Welcome, everybody. My name is Ed Finn. I am the Director of the Center for Science and the Imagination at Arizona State University. I’m also the Academic Director of Future Tense. And you are here at Future Tense. This is a partnership between the New America Foundation, Arizona State University, and *Slate* magazine that explores emerging technologies and their transformative effects on society and public policy.

Central to this partnership is an event series here in Washington DC. There’s a blog on *Slate*, and we also do all sorts of things in different places. For example, we recently did an event series called My Favorite Movie with the National Science Foundation Director Francis Córdova. There was an event on afrofuturism in New York recently. And watch this space in January for an event on human/robot interaction.

And today, of course we’re going to be talking about algorithms. The event today, The Tyranny of Algorithms, you can follow the discussion online with the hashtag #tyrannyofalgorithms, and follow us at @FutureTenseNow, because the algorithms will be following us, and so I think you guys should, too.

So, The Tyranny of Algorithms is obviously a polemical title to start a conversation around computation and culture. But I think that it helps us get into the cultural, the political, the legal, the ethical dimensions of code. Because we so often think of code, and code is so often constructed, in a purely technical framework, by people who see themselves as solving technical problems. And so, I’m not interested in making a hard argument today, and I don’t think that others here are, about algorithms as *truly* tyrannous. But I think that it’s a way of posing the question about what’s really a sort of gravitational tug, the gravitational pull of computation.

As an example, when I came here yesterday…I wear one of those little Fitbit activity trackers. And in the chaos of dealing with our two young children and getting out the door to get to the airport, I forgot my Fitbit. And I felt the tug, because I thought to myself, here I am, I’m walking all over Washington DC. I’m getting all this great exercise, and it counts for *nothing*, because I’m not tracking it and it’s not going be part of my statistics. And then I also forgot my watch, and I realized that I could live without the Fitbit but I could not moderate this event without a watch, so I had to go buy one last night.

So, that notion of algorithms as cultural systems as well as technical systems is really important. They embed mathematical rigor, incredibly clever and sophisticated rational thought, and intellectual constructions. But they also inevitably include all the faulty assumptions and incomplete models and good enough approximations of reality that aren’t actually good enough that humans inevitably create whenever we’re building anything. And so I want to lay out a couple of ideas around the notion of algorithm and what algorithms know as a way to kick things off.

And first, why “algorithm” as opposed to another word like computation or big data. And for me, algorithms are the the place where the rubber meets the road, the moment of intersection between the idealism of mathematics, the idealism of policy, the idealism of big ideas, and the pragmatism of actually building a system that functions in the world.

So in a way, algorithms are recipes—and I’ll talk about a couple of definitions of algorithms. And I like that term “recipe” because it expresses both the the notion of logical order and sequencing. The practicality of a sequence of steps that are simple, straightforward, and will have dependable results. But also the intimacy, the idea that algorithms are not just out in the world, they’re increasingly very close to us. For many of us, fractions of an inch from our bodies right now in our smartphones and our smart devices, and they’re in our heads in all sorts of interesting ways.

So what exactly is an algorithm? I’ll give you three simple definitions. The engineer’s version is that it’s a method for solving a problem. An algorithm is a dependable set of steps that you can take to solve a problem. Typically, let’s call it a mathematical or logical problem.

The mathematician’s version, which goes back to Alan Turing and Alonzo Church and others who were thinking about the foundations of computer science, is that algorithms occupy the space of effective computability. That is, all the things that can be computed in a finite amount of time. So it’s the space of mathematics that can be computed, that can be translated into a sequence of steps that a computer can solve in a finite amount of time. And we’ll turn back to that in a moment, as well.

And then the romantic’s version— I’m not sure if this quote is real, but it’s too good to ignore, by the computer scientist Francis Sullivan. It’s in course materials from one of the classic computer science courses that I took as an undergrad, Algorithms and Data Structures. So, this quote from Francis Sullivan says,

For me, great algorithms are the poetry of computation. Just like verse, they can be terse, allusive, dense, and even mysterious. But once unlocked they cast a brilliant new light on some aspect of computing.

And I think that notion of poetry is really interesting, because it suggests that algorithms have some kind of an interior life, or that there is mystery. That algorithms are not only the predictable products of rational thought. That they can surprise us. And I’ll come back to that as well.

So, algorithms, where do they come from? They’ve been around for a long time. The word comes from Muḥammad ibn Mūsā al‐Khwārizmī, a famous Arabic mathematician who lived in Baghdad who helped reintroduce the West through his work in translation to algebra, and algorithm is a Latinized adaptation from his own name.

What do algorithms know? If you think about that notion of effective computability, it embeds a desire, or a quest to make more things effectively computable. One of the things we’re seeing now, if you think about the stuff that computation has worked on, we went from digitizing maps and storefronts to now, you can stroll through the British Museum in virtual space.

Algorithms are driving cars. They’re helping people get dates. They’re grading essays. They’re writing newspaper articles. They’re playing Jeopardy. They’re diagnosing patients. They’re evaluating loans. You’ve probably heard that metaphor of the notion of the rising tide, that algorithms are gradually getting capable of solving more and more sophisticated problems and challenges, and perhaps displacing humans who used to do that work.

I like to think of this in the context of ubiquitous computing, and the notion of a computational layer, a little bit like Borges’ map and the territory. That layer of sensors and chips and devices is getting thicker every day, and it’s getting more nuanced. And algorithms are the machinery, are the the vehicles, the tools, the objects that live in that layer.

And this is a self‐perpetuating logic. The layer encourages us to build more, right. To digitize further. To bring more things into the web of computation, and make more things effectively computable.

So, I’ll close with just a couple of key words on *how* algorithms know. The first is abstraction. Abstraction is the central intellectual maneuver that allows us to use computational systems to engage in the material world. Abstraction is an incredibly powerful tool, and at times it can be dangerous as well. There’s the classic joke about the physicist who has to calculate something in a barn. He says, “Well, let’s assume every cow is a sphere.” That’s a classic abstraction move.

Another way that algorithms *want* to know is the notion of anticipation. In 2010, Eric Schmidt said famously, “I actually don’t think most people don’t want Google to answer their questions. They want Google to tell them what they should be doing next.” And that notion of anticipation is usually powerful and thought‐provoking.

Surprise, this is where I think things get really interesting, this notion of an interior life for algorithms, because our computational systems increasingly surprise us up and do things that we didn’t expect them to do.

And finally, serendipity. That serendipity is something that is in many ways generated or manufactured. That it can be produced, and that the ways we produce serendipity now are different from the ways we used to produce them.

I hope that we will address these and many other topics in the conversations to follow, so let me invite our first two panelists to come up now.

### Further Reference

The The Tyranny of Algorithms event page.

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