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Excerpted from LIVING IN DATA: A Citizen’s Guide to a Better Information Future. Published by MCD, a division of Farrar, Straus and Giroux, on May 4th, 2021. Copyright © 2021 by Jer Thorp. All rights reserved.
Data visualization artist Jer Thorp, artist, writer and teacher living in New York City explores what it means to rely on data that magnifies existing biases.
Photo: Iaremenko/iStock, yp/iStock
Algorithms hold a pivotal and particularly mysterious place in public discussions around data. We speak of Google’s and Facebook’s algorithms as wizards’ spells, cryptic things that we couldn’t possibly understand. Algorithmic bias is raised in almost every data discussion, in classrooms and congressional hearings, as if all of us have some kind of shared definition of what an algorithm is and just exactly how it might be biased.
Computers run by executing sets of instructions. An algorithm is such a set of instructions, in which a series of tasks are repeated until some particular condition is matched. There are all kinds of algorithms, written for all kinds of purposes, but they are most commonly used for programming tasks like sorting and classification. These tasks are well suited to the algorithm’s do/until mentality: Sort these numbers until they are in ascending order. Classify these photographs until they fall neatly into categories. Sort these prisoners by risk of re-offense. Classify these job applicants as “hire” or “do not hire.”
A neural network is not an algorithm itself, because, when activated, it runs only once. It has the “do” but not the “until.” Neural nets are almost always, though, paired with algorithms that train the network, improving its performance over millions or billions of generations...
Algorithms can, in themselves, be biased. They can be coded to weight certain values over others, to reject conditions their authors have defined, to adhere to specific ideas of failure and success. But more often, and perhaps more dangerously, they act as magnifiers, metastasizing existing schematic biases and further darkening the empty spaces of omission. These effects move forward as the spit-out products of algorithms are passed into visualizations and company reports, or as they’re used as inputs for other computational processes, each with its own particular amplifications and specific harms.
Source: Fast Company
