisionmaking. Using the trolley problem as a moral dilemma, they ask the following question: If an autonomous car goes out of control, should it be programmed to kill its own passengers or the pedestrians who are crossing the street? They devised a "voting-based system" that asked 1.3 million people to assess alternative scenarios, summarized the overall choices, and applied the overall perspective of these individuals to a range of vehicular possibilities. That allowed them to automate ethical decisionmaking in AI algorithms, taking public preferences into account. ⁶⁹ This procedure, of course, does not reduce the tragedy involved in any kind of fatality, such as seen in the Uber case, but it provides a mechanism to help AI developers incorporate ethical considerations in their planning.

Penalize malicious behavior and promote cybersecurity

To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decisionmaking, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.

The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.

Yet the manner in which AI systems unfold has major implications for society as a whole. It matters how policy issues are addressed, ethical conflicts are reconciled, legal realities are resolved, and how much transparency is required in AI and data analytic solutions. ⁷⁴ Human choices about software development affect the way in which decisions are made and the manner in which they are integrated into organizational routines. Exactly how these processes are executed need to be better understood because they will have substantial impact on the general public soon, and for the foreseeable future. AI may well be a revolution in human affairs, and become the single most influential human innovation in history.
Note: We appreciate the research assistance of Grace Gilberg, Jack Karsten, Hillary Schaub, and Kristjan Tomasson on this project. The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars. Support for this publication was generously provided by Amazon. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment. John R. Allen is a member of the Board of Advisors of Amida Technology and on the Board of Directors of Spark Cognition. Both companies work in fields discussed in this piece.

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How artificial intelligence is transforming the world | Brookings

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Darrell West and John Allen examine the societal and political aspects of developing artificial intelligence technologies.

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Abortions

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The Ethics of Animal Research: Exploring the Controversy

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Abstract. An estimated 100 million nonhuman vertebrates worldwide-including primates, dogs, cats, rabbits, hamsters, birds, rats, and mice-are bred, captur