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#431859 Digitized to Democratized: These Are the ...
“The Six Ds are a chain reaction of technological progression, a road map of rapid development that always leads to enormous upheaval and opportunity.”
–Peter Diamandis and Steven Kotler, Bold
We live in incredible times. News travels the globe in an instant. Music, movies, games, communication, and knowledge are ever-available on always-connected devices. From biotechnology to artificial intelligence, powerful technologies that were once only available to huge organizations and governments are becoming more accessible and affordable thanks to digitization.
The potential for entrepreneurs to disrupt industries and corporate behemoths to unexpectedly go extinct has never been greater.
One hundred or fifty or even twenty years ago, disruption meant coming up with a product or service people needed but didn’t have yet, then finding a way to produce it with higher quality and lower costs than your competitors. This entailed hiring hundreds or thousands of employees, having a large physical space to put them in, and waiting years or even decades for hard work to pay off and products to come to fruition.
“Technology is disrupting traditional industrial processes, and they’re never going back.”
But thanks to digital technologies developing at exponential rates of change, the landscape of 21st-century business has taken on a dramatically different look and feel.
The structure of organizations is changing. Instead of thousands of employees and large physical plants, modern start-ups are small organizations focused on information technologies. They dematerialize what was once physical and create new products and revenue streams in months, sometimes weeks.
It no longer takes a huge corporation to have a huge impact.
Technology is disrupting traditional industrial processes, and they’re never going back. This disruption is filled with opportunity for forward-thinking entrepreneurs.
The secret to positively impacting the lives of millions of people is understanding and internalizing the growth cycle of digital technologies. This growth cycle takes place in six key steps, which Peter Diamandis calls the Six Ds of Exponentials: digitization, deception, disruption, demonetization, dematerialization, and democratization.
According to Diamandis, cofounder and chairman of Singularity University and founder and executive chairman of XPRIZE, when something is digitized it begins to behave like an information technology.
Newly digitized products develop at an exponential pace instead of a linear one, fooling onlookers at first before going on to disrupt companies and whole industries. Before you know it, something that was once expensive and physical is an app that costs a buck.
Newspapers and CDs are two obvious recent examples. The entertainment and media industries are still dealing with the aftermath of digitization as they attempt to transform and update old practices tailored to a bygone era. But it won’t end with digital media. As more of the economy is digitized—from medicine to manufacturing—industries will hop on an exponential curve and be similarly disrupted.
Diamandis’s 6 Ds are critical to understanding and planning for this disruption.
The 6 Ds of Exponential Organizations are Digitized, Deceptive, Disruptive, Demonetized, Dematerialized, and Democratized.
Diamandis uses the contrasting fates of Kodak and Instagram to illustrate the power of the six Ds and exponential thinking.
Kodak invented the digital camera in 1975, but didn’t invest heavily in the new technology, instead sticking with what had always worked: traditional cameras and film. In 1996, Kodak had a $28 billion market capitalization with 95,000 employees.
But the company didn’t pay enough attention to how digitization of their core business was changing it; people were no longer taking pictures in the same way and for the same reasons as before.
After a downward spiral, Kodak went bankrupt in 2012. That same year, Facebook acquired Instagram, a digital photo sharing app, which at the time was a startup with 13 employees. The acquisition’s price tag? $1 billion. And Instagram had been founded only 18 months earlier.
The most ironic piece of this story is that Kodak invented the digital camera; they took the first step toward overhauling the photography industry and ushering it into the modern age, but they were unwilling to disrupt their existing business by taking a risk in what was then uncharted territory. So others did it instead.
The same can happen with any technology that’s just getting off the ground. It’s easy to stop pursuing it in the early part of the exponential curve, when development appears to be moving slowly. But failing to follow through only gives someone else the chance to do it instead.
The Six Ds are a road map showing what can happen when an exponential technology is born. Not every phase is easy, but the results give even small teams the power to change the world in a faster and more impactful way than traditional business ever could.
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#431385 Here’s How to Get to Conscious ...
“We cannot be conscious of what we are not conscious of.” – Julian Jaynes, The Origin of Consciousness in the Breakdown of the Bicameral Mind
Unlike the director leads you to believe, the protagonist of Ex Machina, Andrew Garland’s 2015 masterpiece, isn’t Caleb, a young programmer tasked with evaluating machine consciousness. Rather, it’s his target Ava, a breathtaking humanoid AI with a seemingly child-like naïveté and an enigmatic mind.
Like most cerebral movies, Ex Machina leaves the conclusion up to the viewer: was Ava actually conscious? In doing so, it also cleverly avoids a thorny question that has challenged most AI-centric movies to date: what is consciousness, and can machines have it?
Hollywood producers aren’t the only people stumped. As machine intelligence barrels forward at breakneck speed—not only exceeding human performance on games such as DOTA and Go, but doing so without the need for human expertise—the question has once more entered the scientific mainstream.
Are machines on the verge of consciousness?
This week, in a review published in the prestigious journal Science, cognitive scientists Drs. Stanislas Dehaene, Hakwan Lau and Sid Kouider of the Collège de France, University of California, Los Angeles and PSL Research University, respectively, argue: not yet, but there is a clear path forward.
The reason? Consciousness is “resolutely computational,” the authors say, in that it results from specific types of information processing, made possible by the hardware of the brain.
There is no magic juice, no extra spark—in fact, an experiential component (“what is it like to be conscious?”) isn’t even necessary to implement consciousness.
If consciousness results purely from the computations within our three-pound organ, then endowing machines with a similar quality is just a matter of translating biology to code.
Much like the way current powerful machine learning techniques heavily borrow from neurobiology, the authors write, we may be able to achieve artificial consciousness by studying the structures in our own brains that generate consciousness and implementing those insights as computer algorithms.
From Brain to Bot
Without doubt, the field of AI has greatly benefited from insights into our own minds, both in form and function.
For example, deep neural networks, the architecture of algorithms that underlie AlphaGo’s breathtaking sweep against its human competitors, are loosely based on the multi-layered biological neural networks that our brain cells self-organize into.
Reinforcement learning, a type of “training” that teaches AIs to learn from millions of examples, has roots in a centuries-old technique familiar to anyone with a dog: if it moves toward the right response (or result), give a reward; otherwise ask it to try again.
In this sense, translating the architecture of human consciousness to machines seems like a no-brainer towards artificial consciousness. There’s just one big problem.
“Nobody in AI is working on building conscious machines because we just have nothing to go on. We just don’t have a clue about what to do,” said Dr. Stuart Russell, the author of Artificial Intelligence: A Modern Approach in a 2015 interview with Science.
Multilayered consciousness
The hard part, long before we can consider coding machine consciousness, is figuring out what consciousness actually is.
To Dehaene and colleagues, consciousness is a multilayered construct with two “dimensions:” C1, the information readily in mind, and C2, the ability to obtain and monitor information about oneself. Both are essential to consciousness, but one can exist without the other.
Say you’re driving a car and the low fuel light comes on. Here, the perception of the fuel-tank light is C1—a mental representation that we can play with: we notice it, act upon it (refill the gas tank) and recall and speak about it at a later date (“I ran out of gas in the boonies!”).
“The first meaning we want to separate (from consciousness) is the notion of global availability,” explains Dehaene in an interview with Science. When you’re conscious of a word, your whole brain is aware of it, in a sense that you can use the information across modalities, he adds.
But C1 is not just a “mental sketchpad.” It represents an entire architecture that allows the brain to draw multiple modalities of information from our senses or from memories of related events, for example.
Unlike subconscious processing, which often relies on specific “modules” competent at a defined set of tasks, C1 is a global workspace that allows the brain to integrate information, decide on an action, and follow through until the end.
Like The Hunger Games, what we call “conscious” is whatever representation, at one point in time, wins the competition to access this mental workspace. The winners are shared among different brain computation circuits and are kept in the spotlight for the duration of decision-making to guide behavior.
Because of these features, C1 consciousness is highly stable and global—all related brain circuits are triggered, the authors explain.
For a complex machine such as an intelligent car, C1 is a first step towards addressing an impending problem, such as a low fuel light. In this example, the light itself is a type of subconscious signal: when it flashes, all of the other processes in the machine remain uninformed, and the car—even if equipped with state-of-the-art visual processing networks—passes by gas stations without hesitation.
With C1 in place, the fuel tank would alert the car computer (allowing the light to enter the car’s “conscious mind”), which in turn checks the built-in GPS to search for the next gas station.
“We think in a machine this would translate into a system that takes information out of whatever processing module it’s encapsulated in, and make it available to any of the other processing modules so they can use the information,” says Dehaene. “It’s a first sense of consciousness.”
Meta-cognition
In a way, C1 reflects the mind’s capacity to access outside information. C2 goes introspective.
The authors define the second facet of consciousness, C2, as “meta-cognition:” reflecting on whether you know or perceive something, or whether you just made an error (“I think I may have filled my tank at the last gas station, but I forgot to keep a receipt to make sure”). This dimension reflects the link between consciousness and sense of self.
C2 is the level of consciousness that allows you to feel more or less confident about a decision when making a choice. In computational terms, it’s an algorithm that spews out the probability that a decision (or computation) is correct, even if it’s often experienced as a “gut feeling.”
C2 also has its claws in memory and curiosity. These self-monitoring algorithms allow us to know what we know or don’t know—so-called “meta-memory,” responsible for that feeling of having something at the tip of your tongue. Monitoring what we know (or don’t know) is particularly important for children, says Dehaene.
“Young children absolutely need to monitor what they know in order to…inquire and become curious and learn more,” he explains.
The two aspects of consciousness synergize to our benefit: C1 pulls relevant information into our mental workspace (while discarding other “probable” ideas or solutions), while C2 helps with long-term reflection on whether the conscious thought led to a helpful response.
Going back to the low fuel light example, C1 allows the car to solve the problem in the moment—these algorithms globalize the information, so that the car becomes aware of the problem.
But to solve the problem, the car would need a “catalog of its cognitive abilities”—a self-awareness of what resources it has readily available, for example, a GPS map of gas stations.
“A car with this sort of self-knowledge is what we call having C2,” says Dehaene. Because the signal is globally available and because it’s being monitored in a way that the machine is looking at itself, the car would care about the low gas light and behave like humans do—lower fuel consumption and find a gas station.
“Most present-day machine learning systems are devoid of any self-monitoring,” the authors note.
But their theory seems to be on the right track. The few examples whereby a self-monitoring system was implemented—either within the structure of the algorithm or as a separate network—the AI has generated “internal models that are meta-cognitive in nature, making it possible for an agent to develop a (limited, implicit, practical) understanding of itself.”
Towards conscious machines
Would a machine endowed with C1 and C2 behave as if it were conscious? Very likely: a smartcar would “know” that it’s seeing something, express confidence in it, report it to others, and find the best solutions for problems. If its self-monitoring mechanisms break down, it may also suffer “hallucinations” or even experience visual illusions similar to humans.
Thanks to C1 it would be able to use the information it has and use it flexibly, and because of C2 it would know the limit of what it knows, says Dehaene. “I think (the machine) would be conscious,” and not just merely appearing so to humans.
If you’re left with a feeling that consciousness is far more than global information sharing and self-monitoring, you’re not alone.
“Such a purely functional definition of consciousness may leave some readers unsatisfied,” the authors acknowledge.
“But we’re trying to take a radical stance, maybe simplifying the problem. Consciousness is a functional property, and when we keep adding functions to machines, at some point these properties will characterize what we mean by consciousness,” Dehaene concludes.
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#431343 How Technology Is Driving Us Toward Peak ...
At some point in the future—and in some ways we are already seeing this—the amount of physical stuff moving around the world will peak and begin to decline. By “stuff,” I am referring to liquid fuels, coal, containers on ships, food, raw materials, products, etc.
New technologies are moving us toward “production-at-the-point-of-consumption” of energy, food, and products with reduced reliance on a global supply chain.
The trade of physical stuff has been central to globalization as we’ve known it. So, this declining movement of stuff may signal we are approaching “peak globalization.”
To be clear, even as the movement of stuff may slow, if not decline, the movement of people, information, data, and ideas around the world is growing exponentially and is likely to continue doing so for the foreseeable future.
Peak globalization may provide a pathway to preserving the best of globalization and global interconnectedness, enhancing economic and environmental sustainability, and empowering individuals and communities to strengthen democracy.
At the same time, some of the most troublesome aspects of globalization may be eased, including massive financial transfers to energy producers and loss of jobs to manufacturing platforms like China. This shift could bring relief to the “losers” of globalization and ease populist, nationalist political pressures that are roiling the developed countries.
That is quite a claim, I realize. But let me explain the vision.
New Technologies and Businesses: Digital, Democratized, Decentralized
The key factors moving us toward peak globalization and making it economically viable are new technologies and innovative businesses and business models allowing for “production-at-the-point-of-consumption” of energy, food, and products.
Exponential technologies are enabling these trends by sharply reducing the “cost of entry” for creating businesses. Driven by Moore’s Law, powerful technologies have become available to almost anyone, anywhere.
Beginning with the microchip, which has had a 100-billion-fold improvement in 40 years—10,000 times faster and 10 million times cheaper—the marginal cost of producing almost everything that can be digitized has fallen toward zero.
A hard copy of a book, for example, will always entail the cost of materials, printing, shipping, etc., even if the marginal cost falls as more copies are produced. But the marginal cost of a second digital copy, such as an e-book, streaming video, or song, is nearly zero as it is simply a digital file sent over the Internet, the world’s largest copy machine.* Books are one product, but there are literally hundreds of thousands of dollars in once-physical, separate products jammed into our devices at little to no cost.
A smartphone alone provides half the human population access to artificial intelligence—from SIRI, search, and translation to cloud computing—geolocation, free global video calls, digital photography and free uploads to social network sites, free access to global knowledge, a million apps for a huge variety of purposes, and many other capabilities that were unavailable to most people only a few years ago.
As powerful as dematerialization and demonetization are for private individuals, they’re having a stronger effect on businesses. A small team can access expensive, advanced tools that before were only available to the biggest organizations. Foundational digital platforms, such as the internet and GPS, and the platforms built on top of them by the likes of Google, Apple, Amazon, and others provide the connectivity and services democratizing business tools and driving the next generation of new startups.
“As these trends gain steam in coming decades, they’ll bleed into and fundamentally transform global supply chains.”
An AI startup, for example, doesn’t need its own server farm to train its software and provide service to customers. The team can rent computing power from Amazon Web Services. This platform model enables small teams to do big things on the cheap. And it isn’t just in software. Similar trends are happening in hardware too. Makers can 3D print or mill industrial grade prototypes of physical stuff in a garage or local maker space and send or sell designs to anyone with a laptop and 3D printer via online platforms.
These are early examples of trends that are likely to gain steam in coming decades, and as they do, they’ll bleed into and fundamentally transform global supply chains.
The old model is a series of large, connected bits of centralized infrastructure. It makes sense to mine, farm, or manufacture in bulk when the conditions, resources, machines, and expertise to do so exist in particular places and are specialized and expensive. The new model, however, enables smaller-scale production that is local and decentralized.
To see this more clearly, let’s take a look at the technological trends at work in the three biggest contributors to the global trade of physical stuff—products, energy, and food.
Products
3D printing (additive manufacturing) allows for distributed manufacturing near the point of consumption, eliminating or reducing supply chains and factory production lines.
This is possible because product designs are no longer made manifest in assembly line parts like molds or specialized mechanical tools. Rather, designs are digital and can be called up at will to guide printers. Every time a 3D printer prints, it can print a different item, so no assembly line needs to be set up for every different product. 3D printers can also print an entire finished product in one piece or reduce the number of parts of larger products, such as engines. This further lessens the need for assembly.
Because each item can be customized and printed on demand, there is no cost benefit from scaling production. No inventories. No shipping items across oceans. No carbon emissions transporting not only the final product but also all the parts in that product shipped from suppliers to manufacturer. Moreover, 3D printing builds items layer by layer with almost no waste, unlike “subtractive manufacturing” in which an item is carved out of a piece of metal, and much or even most of the material can be waste.
Finally, 3D printing is also highly scalable, from inexpensive 3D printers (several hundred dollars) for home and school use to increasingly capable and expensive printers for industrial production. There are also 3D printers being developed for printing buildings, including houses and office buildings, and other infrastructure.
The technology for finished products is only now getting underway, and there are still challenges to overcome, such as speed, quality, and range of materials. But as methods and materials advance, it will likely creep into more manufactured goods.
Ultimately, 3D printing will be a general purpose technology that involves many different types of printers and materials—such as plastics, metals, and even human cells—to produce a huge range of items, from human tissue and potentially human organs to household items and a range of industrial items for planes, trains, and automobiles.
Energy
Renewable energy production is located at or relatively near the source of consumption.
Although electricity generated by solar, wind, geothermal, and other renewable sources can of course be transmitted over longer distances, it is mostly generated and consumed locally or regionally. It is not transported around the world in tankers, ships, and pipelines like petroleum, coal, and natural gas.
Moreover, the fuel itself is free—forever. There is no global price on sun or wind. The people relying on solar and wind power need not worry about price volatility and potential disruption of fuel supplies as a result of political, market, or natural causes.
Renewables have their problems, of course, including intermittency and storage, and currently they work best if complementary to other sources, especially natural gas power plants that, unlike coal plants, can be turned on or off and modulated like a gas stove, and are half the carbon emissions of coal.
Within the next decades or so, it is likely the intermittency and storage problems will be solved or greatly mitigated. In addition, unlike coal and natural gas power plants, solar is scalable, from solar panels on individual homes or even cars and other devices, to large-scale solar farms. Solar can be connected with microgrids and even allow for autonomous electricity generation by homes, commercial buildings, and communities.
It may be several decades before fossil fuel power plants can be phased out, but the development cost of renewables has been falling exponentially and, in places, is beginning to compete with coal and gas. Solar especially is expected to continue to increase in efficiency and decline in cost.
Given these trends in cost and efficiency, renewables should become obviously cheaper over time—if the fuel is free for solar and has to be continually purchased for coal and gas, at some point the former is cheaper than the latter. Renewables are already cheaper if externalities such as carbon emissions and environmental degradation involved in obtaining and transporting the fuel are included.
Food
Food can be increasingly produced near the point of consumption with vertical farms and eventually with printed food and even printed or cultured meat.
These sources bring production of food very near the consumer, so transportation costs, which can be a significant portion of the cost of food to consumers, are greatly reduced. The use of land and water are reduced by 95% or more, and energy use is cut by nearly 50%. In addition, fertilizers and pesticides are not required and crops can be grown 365 days a year whatever the weather and in more climates and latitudes than is possible today.
While it may not be practical to grow grains, corn, and other such crops in vertical farms, many vegetables and fruits can flourish in such facilities. In addition, cultured or printed meat is being developed—the big challenge is scaling up and reducing cost—that is based on cells from real animals without slaughtering the animals themselves.
There are currently some 70 billion animals being raised for food around the world [PDF] and livestock alone counts for about 15% of global emissions. Moreover, livestock places huge demands on land, water, and energy. Like vertical farms, cultured or printed meat could be produced with no more land use than a brewery and with far less water and energy.
A More Democratic Economy Goes Bottom Up
This is a very brief introduction to the technologies that can bring “production-at-the-point-of-consumption” of products, energy, and food to cities and regions.
What does this future look like? Here’s a simplified example.
Imagine a universal manufacturing facility with hundreds of 3D printers printing tens of thousands of different products on demand for the local community—rather than assembly lines in China making tens of thousands of the same product that have to be shipped all over the world since no local market can absorb all of the same product.
Nearby, a vertical farm and cultured meat facility produce much of tomorrow night’s dinner. These facilities would be powered by local or regional wind and solar. Depending on need and quality, some infrastructure and machinery, like solar panels and 3D printers, would live in these facilities and some in homes and businesses.
The facilities could be owned by a large global corporation—but still locally produce goods—or they could be franchised or even owned and operated independently by the local population. Upkeep and management at each would provide jobs for communities nearby. Eventually, not only would global trade of parts and products diminish, but even required supplies of raw materials and feed stock would decline since there would be less waste in production, and many materials would be recycled once acquired.
“Peak globalization could be a viable pathway to an economic foundation that puts people first while building a more economically and environmentally sustainable future.”
This model suggests a shift toward a “bottom up” economy that is more democratic, locally controlled, and likely to generate more local jobs.
The global trends in democratization of technology make the vision technologically plausible. Much of this technology already exists and is improving and scaling while exponentially decreasing in cost to become available to almost anyone, anywhere.
This includes not only access to key technologies, but also to education through digital platforms available globally. Online courses are available for free, ranging from advanced physics, math, and engineering to skills training in 3D printing, solar installations, and building vertical farms. Social media platforms can enable local and global collaboration and sharing of knowledge and best practices.
These new communities of producers can be the foundation for new forms of democratic governance as they recognize and “capitalize” on the reality that control of the means of production can translate to political power. More jobs and local control could weaken populist, anti-globalization political forces as people recognize they could benefit from the positive aspects of globalization and international cooperation and connectedness while diminishing the impact of globalization’s downsides.
There are powerful vested interests that stand to lose in such a global structural shift. But this vision builds on trends that are already underway and are gaining momentum. Peak globalization could be a viable pathway to an economic foundation that puts people first while building a more economically and environmentally sustainable future.
This article was originally posted on Open Democracy (CC BY-NC 4.0). The version above was edited with the author for length and includes additions. Read the original article on Open Democracy.
* See Jeremy Rifkin, The Zero Marginal Cost Society, (New York: Palgrave Macmillan, 2014), Part II, pp. 69-154.
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#431142 Will Privacy Survive the Future?
Technological progress has radically transformed our concept of privacy. How we share information and display our identities has changed as we’ve migrated to the digital world.
As the Guardian states, “We now carry with us everywhere devices that give us access to all the world’s information, but they can also offer almost all the world vast quantities of information about us.” We are all leaving digital footprints as we navigate through the internet. While sometimes this information can be harmless, it’s often valuable to various stakeholders, including governments, corporations, marketers, and criminals.
The ethical debate around privacy is complex. The reality is that our definition and standards for privacy have evolved over time, and will continue to do so in the next few decades.
Implications of Emerging Technologies
Protecting privacy will only become more challenging as we experience the emergence of technologies such as virtual reality, the Internet of Things, brain-machine interfaces, and much more.
Virtual reality headsets are already gathering information about users’ locations and physical movements. In the future all of our emotional experiences, reactions, and interactions in the virtual world will be able to be accessed and analyzed. As virtual reality becomes more immersive and indistinguishable from physical reality, technology companies will be able to gather an unprecedented amount of data.
It doesn’t end there. The Internet of Things will be able to gather live data from our homes, cities and institutions. Drones may be able to spy on us as we live our everyday lives. As the amount of genetic data gathered increases, the privacy of our genes, too, may be compromised.
It gets even more concerning when we look farther into the future. As companies like Neuralink attempt to merge the human brain with machines, we are left with powerful implications for privacy. Brain-machine interfaces by nature operate by extracting information from the brain and manipulating it in order to accomplish goals. There are many parties that can benefit and take advantage of the information from the interface.
Marketing companies, for instance, would take an interest in better understanding how consumers think and consequently have their thoughts modified. Employers could use the information to find new ways to improve productivity or even monitor their employees. There will notably be risks of “brain hacking,” which we must take extreme precaution against. However, it is important to note that lesser versions of these risks currently exist, i.e., by phone hacking, identify fraud, and the like.
A New Much-Needed Definition of Privacy
In many ways we are already cyborgs interfacing with technology. According to theories like the extended mind hypothesis, our technological devices are an extension of our identities. We use our phones to store memories, retrieve information, and communicate. We use powerful tools like the Hubble Telescope to extend our sense of sight. In parallel, one can argue that the digital world has become an extension of the physical world.
These technological tools are a part of who we are. This has led to many ethical and societal implications. Our Facebook profiles can be processed to infer secondary information about us, such as sexual orientation, political and religious views, race, substance use, intelligence, and personality. Some argue that many of our devices may be mapping our every move. Your browsing history could be spied on and even sold in the open market.
While the argument to protect privacy and individuals’ information is valid to a certain extent, we may also have to accept the possibility that privacy will become obsolete in the future. We have inherently become more open as a society in the digital world, voluntarily sharing our identities, interests, views, and personalities.
“The question we are left with is, at what point does the tradeoff between transparency and privacy become detrimental?”
There also seems to be a contradiction with the positive trend towards mass transparency and the need to protect privacy. Many advocate for a massive decentralization and openness of information through mechanisms like blockchain.
The question we are left with is, at what point does the tradeoff between transparency and privacy become detrimental? We want to live in a world of fewer secrets, but also don’t want to live in a world where our every move is followed (not to mention our every feeling, thought and interaction). So, how do we find a balance?
Traditionally, privacy is used synonymously with secrecy. Many are led to believe that if you keep your personal information secret, then you’ve accomplished privacy. Danny Weitzner, director of the MIT Internet Policy Research Initiative, rejects this notion and argues that this old definition of privacy is dead.
From Witzner’s perspective, protecting privacy in the digital age means creating rules that require governments and businesses to be transparent about how they use our information. In other terms, we can’t bring the business of data to an end, but we can do a better job of controlling it. If these stakeholders spy on our personal information, then we should have the right to spy on how they spy on us.
The Role of Policy and Discourse
Almost always, policy has been too slow to adapt to the societal and ethical implications of technological progress. And sometimes the wrong laws can do more harm than good. For instance, in March, the US House of Representatives voted to allow internet service providers to sell your web browsing history on the open market.
More often than not, the bureaucratic nature of governance can’t keep up with exponential growth. New technologies are emerging every day and transforming society. Can we confidently claim that our world leaders, politicians, and local representatives are having these conversations and debates? Are they putting a focus on the ethical and societal implications of emerging technologies? Probably not.
We also can’t underestimate the role of public awareness and digital activism. There needs to be an emphasis on educating and engaging the general public about the complexities of these issues and the potential solutions available. The current solution may not be robust or clear, but having these discussions will get us there.
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