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Getting rigorously naive, or why tech needs philosophy

A liberal arts degree has been a hot ticket in tech lately, according to a recent article in Forbes. Immediately foregrounding bias, this post is written by two philosophers who couldn’t agree more with the views expressed in the article.

Despite countless jokes about from our families and peers about starting a “philosophy store,” it turns out that the ability to rigorously pursue abstract inquiry is actually quite helpful in today’s tech sector. Stewart Butterfield, the CEO and founder of Slack (Ethical Resolve’s favorite Internet service du jour) and holder of a philosophy degree, recently discussed why. He told reporter George Anders that training in philosophy was critical to building the first user-friendly knowledge management tool on the Internet. “I learned how to write really clearly. I learned how to follow an argument all the way down, which is invaluable in running meetings. And when I studied the history of science, I learned about the ways that everyone believes something is true–like the old notion of some kind of ether in the air propagating gravitational forces–until they realized that it wasn’t true.”

There are other philosophers scattered around the tech sector in prominent positions. Damon Horowitz has the title “In-House Philosopher/Director of Engineering” at Google, which he earned after Google acquired his startup. In this TEDx presentation, Horowitz argues that tech requires a “moral operating system” if we are to build data analytics systems that peer deeply into our lives. His view is that tech companies need to make space for careful thinking about ancient questions of morality.

“Deviant philosopher” Alex Karp is CEO and Founder of Palantir, the secretive data analytics firm that has built tools for national security and policing which gather data from highly disparate databases. Karp earned his PhD studying with one of the late 20th Century’s most important social philosophers, Jurgen Habermas. He claims that behind Palantir’s tech is an ethical commitment to foster data-mining data mining practices that subvert the heretofore-zero-sum game between security and privacy and leave appropriate space for deviant behavior (whether Palantir’s products actually do that is entirely different question). Other famous fonts of innovation in Silicon Valley have also employed the philosophically-minded, such as the anthropologists at Xerox’s Palo Alto Research Center (PARC).

In our view the traits that make philosophical training so useful in tech are actually the opposite of what you would expect. Abstract thought looks meandering and pointless from the outside, but learning to pursue questions to their end is actually an exercise in precision and purpose. For example, 19th Century theories of language might look completely dated, but they were critical to the creation of the formal logic that makes computer code possible.

Perhaps the single most important trait of philosophical training is what I’ve taken to calling “getting rigorously naive.” Others have called it asking stupid questions! When faced with what looks like an intractable problem with no good solutions, sometimes the best strategy for making the problem tractable again is to dig into even our most basic assumptions. At it’s heart, digging into our most basic assumptions is what philosophy is all about.

When clients have come to us for advice on business decisions with significant financial and ethical consequences, the real value-added component of our research and consulting is the ability to identify the most critical decision points and chase down consequences to their logical ends. Getting naive is something to do rigorously because the questions that get asked need to lead our clients back to an effective and efficient decision.

Bringing the abstract into the concrete is what we do best at Ethical Resolve. We ask the questions your team hasn’t thought of so that you can see the option that wasn’t there before. In the moments where the accepted wisdom isn’t cutting it, questioning your most basic assumptions might be exactly what you need.

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