The idea of privacy is in many ways antithetical to the data-driven mindset promoted by computer science curriculums over the past decade. Massive advances in data availability and analytic capabilities has led to a reshaping of many curriculums towards coursework teaching how to manage, explore, assess, understand and exploit these newfound digital riches. Deep learning and other analytics courses are frequently filled beyond capacity. In contrast, the idea that programmers should give up their data riches in the name of privacy is a view that receives little attention in many curriculums.

Today’s computer science curriculums have centralized around preparing tomorrow’s future technology leaders to harness the digital revolution. From managing “big data” to making sense of it through deep learning, coursework has heavily emphasized the positives of today’s digital deluge rather than the considerable negatives it can wreak on privacy, safety and security.

Privacy was historically far too often lumped under the heading of cybersecurity and relegated to an afterthought. Privacy violations were largely viewed in the context of companies losing control of customer data, rather than deliberately harnessing that data in privacy-invading manners.

A company that harvested massive amounts of data from its customers, held onto it without any form of cyber intrusion and lawfully resold that data to others was often viewed as privacy-protecting, since it successfully safeguarded the data in its hands from loss.

An increasing number of programs have begun to integrate some form of ethical training into their curriculums. Yet here the focus has largely been on the programmer themselves, emphasizing how to think about what they do with users’ data and how to address biases in their designs. Such courses may emphasize topics like AI explainability to mitigate inadvertent demographic bias and consideration of algorithmic harm, such as why building an AI system that can forcibly uncover members of vulnerable communities could cause grave harm and thus should not be pursued even if it represents a great technical achievement.

Some programs teach compliance with privacy laws like GDPR, but this guidance typically revolves around “minimal minimization” in which data collection is adjusted to fit the letter of the law, if not its spirit, utilizing the laws’ myriad loopholes and exemptions.

In contrast, library and information science curriculums have historically emphasized privacy and civil liberties concerns, especially the minimization of data collection. In contrast to the digital behemoths racing behind us vacuuming up every byte of our online behavior and interests, libraries have historically adopted precisely the opposite stance, keeping only the bare minimum of information they need and deleting data the first moment they can.

Libraries have historically had enormous insights into our most intimate and unfettered interests, often recording our information consumption from the first children’s books our parents checked out of the library to read to us as infants. If libraries kept this information they could build incredible personalized recommendation systems and generate lucrative revenue streams reselling that data or making it available for advertising.

Instead, most public libraries have practiced absolute minimization in which every data point is discarded the moment it is no longer needed. Rather than keeping a user’s entire checkout history through time, most public libraries have historically kept a list only of the items currently checked out, deleting them as soon as the materials are returned.

This minimization was borne out of necessity, with libraries, especially in the pre-Internet era, of great interest to surveillance-minded authorities.

Computer science curriculums, however, have not historically emphasized this idea of minimization-at-all-costs. Quite the opposite, with data hoarding embraced as the path to limitless riches. After all, even if you pay for a service today you are still the product, as data exhaust becomes more valuable than subscription fees.

Privacy naturally conflicts with capability when it comes to data analytics. The more data and the higher resolution it is, the more insight algorithms can yield. Thus, the more companies prioritize privacy and actively delete everything they can and minimize the resolution on what they do have to collect, the less capability their analytics have to offer.

This represents a philosophical tradeoff. On the one hand, computer science students are taught to collect every datapoint they can at the highest resolution they can and to hoard it indefinitely. This extends all the way to things like diagnostic logging that often becomes an everything-or-nothing concept that has led even major companies to have serious security breaches. On the other hand, disciplines like library and information science emphasize privacy over capability, getting rid of data the moment it is safe to do so.

When it comes to government surveillance, data breaches, ethically questionable research, insider threats and other privacy issues, the less data companies keep about their users, the less information there is that can be misused and the lower their storage and analytic costs. If a company can make do with a terabyte of aggregated data rather than a petabyte of individual-level high resolution data, it can achieve considerable cost savings and move many of its batch analyses to real-time.

In the end, rather than enshrining mottos like “data is the new oil” into the vocabulary of tomorrow’s future technology leaders, perhaps we should emphasize “privacy first” and focus on how companies can absolutely minimize the data they collect to ensure a more privacy-protecting and less Orwellian future.

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No stranger to controversy, a Tony Stark reincarnate — Elon Musk — came out with an ominous prediction recently. “Forget North Korea, AI will start World War III” read the CNN headline. Elon Musk is not alone in fearing unintended consequences of the race to develop algorithms that we may or may not be able to control. Once a new technology is introduced it can’t be uninvented — Sam Harris points out in his viral TED talk. He argues that it’ll be impossible to halt the pace of progress, even if humankind could collectively make such a decision.

The critics and cheerleaders of AI alike agree on one thing: intelligence explosion will change the world beyond recognition.

While Bill Gates, Stephen Hawking and countless others are broadly on the same page with Musk and Harris, some of the leading thinkers recognize that AI, like any other technology, is value-neutral. Gunpowder, after all, was first used in fireworks.

Ray Kurzweil argues that “AI will be the pivotal technology in achieving [human] progress. We have a moral imperative to realize this promise while controlling the peril.” And, in his view, humanity has ample time to develop ethical guidelines and regulatory standards.

Making computers part of us, part of our bodies, is going to change our capabilities so much that one day, we will see our current selves as goldfish.

As the world edges towards singularity, future technology is bound to enhance the human experience in some way, and it is up to us to make sure it is for the better.

The critics and cheerleaders of AI alike agree on one thing: intelligence explosion will change the world beyond recognition. When thinking about the future, I found the metaphor offered by Vernor Vinge, on the Invisibilia podcast, especially stark: “making computers part of us, part of our bodies, is going to change our capabilities so much that one day, we will see our current selves as goldfish.” If this is a true extent of our expected AI-and-Tech-powered evolution, our contemporary norms and conventions go straight out of the window.

Putting the war and AI in the same sentence, we anthropomorphize the latter.

Even if the accurate predictions are a dud, shouldn’t we at least attempt to apply the prism of exponential technologies to review our basic assumptions, question fundamentals of human behavior, and scrutinize our societal organization? AI’s promise could be an apocalypse or eternal bliss or anything in between, but, as we speculate on the outcome, we are making a value judgment. And here we ought to recognize our susceptibility to the projection bias. It compels us to apply the present-day intellectual framing to ponder the future.

Putting the war and AI in the same sentence, we anthropomorphize the latter. When we worry about the robots and machine-intelligence causing mass unemployment, we must recognize that such anxiety is only justified if human labor remains an economic necessity. When we say that the spiraling-out-of-control tech progress will create more inequality, we assume that the idea of private property, wealth, and money will survive the fourth-industrial revolution.

It’s an arduous task to define the fundamental terms, much less to question them. But, perhaps, playing out a couple of scenarios could prove a useful exercise is circumventing projection bias.

Competition & Collaboration

The natural selection is, at its core, a multidimensional competition of traits and behaviors. It manifests itself in a basic competitive instinct that humans are all too familiar with. Evolutionary psychology postulates that the driver of human behavior is a need to perpetuate one’s genes. So Homo Sapiens evolved competing for mates, fighting for resources to feed the offspring, all with a singular objective to maximize their genes’ chances to be passed on.

When the algorithms are better at decision-making than humans, and we surrender much of our autonomy to them, how will our competitive instinct fare?

On the other hand, we are, according to Edward O. Wilson, “one of only two dozen or so animal lines ever to evolve eusociality, the next major level of biological organization above the organismic. There, group members across two or more generations stay together, cooperate, care for the young, and divide labor…” In other words, we might have to attribute the stunning success of our species to the fine balance we’ve maintained between competition and cooperation instincts.

What will be the point of resource competition in the world of abundance?

Whether general machine intelligence is imminent or even achievable, the idea of post-scarcity economy is gaining ground. If and when the automation of pretty much everything delivers the world where human labor is redundant, what will be the wider ramifications for our value system and societal organization? When the algorithms are better at decision-makingthan humans, and we surrender much of our autonomy to them, how will our competitive instinct fare?

What will be the point of resource competition in the world of abundance? Is it possible that our instinct to compete slowly evaporates as a useful construct? Could we evolve to live without it? Unlike ants and bees that cooperate on the basis of rigid protocols, humans are spectacularly adaptable in our cooperation abilities. According to Yuval Harari, that’s what ultimately underpinned the rise of sapiens to dominate the Earth. Is it conceivable that the need to compete turns into an atavism as the technic transformations described by Kurzweil begin to materialize?

Economy

How can we be sure that the basic pillars of our economic thinking (e.g. private property, ownership, capital, wealth, etc.) will survive post-scarcity? 100 years from now, will anybody care for labor productivity? How relevant could our policies encouraging employment be when all of the humanity is freeriding on the “efforts” of the machines? What are we left with, when the basics such as supply and demand have been shuddered?

If ownership is pointless and money is no longer a useful unit of exchange, how will we define status?

To a gainfully employed person, today, a prospect of indefinite leisure might appear more of a curse than a blessing. This sentiment, viewed through the lens of natural selection, makes sense. The economic contribution by all able members of society would’ve been preferred to the mass pursuit of idleness. But should we be projecting the same trend into the future? What may sound like decadence and decay to us now, may be construed quite differently in the world no longer powered by the known economic forces.

The working assumption is that no matter what, someone will have to own the machines and pay for goods and services. Yet the idea of property and money is nothing more than social constructs that we all agreed on. If ownership is pointless and money is no longer a useful unit of exchange, how will we define status?

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Work is a defining feature of our civilisation. We spend more time in our jobs than any other activity and the spoils of our labour provide us the means to survive. It gives identity, status and purpose.

So we can be forgiven if we get nervous when our jobs are threatened. And there has never been a clearer threat than increasing automation through robotics and AI.

I am of the camp that ultimately, in about 15 years, we will all be far better off, our notion of work evolved and with it our lives. Yet the transition up to that point is going to be very harsh for most, leaving plenty of scars of inequality and alienation on our society.

Talk of universal basic income, the taxation of robot output and a demotion of human capital to that of mere data suppliers is real. How our innovators introduce the coming disruption will shape its impact. How we manage this transition as a people will define us all.

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