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A Transhumanist Opinion on Privacy – Article by Ryan Starr

A Transhumanist Opinion on Privacy – Article by Ryan Starr

The New Renaissance HatRyan Starr

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Privacy is a favorite topic of mine. Maintaining individual privacy is a crucial element in free society. Yet there are many who want to invade it for personal or political gain. As our digital fingerprint becomes a part of our notion of self, how do we maintain our personal privacy on an inherently impersonal network of data? Where do we draw that line on what is private, and how do we enforce it? These are questions that are difficult to answer when looking at a short-term perspective. However, if we look further into the probable future, we can create a plan that helps protect the privacy of citizens today and for generations to come. By taking into account the almost certain physical merger of human biology and technology, the answer becomes clear. Our electronic data should be treated as part of our bodily autonomy.

The explosive success of social media has shown that we already view ourselves as partly digital entities. Where we go, what we eat, and who we are with is proudly displayed in cyberspace for eternity. But beyond that we store unique data about ourselves “securely” on the internet. Bank accounts, tax returns, even medical information are filed away on a server somewhere and specifically identified as us. It’s no longer solely what we chose to let people see. We are physical and digital beings, and it is time we view these two sides as one before we take the next step into enhanced humanity.

Subdermal storage of electronic data is here, and its storage capabilities will expand rapidly. Soon we will be able to store a lot more than just access codes for our doors. It is hard to speculate exactly what people will chose to keep stored this way, and there may even come a time when what we see and hear is automatically stored this way. But before we go too far into what will be stored, we must understand how this information is accessed in present time. These implants are currently based in NFC technology. Near-Field Communication is a method of storing and transmitting data wirelessly within a very short distance. Yes, “wireless” is the key word. It means that if I can connect my NFC tag to my smart phone by just waiving my hand close to it (usually within an inch or so), then technically someone else can, too. While current antenna limitations and the discreetness of where a person’s tag is implanted create a highly secure method of storage, advances in technology will eventually make it easier to access the individual. This is why it is urgent we develop a streamlined policy for privacy.

The current Transhumanist position is that personally collected intellectual property, whether stored digitally or organically, is the property of the individual. As such, it should be protected from unauthorized search and download. The current platform also states that each individual has the freedom to enhance their own body as they like so long as it doesn’t negatively impact others. However, it does not specify what qualifies as a negative impact or how to prevent it. Morphological freedom is a double-edged sword. A person can a person enhance their ability to access information on themselves, but they can also use it to access others. It is entirely feasible enhancements will be created that allow a person to hack another. And collecting personal data isn’t the only risk with that. What if the hacking victim has an artificial heart or an implanted insulin pump? The hacker could potentially access the code the medical device is operating with and change or delete it, ultimately leading to death. Another scenario might be hacking into someone’s enhanced sensory abilities. Much like in the novel Ender’s Game, a person can access another to see what they see. This ability can be abused countless ways ranging from government surveillance to sexual voyeurism. While this is still firmly within the realm of science fiction, a transhuman society will need to create laws to protect against these person-to-person invasions of privacy.

Now let’s consider mass data collection. Proximity beacons could easily and cheaply be scattered across stores and cities to function as passive collection points much like overhead cameras are today. Retail stands to gain significantly from this technology, especially if they are allowed access to intimate knowledge about customers. Government intelligence gathering also stands to benefit from this capability. Levels of adrenaline, dopamine, and oxytocin stored for personal health analysis could be taken and paired with location data to put together an invasive picture of how people are feeling in a certain situation. Far more can be learned and exploited when discreetly collected biodata is merged with publicly observable activity.

In my mind, these are concerns that should be addressed sooner than later. If we take the appropriate steps to preserve personal privacy in all domains, we can make a positive impact that will last into the 22nd century.
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Ryan Starr is the leader of the Transhumanist Party of Colorado. This article was originally published on his blog, and has been republished here with his permission.
No, “Big Data” Can’t Predict the Future – Article by Per Bylund

No, “Big Data” Can’t Predict the Future – Article by Per Bylund

The New Renaissance HatPer Bylund
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With Google’s dominance in the online search engine market we entered the Age of Free. Indeed, services offered online are nowadays expected to be offered at no cost. Which, of course, does not mean that there is no cost to it, only that the consumer doesn’t pay it. Early attempts financed the services with ads, but we soon saw a move toward making the consumer the product. Today, free and unfree services alike compete for “users” and then make money off the data they collect.

Data has always been used, but what’s new for our time is the very low (or even zero) marginal cost for collecting and analyzing huge amounts of data. The concept of “Big Data” is taking over and is predicted to be “the future” of business.

There’s a problem here, and it is the over-reliance on the Law of Large Numbers in social forecasting. Statistical probabilities for events may mathematically converge to the mean, but is it applicable in the real world? The answer is most definitely yes in the natural sciences. Repeated controlled experiments will weed out erroneous explanations or causes to phenomena, at least assuming we’re good enough at separating and controlling those causes.

What about the social sciences? In this age of scientism, as Hayek called it, we’re told “Big Data” will completely transform production, logistics, and sales. The reason for this is that vendors can better target customers and even foresee what they might want next. Amazon.com does this on their web site in crude form, where they make suggestions based on your purchase history and what others with similar purchase histories have searched for. Sometimes it works, and sometimes it doesn’t.

There is some regularity to our interests and behavior. All of us are, after all, human beings — and we’re formed in certain cultures. So one American with interests x, y, and z may have other interests similar to another American who also has an interest in x, y, and z.

Human Behavior Is Unpredictable
But similarity is not the same thing as prediction. Amazon.com’s suggestions or the highly annoying ads following you around web sites are useful methods for sellers because they can somewhat accurately identify what not to offer. Exclusion of very low-probability interests increases the probability for suggesting something that the person behind the eyeballs focusing on the computer screen may be interested in.

To use as prediction, however, exclusion of almost-zero probability events is far from sufficient. Indeed, prediction requires that we are able to accurately exclude all but one or a couple highly probable outcomes. And we have to be able to rely on that these predictions turn out to be true. Otherwise we’re just playing games, and so we’re making guesses. Sure, they’re educated guesses (because we’ve excluded the impossible and almost-impossible), but they’re still games and guesses.

Where Big Data Fails
Speaking of guesses, Microsoft’s Bing search engine, which powers the Windows digital assistant Cortana among other things, has produced a prediction engine with the purpose of predicting sports and other results. They rely on very advanced algorithms and huge amounts of collected data.

Amazingly, they did very well initially and predicted the outcomes of the World Cup perfectly. So maybe we can use Big Data to get a glimpse of the future?

No, not so. The Bing teams are learning a lesson only Austrians and, more specifically, Misesian praxeologists, seem to be alone in grasping: that there are no constants in human action, and therefore that predictions of social phenomena are impossible. Pattern predictions, as Hayek called them, may not be impossible, but predictions of exact magnitudes are. For instance, we can rely on economic law (such as “demand curves slope downward”) to estimate an outcome such as “the price will be lower than it otherwise would have been,” but we can’t say exactly what that price will be.

When it comes to sports, reality shows and other competitions between individuals or teams, the story is exactly the same. The team with a better track record doesn’t always win. Why? They have objectively performed better than the other team, perhaps exclusively so, but this doesn’t say anything about the future. We’re not here referring to the philosophical doubt as in “will the sun shine tomorrow?” (maybe something changes completely the sun’s ability to shine during the night).

The Social Sciences Are Different
In the social sciences we’re dealing with complex phenomena. Action and, especially, its outcome is the result of a complex system of social interaction, psychology, and much more. Are the players in both teams as motivated and focused as they were before? Did anything in their personal lives affect their mindsets or psyches? How do the players within their teams and players in other teams react on each other before and during the game? A team with a poor track record can upset a team with an objectively better track record; this happens all the time. Sometimes for the sole reason that the better team underestimates the worse team, or because the underdog feels no pressure to perform and therefore plays less defensively.

Bing’s prediction engine struggles with this, just as we would predict. As Windows Central reported recently, the prediction engine had its “worst week yet” picking only four of fourteen winners in the NFL. Overall, its track record was approximately two-thirds right and one-third wrong (95–53). It’s definitely better than tossing a coin, but pretty far from actually predicting the results.

In other words, if you’re placing bets you may want to use the Bing prediction engine. That is, unless you have the type of tacit, implicit understanding of what’s going on that the engine is missing. Maybe you can beat it, or maybe not. In either case, you cannot count on coming out a victor each and every time.

The reason for this is that the outcome simply cannot be predicted perfectly — or even close to it. Even the players themselves cannot predict who’ll win a game, but they may have inside information about whether their own team seems motivated and focused. It is not a perfect method, however, and it certainly cannot be scientific.

Even with Big Data there’s no predicting of social events — there’s only guessing. Yes, guessing with access to huge amounts of data is easier, at least if the data is reliable and relevant. But a good guess is not the same thing as a prediction; it is still a guess, and it can be wrong. Winning every time requires luck.

Per L. Bylund, PhD, is Assistant Professor of Entrepreneurship and Records-Johnston Professor of Free Enterprise in the School of Entrepreneurship at Oklahoma State University. Visit his website at PerBylund.com.

This article was originally published by the Ludwig von Mises Institute. Permission to reprint in whole or in part is hereby granted, provided full credit is given.