Tag Archives: have
Powerful surveillance cameras have crept into public spaces. We are filmed and photographed hundreds of times a day. To further raise the stakes, the resulting video footage is fed to new forms of artificial intelligence software that can recognize faces in real time, read license plates, even instantly detect when a particular pre-defined action or activity takes place in front of a camera.
As most modern cities have quietly become surveillance cities, the law has been slow to catch up. While we wait for robust legal frameworks to emerge, the best way to protect our civil liberties right now is to fight technology with technology. All cities should place local surveillance video into a public cloud-based data trust. Here’s how it would work.
In Public Data We Trust
To democratize surveillance, every city should implement three simple rules. First, anyone who aims a camera at public space must upload that day’s haul of raw video file (and associated camera meta-data) into a cloud-based repository. Second, this cloud-based repository must have open APIs and a publicly-accessible log file that records search histories and tracks who has accessed which video files. And third, everyone in the city should be given the same level of access rights to the stored video data—no exceptions.
This kind of public data repository is called a “data trust.” Public data trusts are not just wishful thinking. Different types of trusts are already in successful use in Estonia and Barcelona, and have been proposed as the best way to store and manage the urban data that will be generated by Alphabet’s planned Sidewalk Labs project in Toronto.
It’s true that few people relish the thought of public video footage of themselves being looked at by strangers and friends, by ex-spouses, potential employers, divorce attorneys, and future romantic prospects. In fact, when I propose this notion when I give talks about smart cities, most people recoil in horror. Some turn red in the face and jeer at my naiveté. Others merely blink quietly in consternation.
The reason we should take this giant step towards extreme transparency is to combat the secrecy that surrounds surveillance. Openness is a powerful antidote to oppression. Edward Snowden summed it up well when he said, “Surveillance is not about public safety, it’s about power. It’s about control.”
Let Us Watch Those Watching Us
If public surveillance video were put back into the hands of the people, citizens could watch their government as it watches them. Right now, government cameras are controlled by the state. Camera locations are kept secret, and only the agencies that control the cameras get to see the footage they generate.
Because of these information asymmetries, civilians have no insight into the size and shape of the modern urban surveillance infrastructure that surrounds us, nor the uses (or abuses) of the video footage it spawns. For example, there is no swift and efficient mechanism to request a copy of video footage from the cameras that dot our downtown. Nor can we ask our city’s police force to show us a map that documents local traffic camera locations.
By exposing all public surveillance videos to the public gaze, cities could give regular people tools to assess the size, shape, and density of their local surveillance infrastructure and neighborhood “digital dragnet.” Using the meta-data that’s wrapped around video footage, citizens could geo-locate individual cameras onto a digital map to generate surveillance “heat maps.” This way people could assess whether their city’s camera density was higher in certain zip codes, or in neighborhoods populated by a dominant ethnic group.
Surveillance heat maps could be used to document which government agencies were refusing to upload their video files, or which neighborhoods were not under surveillance. Given what we already know today about the correlation between camera density, income, and social status, these “dark” camera-free regions would likely be those located near government agencies and in more affluent parts of a city.
Extreme transparency would democratize surveillance. Every city’s data trust would keep a publicly-accessible log of who’s searching for what, and whom. People could use their local data trust’s search history to check whether anyone was searching for their name, face, or license plate. As a result, clandestine spying on—and stalking of—particular individuals would become difficult to hide and simpler to prove.
Protect the Vulnerable and Exonerate the Falsely Accused
Not all surveillance video automatically works against the underdog. As the bungled (and consequently no longer secret) assassination of journalist Jamal Khashoggi demonstrated, one of the unexpected upsides of surveillance cameras has been the fact that even kings become accountable for their crimes. If opened up to the public, surveillance cameras could serve as witnesses to justice.
Video evidence has the power to protect vulnerable individuals and social groups by shedding light onto messy, unreliable (and frequently conflicting) human narratives of who did what to whom, and why. With access to a data trust, a person falsely accused of a crime could prove their innocence. By searching for their own face in video footage or downloading time/date stamped footage from a particular camera, a potential suspect could document their physical absence from the scene of a crime—no lengthy police investigation or high-priced attorney needed.
Given Enough Eyeballs, All Crimes Are Shallow
Placing public surveillance video into a public trust could make cities safer and would streamline routine police work. Linus Torvalds, the developer of open-source operating system Linux, famously observed that “given enough eyeballs, all bugs are shallow.” In the case of public cameras and a common data repository, Torvald’s Law could be restated as “given enough eyeballs, all crimes are shallow.”
If thousands of citizen eyeballs were given access to a city’s public surveillance videos, local police forces could crowdsource the work of solving crimes and searching for missing persons. Unfortunately, at the present time, cities are unable to wring any social benefit from video footage of public spaces. The most formidable barrier is not government-imposed secrecy, but the fact that as cameras and computers have grown cheaper, a large and fast-growing “mom and pop” surveillance state has taken over most of the filming of public spaces.
While we fear spooky government surveillance, the reality is that we’re much more likely to be filmed by security cameras owned by shopkeepers, landlords, medical offices, hotels, homeowners, and schools. These businesses, organizations, and individuals install cameras in public areas for practical reasons—to reduce their insurance costs, to prevent lawsuits, or to combat shoplifting. In the absence of regulations governing their use, private camera owners store video footage in a wide variety of locations, for varying retention periods.
The unfortunate (and unintended) result of this informal and decentralized network of public surveillance is that video files are not easy to access, even for police officers on official business. After a crime or terrorist attack occurs, local police (or attorneys armed with a subpoena) go from door to door to manually collect video evidence. Once they have the videos in hand, their next challenge is searching for the right “codex” to crack the dozens of different file formats they encounter so they can watch and analyze the footage.
The result of these practical barriers is that as it stands today, only people with considerable legal or political clout are able to successfully gain access into a city’s privately-owned, ad-hoc collections of public surveillance videos. Not only are cities missing the opportunity to streamline routine evidence-gathering police work, they’re missing a radically transformative benefit that would become possible once video footage from thousands of different security cameras were pooled into a single repository: the ability to apply the power of citizen eyeballs to the work of improving public safety.
Why We Need Extreme Transparency
When regular people can’t access their own surveillance videos, there can be no data justice. While we wait for the law to catch up with the reality of modern urban life, citizens and city governments should use technology to address the problem that lies at the heart of surveillance: a power imbalance between those who control the cameras and those who don’t.
Cities should permit individuals and organizations to install and deploy as many public-facing cameras as they wish, but with the mandate that camera owners must place all resulting video footage into the mercilessly bright sunshine of an open data trust. This way, cloud computing, open APIs, and artificial intelligence software can help combat abuses of surveillance and give citizens insight into who’s filming us, where, and why.
Image Credit: VladFotoMag / Shutterstock.com Continue reading →
Facial recognition technology has progressed to point where it now interprets emotions in facial expressions. This type of analysis is increasingly used in daily life. For example, companies can use facial recognition software to help with hiring decisions. Other programs scan the faces in crowds to identify threats to public safety.
Unfortunately, this technology struggles to interpret the emotions of black faces. My new study, published last month, shows that emotional analysis technology assigns more negative emotions to black men’s faces than white men’s faces.
This isn’t the first time that facial recognition programs have been shown to be biased. Google labeled black faces as gorillas. Cameras identified Asian faces as blinking. Facial recognition programs struggled to correctly identify gender for people with darker skin.
My work contributes to a growing call to better understand the hidden bias in artificial intelligence software.
To examine the bias in the facial recognition systems that analyze people’s emotions, I used a data set of 400 NBA player photos from the 2016 to 2017 season, because players are similar in their clothing, athleticism, age and gender. Also, since these are professional portraits, the players look at the camera in the picture.
I ran the images through two well-known types of emotional recognition software. Both assigned black players more negative emotional scores on average, no matter how much they smiled.
For example, consider the official NBA pictures of Darren Collison and Gordon Hayward. Both players are smiling, and, according to the facial recognition and analysis program Face++, Darren Collison and Gordon Hayward have similar smile scores—48.7 and 48.1 out of 100, respectively.
Basketball players Darren Collision (left) and Gordon Hayward (right). basketball-reference.com
However, Face++ rates Hayward’s expression as 59.7 percent happy and 0.13 percent angry and Collison’s expression as 39.2 percent happy and 27 percent angry. Collison is viewed as nearly as angry as he is happy and far angrier than Hayward—despite the facial recognition program itself recognizing that both players are smiling.
In contrast, Microsoft’s Face API viewed both men as happy. Still, Collison is viewed as less happy than Hayward, with 98 and 93 percent happiness scores, respectively. Despite his smile, Collison is even scored with a small amount of contempt, whereas Hayward has none.
Across all the NBA pictures, the same pattern emerges. On average, Face++ rates black faces as twice as angry as white faces. Face API scores black faces as three times more contemptuous than white faces. After matching players based on their smiles, both facial analysis programs are still more likely to assign the negative emotions of anger or contempt to black faces.
Stereotyped by AI
My study shows that facial recognition programs exhibit two distinct types of bias.
First, black faces were consistently scored as angrier than white faces for every smile. Face++ showed this type of bias. Second, black faces were always scored as angrier if there was any ambiguity about their facial expression. Face API displayed this type of disparity. Even if black faces are partially smiling, my analysis showed that the systems assumed more negative emotions as compared to their white counterparts with similar expressions. The average emotional scores were much closer across races, but there were still noticeable differences for black and white faces.
This observation aligns with other research, which suggests that black professionals must amplify positive emotions to receive parity in their workplace performance evaluations. Studies show that people perceive black men as more physically threatening than white men, even when they are the same size.
Some researchers argue that facial recognition technology is more objective than humans. But my study suggests that facial recognition reflects the same biases that people have. Black men’s facial expressions are scored with emotions associated with threatening behaviors more often than white men, even when they are smiling. There is good reason to believe that the use of facial recognition could formalize preexisting stereotypes into algorithms, automatically embedding them into everyday life.
Until facial recognition assesses black and white faces similarly, black people may need to exaggerate their positive facial expressions—essentially smile more—to reduce ambiguity and potentially negative interpretations by the technology.
Although innovative, artificial intelligence can perpetrate and exacerbate existing power dynamics, leading to disparate impact across racial/ethnic groups. Some societal accountability is necessary to ensure fairness to all groups because facial recognition, like most artificial intelligence, is often invisible to the people most affected by its decisions.
Lauren Rhue, Assistant Professor of Information Systems and Analytics, Wake Forest University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Image Credit: Alex_Po / Shutterstock.com Continue reading →
Imagine trying to read War and Peace one letter at a time. The thought alone feels excruciating. But in many ways, this painful idea holds parallels to how human-machine interfaces (HMI) force us to interact with and process data today.
Designed back in the 1970s at Xerox PARC and later refined during the 1980s by Apple, today’s HMI was originally conceived during fundamentally different times, and specifically, before people and machines were generating so much data. Fast forward to 2019, when humans are estimated to produce 44 zettabytes of data—equal to two stacks of books from here to Pluto—and we are still using the same HMI from the 1970s.
These dated interfaces are not equipped to handle today’s exponential rise in data, which has been ushered in by the rapid dematerialization of many physical products into computers and software.
Breakthroughs in perceptual and cognitive computing, especially machine learning algorithms, are enabling technology to process vast volumes of data, and in doing so, they are dramatically amplifying our brain’s abilities. Yet even with these powerful technologies that at times make us feel superhuman, the interfaces are still crippled with poor ergonomics.
Many interfaces are still designed around the concept that human interaction with technology is secondary, not instantaneous. This means that any time someone uses technology, they are inevitably multitasking, because they must simultaneously perform a task and operate the technology.
If our aim, however, is to create technology that truly extends and amplifies our mental abilities so that we can offload important tasks, the technology that helps us must not also overwhelm us in the process. We must reimagine interfaces to work in coherence with how our minds function in the world so that our brains and these tools can work together seamlessly.
Most technology is designed to serve either the mind or the body. It is a problematic divide, because our brains use our entire body to process the world around us. Said differently, our minds and bodies do not operate distinctly. Our minds are embodied.
Studies using MRI scans have shown that when a person feels an emotion in their gut, blood actually moves to that area of the body. The body and the mind are linked in this way, sharing information back and forth continuously.
Current technology presents data to the brain differently from how the brain processes data. Our brains, for example, use sensory data to continually encode and decipher patterns within the neocortex. Our brains do not create a linguistic label for each item, which is how the majority of machine learning systems operate, nor do our brains have an image associated with each of these labels.
Our bodies move information through us instantaneously, in a sense “computing” at the speed of thought. What if our technology could do the same?
Using Cognitive Ergonomics to Design Better Interfaces
Well-designed physical tools, as philosopher Martin Heidegger once meditated on while using the metaphor of a hammer, seem to disappear into the “hand.” They are designed to amplify a human ability and not get in the way during the process.
The aim of physical ergonomics is to understand the mechanical movement of the human body and then adapt a physical system to amplify the human output in accordance. By understanding the movement of the body, physical ergonomics enables ergonomically sound physical affordances—or conditions—so that the mechanical movement of the body and the mechanical movement of the machine can work together harmoniously.
Cognitive ergonomics applied to HMI design uses this same idea of amplifying output, but rather than focusing on physical output, the focus is on mental output. By understanding the raw materials the brain uses to comprehend information and form an output, cognitive ergonomics allows technologists and designers to create technological affordances so that the brain can work seamlessly with interfaces and remove the interruption costs of our current devices. In doing so, the technology itself “disappears,” and a person’s interaction with technology becomes fluid and primary.
By leveraging cognitive ergonomics in HMI design, we can create a generation of interfaces that can process and present data the same way humans process real-world information, meaning through fully-sensory interfaces.
Several brain-machine interfaces are already on the path to achieving this. AlterEgo, a wearable device developed by MIT researchers, uses electrodes to detect and understand nonverbal prompts, which enables the device to read the user’s mind and act as an extension of the user’s cognition.
Another notable example is the BrainGate neural device, created by researchers at Stanford University. Just two months ago, a study was released showing that this brain implant system allowed paralyzed patients to navigate an Android tablet with their thoughts alone.
These are two extraordinary examples of what is possible for the future of HMI, but there is still a long way to go to bring cognitive ergonomics front and center in interface design.
Disruptive Innovation Happens When You Step Outside Your Existing Users
Most of today’s interfaces are designed by a narrow population, made up predominantly of white, non-disabled men who are prolific in the use of technology (you may recall The New York Times viral article from 2016, Artificial Intelligence’s White Guy Problem). If you ask this population if there is a problem with today’s HMIs, most will say no, and this is because the technology has been designed to serve them.
This lack of diversity means a limited perspective is being brought to interface design, which is problematic if we want HMI to evolve and work seamlessly with the brain. To use cognitive ergonomics in interface design, we must first gain a more holistic understanding of how people with different abilities understand the world and how they interact with technology.
Underserved groups, such as people with physical disabilities, operate on what Clayton Christensen coined in The Innovator’s Dilemma as the fringe segment of a market. Developing solutions that cater to fringe groups can in fact disrupt the larger market by opening a downward, much larger market.
Learning From Underserved Populations
When technology fails to serve a group of people, that group must adapt the technology to meet their needs.
The workarounds created are often ingenious, specifically because they have not been arrived at by preferences, but out of necessity that has forced disadvantaged users to approach the technology from a very different vantage point.
When a designer or technologist begins learning from this new viewpoint and understanding challenges through a different lens, they can bring new perspectives to design—perspectives that otherwise can go unseen.
Designers and technologists can also learn from people with physical disabilities who interact with the world by leveraging other senses that help them compensate for one they may lack. For example, some blind people use echolocation to detect objects in their environments.
The BrainPort device developed by Wicab is an incredible example of technology leveraging one human sense to serve or compliment another. The BrainPort device captures environmental information with a wearable video camera and converts this data into soft electrical stimulation sequences that are sent to a device on the user’s tongue—the most sensitive touch receptor in the body. The user learns how to interpret the patterns felt on their tongue, and in doing so, become able to “see” with their tongue.
Key to the future of HMI design is learning how different user groups navigate the world through senses beyond sight. To make cognitive ergonomics work, we must understand how to leverage the senses so we’re not always solely relying on our visual or verbal interactions.
Radical Inclusion for the Future of HMI
Bringing radical inclusion into HMI design is about gaining a broader lens on technology design at large, so that technology can serve everyone better.
Interestingly, cognitive ergonomics and radical inclusion go hand in hand. We can’t design our interfaces with cognitive ergonomics without bringing radical inclusion into the picture, and we also will not arrive at radical inclusion in technology so long as cognitive ergonomics are not considered.
This new mindset is the only way to usher in an era of technology design that amplifies the collective human ability to create a more inclusive future for all.
Image Credit: jamesteohart / Shutterstock.com Continue reading →
It’s hard to avoid the prominence of AI in our lives, and there is a plethora of predictions about how it will influence our future. In their new book Solomon’s Code: Humanity in a World of Thinking Machines, co-authors Olaf Groth, Professor of Strategy, Innovation and Economics at HULT International Business School and CEO of advisory network Cambrian.ai, and Mark Nitzberg, Executive Director of UC Berkeley’s Center for Human-Compatible AI, believe that the shift in balance of power between intelligent machines and humans is already here.
I caught up with the authors about how the continued integration between technology and humans, and their call for a “Digital Magna Carta,” a broadly-accepted charter developed by a multi-stakeholder congress that would help guide the development of advanced technologies to harness their power for the benefit of all humanity.
Lisa Kay Solomon: Your new book, Solomon’s Code, explores artificial intelligence and its broader human, ethical, and societal implications that all leaders need to consider. AI is a technology that’s been in development for decades. Why is it so urgent to focus on these topics now?
Olaf Groth and Mark Nitzberg: Popular perception always thinks of AI in terms of game-changing narratives—for instance, Deep Blue beating Gary Kasparov at chess. But it’s the way these AI applications are “getting into our heads” and making decisions for us that really influences our lives. That’s not to say the big, headline-grabbing breakthroughs aren’t important; they are.
But it’s the proliferation of prosaic apps and bots that changes our lives the most, by either empowering or counteracting who we are and what we do. Today, we turn a rapidly growing number of our decisions over to these machines, often without knowing it—and even more often without understanding the second- and third-order effects of both the technologies and our decisions to rely on them.
There is genuine power in what we call a “symbio-intelligent” partnership between human, machine, and natural intelligences. These relationships can optimize not just economic interests, but help improve human well-being, create a more purposeful workplace, and bring more fulfillment to our lives.
However, mitigating the risks while taking advantage of the opportunities will require a serious, multidisciplinary consideration of how AI influences human values, trust, and power relationships. Whether or not we acknowledge their existence in our everyday life, these questions are no longer just thought exercises or fodder for science fiction.
In many ways, these technologies can challenge what it means to be human, and their ramifications already affect us in real and often subtle ways. We need to understand how
LKS: There is a lot of hype and misconceptions about AI. In your book, you provide a useful distinction between the cognitive capability that we often associate with AI processes, and the more human elements of consciousness and conscience. Why are these distinctions so important to understand?
OG & MN: Could machines take over consciousness some day as they become more powerful and complex? It’s hard to say. But there’s little doubt that, as machines become more capable, humans will start to think of them as something conscious—if for no other reason than our natural inclination to anthropomorphize.
Machines are already learning to recognize our emotional states and our physical health. Once they start talking that back to us and adjusting their behavior accordingly, we will be tempted to develop a certain rapport with them, potentially more trusting or more intimate because the machine recognizes us in our various states.
Consciousness is hard to define and may well be an emergent property, rather than something you can easily create or—in turn—deduce to its parts. So, could it happen as we put more and more elements together, from the realms of AI, quantum computing, or brain-computer interfaces? We can’t exclude that possibility.
Either way, we need to make sure we’re charting out a clear path and guardrails for this development through the Three Cs in machines: cognition (where AI is today); consciousness (where AI could go); and conscience (what we need to instill in AI before we get there). The real concern is that we reach machine consciousness—or what humans decide to grant as consciousness—without a conscience. If that happens, we will have created an artificial sociopath.
LKS: We have been seeing major developments in how AI is influencing product development and industry shifts. How is the rise of AI changing power at the global level?
OG & MN: Both in the public and private sectors, the data holder has the power. We’ve already seen the ascendance of about 10 “digital barons” in the US and China who sit on huge troves of data, massive computing power, and the resources and money to attract the world’s top AI talent. With these gaps already open between the haves and the have-nots on the technological and corporate side, we’re becoming increasingly aware that similar inequalities are forming at a societal level as well.
Economic power flows with data, leaving few options for socio-economically underprivileged populations and their corrupt, biased, or sparse digital footprints. By concentrating power and overlooking values, we fracture trust.
We can already see this tension emerging between the two dominant geopolitical models of AI. China and the US have emerged as the most powerful in both technological and economic terms, and both remain eager to drive that influence around the world. The EU countries are more contained on these economic and geopolitical measures, but they’ve leaped ahead on privacy and social concerns.
The problem is, no one has yet combined leadership on all three critical elements of values, trust, and power. The nations and organizations that foster all three of these elements in their AI systems and strategies will lead the future. Some are starting to recognize the need for the combination, but we found just 13 countries that have created significant AI strategies. Countries that wait too long to join them risk subjecting themselves to a new “data colonialism” that could change their economies and societies from the outside.
LKS: Solomon’s Code looks at AI from a variety of perspectives, considering both positive and potentially dangerous effects. You caution against the rising global threat and weaponization of AI and data, suggesting that “biased or dirty data is more threatening than nuclear arms or a pandemic.” For global leaders, entrepreneurs, technologists, policy makers and social change agents reading this, what specific strategies do you recommend to ensure ethical development and application of AI?
OG & MN: We’ve surrendered many of our most critical decisions to the Cult of Data. In most cases, that’s a great thing, as we rely more on scientific evidence to understand our world and our way through it. But we swing too far in other instances, assuming that datasets and algorithms produce a complete story that’s unsullied by human biases or intellectual shortcomings. We might choose to ignore it, but no one is blind to the dangers of nuclear war or pandemic disease. Yet, we willfully blind ourselves to the threat of dirty data, instead believing it to be pristine.
So, what do we do about it? On an individual level, it’s a matter of awareness, knowing who controls your data and how outsourcing of decisions to thinking machines can present opportunities and threats alike.
For business, government, and political leaders, we need to see a much broader expansion of ethics committees with transparent criteria with which to evaluate new products and services. We might consider something akin to clinical trials for pharmaceuticals—a sort of testing scheme that can transparently and independently measure the effects on humans of algorithms, bots, and the like. All of this needs to be multidisciplinary, bringing in expertise from across technology, social systems, ethics, anthropology, psychology, and so on.
Finally, on a global level, we need a new charter of rights—a Digital Magna Carta—that formalizes these protections and guides the development of new AI technologies toward all of humanity’s benefit. We’ve suggested the creation of a multi-stakeholder Cambrian Congress (harkening back to the explosion of life during the Cambrian period) that can not only begin to frame benefits for humanity, but build the global consensus around principles for a basic code-of-conduct, and ideas for evaluation and enforcement mechanisms, so we can get there without any large-scale failures or backlash in society. So, it’s not one or the other—it’s both.
Image Credit: whiteMocca / Shutterstock.com Continue reading →
In the past few years, artificial intelligence has advanced so quickly that it now seems hardly a month goes by without a newsworthy AI breakthrough. In areas as wide-ranging as speech translation, medical diagnosis, and gameplay, we have seen computers outperform humans in startling ways.
This has sparked a discussion about how AI will impact employment. Some fear that as AI improves, it will supplant workers, creating an ever-growing pool of unemployable humans who cannot compete economically with machines.
This concern, while understandable, is unfounded. In fact, AI will be the greatest job engine the world has ever seen.
New Technology Isn’t a New Phenomenon
On the one hand, those who predict massive job loss from AI can be excused. It is easier to see existing jobs disrupted by new technology than to envision what new jobs the technology will enable.
But on the other hand, radical technological advances aren’t a new phenomenon. Technology has progressed nonstop for 250 years, and in the US unemployment has stayed between 5 to 10 percent for almost all that time, even when radical new technologies like steam power and electricity came on the scene.
But you don’t have to look back to steam, or even electricity. Just look at the internet. Go back 25 years, well within the memory of today’s pessimistic prognosticators, to 1993. The web browser Mosaic had just been released, and the phrase “surfing the web,” that most mixed of metaphors, was just a few months old.
If someone had asked you what would be the result of connecting a couple billion computers into a giant network with common protocols, you might have predicted that email would cause us to mail fewer letters, and the web might cause us to read fewer newspapers and perhaps even do our shopping online. If you were particularly farsighted, you might have speculated that travel agents and stockbrokers would be adversely affected by this technology. And based on those surmises, you might have thought the internet would destroy jobs.
But now we know what really happened. The obvious changes did occur. But a slew of unexpected changes happened as well. We got thousands of new companies worth trillions of dollars. We bettered the lot of virtually everyone on the planet touched by the technology. Dozens of new careers emerged, from web designer to data scientist to online marketer. The cost of starting a business with worldwide reach plummeted, and the cost of communicating with customers and leads went to nearly zero. Vast storehouses of information were made freely available and used by entrepreneurs around the globe to build new kinds of businesses.
But yes, we mail fewer letters and buy fewer newspapers.
The Rise of Artificial Intelligence
Then along came a new, even bigger technology: artificial intelligence. You hear the same refrain: “It will destroy jobs.”
Consider the ATM. If you had to point to a technology that looked as though it would replace people, the ATM might look like a good bet; it is, after all, an automated teller machine. And yet, there are more tellers now than when ATMs were widely released. How can this be? Simple: ATMs lowered the cost of opening bank branches, and banks responded by opening more, which required hiring more tellers.
In this manner, AI will create millions of jobs that are far beyond our ability to imagine. For instance, AI is becoming adept at language translation—and according to the US Bureau of Labor Statistics, demand for human translators is skyrocketing. Why? If the cost of basic translation drops to nearly zero, the cost of doing business with those who speak other languages falls. Thus, it emboldens companies to do more business overseas, creating more work for human translators. AI may do the simple translations, but humans are needed for the nuanced kind.
In fact, the BLS forecasts faster-than-average job growth in many occupations that AI is expected to impact: accountants, forensic scientists, geological technicians, technical writers, MRI operators, dietitians, financial specialists, web developers, loan officers, medical secretaries, and customer service representatives, to name a very few. These fields will not experience job growth in spite of AI, but through it.
But just as with the internet, the real gains in jobs will come from places where our imaginations cannot yet take us.
You may recall waking up one morning to the news that “47 percent of jobs will be lost to technology.”
That report by Carl Frey and Michael Osborne is a fine piece of work, but readers and the media distorted their 47 percent number. What the authors actually said is that some functions within 47 percent of jobs will be automated, not that 47 percent of jobs will disappear.
Frey and Osborne go on to rank occupations by “probability of computerization” and give the following jobs a 65 percent or higher probability: social science research assistants, atmospheric and space scientists, and pharmacy aides. So what does this mean? Social science professors will no longer have research assistants? Of course they will. They will just do different things because much of what they do today will be automated.
The intergovernmental Organization for Economic Co-operation and Development released a report of their own in 2016. This report, titled “The Risk of Automation for Jobs in OECD Countries,” applies a different “whole occupations” methodology and puts the share of jobs potentially lost to computerization at nine percent. That is normal churn for the economy.
But what of the skills gap? Will AI eliminate low-skilled workers and create high-skilled job opportunities? The relevant question is whether most people can do a job that’s just a little more complicated than the one they currently have. This is exactly what happened with the industrial revolution; farmers became factory workers, factory workers became factory managers, and so on.
Embracing AI in the Workplace
A January 2018 Accenture report titled “Reworking the Revolution” estimates that new applications of AI combined with human collaboration could boost employment worldwide as much as 10 percent by 2020.
Electricity changed the world, as did mechanical power, as did the assembly line. No one can reasonably claim that we would be better off without those technologies. Each of them bettered our lives, created jobs, and raised wages. AI will be bigger than electricity, bigger than mechanization, bigger than anything that has come before it.
This is how free economies work, and why we have never run out of jobs due to automation. There are not a fixed number of jobs that automation steals one by one, resulting in progressively more unemployment. There are as many jobs in the world as there are buyers and sellers of labor.
Image Credit: enzozo / Shutterstock.com Continue reading →