These AIs Can Predict Your Moral Principles

The models assess a person’s choice of words to determine their stance on five key principles of morality

2 min read
Illustration of the word #morality
Illustration: IEEE Spectrum

The death penalty, abortion, gun legislation: There’s no shortage of controversial topics that are hotly debated today on social media. These topics are so important to us because they touch on an essential underlying force that makes us human, our morality.

Researchers in Brazil have developed and analyzed three models that can describe the morality of individuals based on the language they use. The results were published last month in IEEE Transactions on Affective Computing.

Ivandré Paraboni is an associate professor at the School of Arts, Sciences and Humanities at the University of São Paulo who led the study. His team choose to focus on a theory commonly used by social scientists called Moral foundations theory. It postulates several key categories of morality including care, fairness, loyalty, authority, and purity. 

The aim of the new models, according to Paraboni, is to infer values of those five moral foundations just by looking at their writing, regardless of what they are talking about. “They may be talking about their everyday life, or about whatever they talk about on social media,” Paraboni says. “And we may still find underlying patterns that are revealing of their five moral foundations.”

To develop and validate the models, Paraboni’s team provided more than 500 volunteers with questionnaires. Participants were asked to rate eight topics (e.g., same sex marriage, gun ownership, drug policy) with sentiment scores (from 0 = ‘totally against’ to 5 = ‘totally in favor’). They were also asked to write out explanations of their ratings.

Human judges then gave their own rating to a subset of explanations from participants. The exercise determined how well humans could infer the intended opinions from the text. “Knowing the complexity of the task from a human perspective in this way gave us a more realistic view of what the computational models can or cannot do with this particular dataset,” says Paraboni.

Using the text opinions from the study participants, the research team created three machine learning algorithms that could assess the language used in each participant’s statement. The models analyzed psycholinguistics (emotional context of words), words, and word sequences, respectively.

All three models were able to infer an individual's moral foundations from the text. The first two models, which focus on individual words used by the author, were more accurate than the deep learning approach that analyzes word sequences.

Paraboni adds, “Word counts–such as how often an individual uses words like ‘sin’ or ‘duty’–turned out to be highly revealing of their moral foundations, that is, predicting with higher accuracy their degrees of care, fairness, loyalty, authority, and purity.”

He says his team plans to continue to incorporate other forms of linguistic analysis into their models. They are, he says, exploring other models that focus more on the text (independent of the author) as a way to analyze Twitter data.

The Conversation (0)

Will AI Steal Submarines’ Stealth?

Better detection will make the oceans transparent—and perhaps doom mutually assured destruction

11 min read
A photo of a submarine in the water under a partly cloudy sky.

The Virginia-class fast attack submarine USS Virginia cruises through the Mediterranean in 2010. Back then, it could effectively disappear just by diving.

U.S. Navy

Submarines are valued primarily for their ability to hide. The assurance that submarines would likely survive the first missile strike in a nuclear war and thus be able to respond by launching missiles in a second strike is key to the strategy of deterrence known as mutually assured destruction. Any new technology that might render the oceans effectively transparent, making it trivial to spot lurking submarines, could thus undermine the peace of the world. For nearly a century, naval engineers have striven to develop ever-faster, ever-quieter submarines. But they have worked just as hard at advancing a wide array of radar, sonar, and other technologies designed to detect, target, and eliminate enemy submarines.

The balance seemed to turn with the emergence of nuclear-powered submarines in the early 1960s. In a 2015 study for the Center for Strategic and Budgetary Assessment, Bryan Clark, a naval specialist now at the Hudson Institute, noted that the ability of these boats to remain submerged for long periods of time made them “nearly impossible to find with radar and active sonar.” But even these stealthy submarines produce subtle, very-low-frequency noises that can be picked up from far away by networks of acoustic hydrophone arrays mounted to the seafloor.

Keep Reading ↓Show less
{"imageShortcodeIds":["30133857"]}