37: What role do algorithms play in learning? (i.e., adaptive tools, google searches, twitter/facebook streams) #lifewidelearning16 @mrchase
— Ben Wilkoff (@bhwilkoff) February 6, 2016
Sometimes, when I want to know what it’s like not to be me, I’ll jump into incognito mode on Chrome and search for something, anything – just to see what a newborn baby might find on his first search.
If there’s a notable difference, it’s that I’m searching alone. None of the content from my friends (as Google knows them) is present. None of the recommendations take into effect what past me has gone searching for. I’m asking a question of the entire web, not the web as Google curates it for me.
Still, Google lets me search. It doesn’t require I feed its data monster with my specific personal information. I am free to wander the Internet as anonymously as possible for anyone with a static IP address.
When I turn to sites like LinkedIn or Facebook, though, doors are closed. While Google will let me get by only paying with the what of my searching, these sites raise the price – they want to know who.
All of this rests on the idea that computer algorithms are strong in their ability to furnish me with answers. The more they know about the questions I’m asking, the better their ability to anticipate and queue up answers most relevant to me. That’s Me, specifically, not someone like me. Insomuch as is possible for a machine, these lines of code are personalizing the answers for which I’m searching in my learning.
But these algorithms are doing more than that. They are deciding what I don’t see. They are narrowing the Internet I experience. Because search engines and other sites that track my behavior online track what they take to be my habits, the options I see when I go looking for information are the answers I’m anticipated to need or want. And, there’s a trade off. I often find what I’m looking for, but I hardly ever stumble upon something randomly interesting. Imagine traveling the world an avoiding all the places you hadn’t seen or heard about before.
These are the answers algorithms provide.
What’s more, while these lines of code are narrowing the world and people I experience online, they’re failing to help me ask better questions. When I’m led to ask questions online, it’s because of breadcrumbs left by other people on the chance I might want to make a turn. Think of a Wikipedia entry as an example. A well written page includes loads of links to what a computer might read as randomly selected. Even when able to identify parts of speech, it is the human element that decides Prince Adam deserves a link on the entry for Skeletor while leaving Keldor as plain text.
Algorithms suck at curiosity. They don’t anticipate it well, and they rarely engender it in users. Any program that ushers a user through a series of pre-conceived questions is avoiding actual questioning. To keep the travel metaphor going, these experiences are like riding It’s a Small World rather than actually traveling to each of the countries depicted. And, no matter how well such applications anticipate your reaction to a given set of stimuli, whatever is put in front of you next isn’t computer generated, it was programmed by someone who decided where your unknowing should go next.
While the secret soup that makes search engines and other sites pull up the answers to my questions is imperfectly good, I have to remember that it comes at the cost of my information (anonymous or not) and experiencing the world in a way someone like me is “supposed” to see it. This is more limited than I know.
For however good these systems are at finding my answers, they are nowhere near as capable of helping me generate questions as a conversation with a friend or reading a thoughtful editorial. While they are able to learn, they are certainly not curious.
This post is part of a daily conversation between Ben Wilkoff and me. Each day Ben and I post a question to each other and then respond to one another. You can follow the questions and respond via Twitter at #LifeWideLearning16.