#19: Authenticity and the Black Box of Algorithms
Authenticity has been a centuries-long project, but algorithms know more about you today.
Disclaimer: This edition was written by our community member, Varun Choraria.
Dear Reader,
At the outset, we’re hoping you’re doing okay and staying healthy. In order to keep you updated, we’ve been compiling some helpful resources in #general, and it’s been pinned to the channel.
You can also check out Edition #17, solely dedicated to some resources we’d compiled.
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There’s this famous Oscar Wilde quote that says, “Be yourself, everyone else is taken.” Whether it’s monks, desk workers, artists, hippies- at one level or another almost everyone’s in search of themselves. On that note, ‘authenticity’, or the legitimacy/ uniqueness of ourselves is a hard path to take- full of paradoxes, tough questions and even tougher answers.
This week, we’re featuring two brilliant stories from the web, trying to make an effort to explore a correlation between our natural personalities, and our “commoditised personalities” that modern day capitalism is driving.
We hope you enjoy our picks.
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If you’re curious about Bitcoin, fluctuations in gold or silver prices, then Rishi has shared some interesting insights in #finance. It’s indeed a goldmine of some great resources!
Is Authenticity a sham? Do algorithms really know more about us?
There’s this famous Oscar Wilde quote that says, “Be yourself, everyone else is taken.” Whether it’s monks, desk workers, artists, hippies- at one level or another almost everyone’s in search of themselves. On that note, ‘authenticity’, or the legitimacy/ uniqueness of ourselves is a hard path to take- full of paradoxes, tough questions and even tougher answers.
Give this a thought: Everyone else might be taken, but the effort to be ourselves is the surest path to being just like everyone else, especially in the context of a highly commodified and surveilled culture where we always seem to be on stage. If some person or organisation claims to be concerned with authenticity, you can be almost certain that they’re conformist posers. As Wilde actually did write: ‘Most people are other people. Their thoughts are someone else’s opinions, their lives a mimicry, their passions a quotation.’ (Or misquotation.)
Where did all these paradoxes arise from?
Paradoxically, one of the primary vehicles of bad faith in our own time has become the ‘jargon of authenticity’ itself. Religion, Business, conformity, unrealistic benchmarks, etc.- have all estimated different meanings around authenticity. If we’re lucky, then, we’ll be able to see ourselves reflected in meaningful work, in what we produce, but what about higher meaning?
Let’s add a little spice to this.
Facebook, or most social media these days are somewhat of an intentional bad actor. It’s like we’re in dystopia, and Big Brother is trying to profile us in every way possible.
Facebook’s most aggravating aspect is also its most intriguing: the way it attempts to predict our needs and desires. On the surface, it’s simple: The platform uses what it knows about us from our clicks and likes and shares and choices of friends to allow marketers to deliver ads to which we are likely to respond. Sometimes the data is analyzed by Facebook itself, and sometimes it is analyzed by Facebook’s clients — who can then combine the information they get about us on Facebook with the data they get from the web’s many other surveillance tools.
But as distorted as it may be in its current form, Facebook is still a kind of mirror. And even if it doesn’t represent us accurately — especially if it doesn’t represent us accurately — Facebook offers us a rare opportunity to explore the difference between what algorithms assume about us and who we really are. The aspects of ourselves that they can’t categorize meaningfully, the places where they falter, represent some of our most important qualities as human beings simply by virtue of the fact that algorithms can’t yet quantify them. Maybe the best way to know which aspects of our humanity are not computational is to pay closer attention to the limits of the algorithms in use all around us.
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