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AI could shore up democracy – here’s one way

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AI could shore up democracy – here’s one way

AI could help elected representatives raise up constituent voices.
AP Photo/Patrick Semansky

Bruce Schneier, Harvard Kennedy School and Nathan Sanders, Harvard University

It’s become fashionable to think of artificial intelligence as an inherently dehumanizing technology, a ruthless force of automation that has unleashed legions of virtual skilled laborers in faceless form. But what if AI turns out to be the one tool able to identify what makes your ideas special, recognizing your unique perspective and potential on the issues where it matters most?

You’d be forgiven if you’re distraught about society’s ability to grapple with this new technology. So far, there’s no lack of prognostications about the democratic doom that AI may wreak on the U.S. system of government. There are legitimate reasons to be concerned that AI could spread misinformation, break public comment processes on regulations, inundate legislators with artificial constituent outreach, help to automate corporate lobbying, or even generate laws in a way tailored to benefit narrow interests.

But there are reasons to feel more sanguine as well. Many groups have started demonstrating the potential beneficial uses of AI for governance. A key constructive-use case for AI in democratic processes is to serve as discussion moderator and consensus builder.

To help democracy scale better in the face of growing, increasingly interconnected populations – as well as the wide availability of AI language tools that can generate reams of text at the click of a button – the U.S. will need to leverage AI’s capability to rapidly digest, interpret and summarize this content.

An old problem

There are two different ways to approach the use of generative AI to improve civic participation and governance. Each is likely to lead to drastically different experience for public policy advocates and other people trying to have their voice heard in a future system where AI chatbots are both the dominant readers and writers of public comment.

For example, consider individual letters to a representative, or comments as part of a regulatory rulemaking process. In both cases, we the people are telling the government what we think and want.

For more than half a century, agencies have been using human power to read through all the comments received, and to generate summaries and responses of their major themes. To be sure, digital technology has helped.

black-and-white photo of a man in a business suit holding a letter with a large pile of mail on the wooden desk in front of him
Taking in comments from the public has been a challenge for representatives and their staffs for many decades.
AP Photo

In 2021, the Council of Federal Chief Data Officers recommended modernizing the comment review process by implementing natural language processing tools for removing duplicates and clustering similar comments in processes governmentwide. These tools are simplistic by the standards of 2023 AI. They work by assessing the semantic similarity of comments based on metrics like word frequency (How often did you say “personhood”?) and clustering similar comments and giving reviewers a sense of what topic they relate to.

Getting the gist

Think of this approach as collapsing public opinion. They take a big, hairy mass of comments from thousands of people and condense them into a tidy set of essential reading that generally suffices to represent the broad themes of community feedback. This is far easier for a small agency staff or legislative office to handle than it would be for staffers to actually read through that many individual perspectives.

But what’s lost in this collapsing is individuality, personality and relationships. The reviewer of the condensed comments may miss the personal circumstances that led so many commenters to write in with a common point of view, and may overlook the arguments and anecdotes that might be the most persuasive content of the testimony.

Most importantly, the reviewers may miss out on the opportunity to recognize committed and knowledgeable advocates, whether interest groups or individuals, who could have long-term, productive relationships with the agency.

These drawbacks have real ramifications for the potential efficacy of those thousands of individual messages, undermining what all those people were doing it for. Still, practicality tips the balance toward of some kind of summarization approach. A passionate letter of advocacy doesn’t hold any value if regulators or legislators simply don’t have time to read it.

Finding the signals and the noise

There is another approach. In addition to collapsing testimony through summarization, government staff can use modern AI techniques to explode it. They can automatically recover and recognize a distinctive argument from one piece of testimony that does not exist in the thousands of other testimonies received. They can discover the kinds of constituent stories and experiences that legislators love to repeat at hearings, town halls and campaign events. This approach can sustain the potential impact of individual public comment to shape legislation even as the volumes of testimony may rise exponentially.

YouTube video
Representatives often use anecdotes from constituents to humanize issues.

In computing, there is a rich history of that type of automation task in what is called outlier detection. Traditional methods generally involve finding a simple model that explains most of the data in question, like a set of topics that well describe the vast majority of submitted comments. But then they go a step further by isolating those data points that fall outside the mold — comments that don’t use arguments that fit into the neat little clusters.

State-of-the-art AI language models aren’t necessary for identifying outliers in text document data sets, but using them could bring a greater degree of sophistication and flexibility to this procedure. AI language models can be tasked to identify novel perspectives within a large body of text through prompting alone. You simply need to tell the AI to find them.

In the absence of that ability to extract distinctive comments, lawmakers and regulators have no choice but to prioritize on other factors. If there is nothing better, “who donated the most to our campaign” or “which company employs the most of my former staffers” become reasonable metrics for prioritizing public comments. AI can help elected representatives do much better.

If Americans want AI to help revitalize the country’s ailing democracy, they need to think about how to align the incentives of elected leaders with those of individuals. Right now, as much as 90% of constituent communications are mass emails organized by advocacy groups, and they go largely ignored by staffers. People are channeling their passions into a vast digital warehouses where algorithms box up their expressions so they don’t have to be read. As a result, the incentive for citizens and advocacy groups is to fill that box up to the brim, so someone will notice it’s overflowing.

A talented, knowledgeable, engaged citizen should be able to articulate their ideas and share their personal experiences and distinctive points of view in a way that they can be both included with everyone else’s comments where they contribute to summarization and recognized individually among the other comments. An effective comment summarization process would extricate those unique points of view from the pile and put them into lawmakers’ hands.The Conversation

Bruce Schneier, Adjunct Lecturer in Public Policy, Harvard Kennedy School and Nathan Sanders, Affiliate, Berkman Klein Center for Internet & Society, Harvard University

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Study shows surge of imagery and fakes can precede international and political violence

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theconversation.com – Tim Weninger, Collegiate Proessor of Engineering, University of Notre Dame – 2025-04-24 07:59:00

AI tools reveal how images have been manipulated.
William Theisen et al.

Tim Weninger, University of Notre Dame and Ernesto Verdeja, University of Notre Dame

Imagine a country with deep political divisions, where different groups don’t trust each other and violence seems likely. Now, imagine a flood of political images, hateful memes and mocking videos from domestic and foreign sources taking over social media. What is likely to happen next?

The widespread use of social media during times of political trouble and violence has made it harder to prevent conflict and build peace. Social media is changing, with new technologies and strategies available to influence what people think during political crises. These include new ways to promote beliefs and goals, gain support, dehumanize opponents, justify violence and create doubt or dismiss inconvenient facts.

At the same time, the technologies themselves are becoming more sophisticated. More and more, social media campaigns use images such as memes, videos and photos – whether edited or not – that have a bigger impact on people than just text.

It’s harder for AI systems to understand images compared with text. For example, it’s easier to track posts that say “Ukrainians are Nazis” than it is to find and understand fake images showing Ukrainian soldiers with Nazi symbols. But these kinds of images are becoming more common. Just as a picture is worth a thousand words, a meme is worth a thousand tweets.

Our team of computer and social scientists has tackled the challenge of interpreting image content by combining artificial intelligence methods with human subject matter experts to study how visual social media posts change in high-risk situations. Our research shows that these changes in social media posts, especially those with images, serve as strong indicators of coming mass violence.

Surge of memes

Our recent analysis found that in the two weeks leading up to Russia’s 2022 invasion of Ukraine there was a nearly 9,000% increase in the number of posts and a more than 5,000% increase in manipulated images from Russian milbloggers. Milbloggers are bloggers who focus on current military conflicts.

These huge increases show how intense Russia’s online propaganda campaign was and how it used social media to influence people’s opinions and justify the invasion.

This also shows the need to better monitor and analyze visual content on social media. To conduct our analysis, we collected the entire history of posts and images from the accounts of 989 Russian milbloggers on the messaging app Telegram. This includes nearly 6 million posts and over 3 million images. Each post and image was time-stamped and categorized to facilitate detailed analysis.

Media forensics

We had previously developed a suite of AI tools capable of detecting image alterations and manipulations. For instance, one detected image shows a pro-Russian meme mocking anti-Putin journalist and former Russian soldier Arkady Babchenko, whose death was faked by Ukrainian security services to expose an assassination plot against him.

The meme features the language “gamers don’t die, they respawn,” alluding to video game characters who return to life after dying. This makes light of Babchenko’s predicament and illustrates the use of manipulated images to convey political messages and influence public opinion.

This is just one example out of millions of images that were strategically manipulated to promote various narratives. Our statistical analysis revealed a massive increase in both the number of images and the extent of their manipulations prior to the invasion.

Political context is critical

Although these AI systems are very good at finding fakes, they are incapable of understanding the images’ political contexts. It is therefore critical that AI scientists work closely with social scientists in order to properly interpret these findings.

Our AI systems also categorized images by similarity, which then allowed subject experts to further analyze image clusters based on their narrative content and culturally and politically specific meanings. This is impossible to do at a large scale without AI support.

For example, a fake image of French president Emmanuel Macron with Ukrainian governor Vitalii Kim may be meaningless to an AI scientist. But to political scientists the image appears to laud Ukrainians’ outsize courage in contrast to foreign leaders who have appeared to be afraid of Russian nuclear threats. The goal was to reinforce Ukrainian doubts about their European allies.

image of of two men, one seated
This manipulated image combines French president Emmanuel Macron with Ukranian governor Vitalii Kim. It requires the expertise of political scientists to interpret the creator’s pro-Russian meaning.
William Theisen et al.

Meme warfare

The shift to visual media in recent years brings a new type of data that researchers haven’t yet studied much in detail.

Looking at images can help researchers understand how adversaries frame each other and how this can lead to political conflict. By studying visual content, researchers can see how stories and ideas are spread, which helps us understand the psychological and social factors involved.

This is especially important for finding more advanced and subtle ways people are influenced. Projects like this also can contribute to improving early warning efforts and reduce the risks of violence and instability.The Conversation

Tim Weninger, Collegiate Proessor of Engineering, University of Notre Dame and Ernesto Verdeja, Associate Professor of Peace Studies and Global Politics, University of Notre Dame

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Colors are objective, according to two philosophers − even though the blue you see doesn’t match what I see

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theconversation.com – Elay Shech, Professor of Philosophy, Auburn University – 2025-04-25 07:55:00

What appear to be blue and green spirals are actually the same color.
Akiyoshi Kitaoka

Elay Shech, Auburn University and Michael Watkins, Auburn University

Is your green my green? Probably not. What appears as pure green to me will likely look a bit yellowish or blueish to you. This is because visual systems vary from person to person. Moreover, an object’s color may appear differently against different backgrounds or under different lighting.

These facts might naturally lead you to think that colors are subjective. That, unlike features such as length and temperature, colors are not objective features. Either nothing has a true color, or colors are relative to observers and their viewing conditions.

But perceptual variation has misled you. We are philosophers who study colors, objectivity and science, and we argue in our book “The Metaphysics of Colors” that colors are as objective as length and temperature.

Perceptual variation

There is a surprising amount of variation in how people perceive the world. If you offer a group of people a spectrum of color chips ranging from chartreuse to purple and asked them to pick the unique green chip – the chip with no yellow or blue in it – their choices would vary considerably. Indeed, there wouldn’t be a single chip that most observers would agree is unique green.

Generally, an object’s background can result in dramatic changes in how you perceive its colors. If you place a gray object against a lighter background, it will appear darker than if you place it against a darker background. This variation in perception is perhaps most striking when viewing an object under different lighting, where a red apple could look green or blue.

Of course, that you experience something differently does not prove that what is experienced is not objective. Water that feels cold to one person may not feel cold to another. And although we do not know who is feeling the water “correctly,” or whether that question even makes sense, we can know the temperature of the water and presume that this temperature is independent of your experience.

Similarly, that you can change the appearance of something’s color is not the same as changing its color. You can make an apple look green or blue, but that is not evidence that the apple is not red.

Apple under a gradient of red to blue light
Under different lighting conditions, objects take on different colors.
Gyozo Vaczi/iStock via Getty Images Plus

For comparison, the Moon appears larger when it’s on the horizon than when it appears near its zenith. But the size of the Moon has not changed, only its appearance. Hence, that the appearance of an object’s color or size varies is, by itself, no reason to think that its color and size are not objective features of the object. In other words, the properties of an object are independent of how they appear to you.

That said, given that there is so much variation in how objects appear, how do you determine what color something actually is? Is there a way to determine the color of something despite the many different experiences you might have of it?

Matching colors

Perhaps determining the color of something is to determine whether it is red or blue. But we suggest a different approach. Notice that squares that appear to be the same shade of pink against different backgrounds look different against the same background.

Green, purple and orange squares with smaller squares in shades of pink placed at their centers and at the bottom of the image
The smaller squares may appear to be the same color, but if you compare them with the strip of squares at the bottom, they’re actually different shades.
Shobdohin/Wikimedia Commons, CC BY-SA

It’s easy to assume that to prove colors are objective would require knowing which observers, lighting conditions and backgrounds are the best, or “normal.” But determining the right observers and viewing conditions is not required for determining the very specific color of an object, regardless of its name. And it is not required to determine whether two objects have the same color.

To determine whether two objects have the same color, an observer would need to view the objects side by side against the same background and under various lighting conditions. If you painted part of a room and find that you don’t have enough paint, for instance, finding a match might be very tricky. A color match requires that no observer under any lighting condition will see a difference between the new paint and the old.

YouTube video
Is the dress yellow and white or black and blue?

That two people can determine whether two objects have the same color even if they don’t agree on exactly what that color is – just as a pool of water can have a particular temperature without feeling the same to me and you – seems like compelling evidence to us that colors are objective features of our world.

Colors, science and indispensability

Everyday interactions with colors – such as matching paint samples, determining whether your shirt and pants clash, and even your ability to interpret works of art – are hard to explain if colors are not objective features of objects. But if you turn to science and look at the many ways that researchers think about colors, it becomes harder still.

For example, in the field of color science, scientific laws are used to explain how objects and light affect perception and the colors of other objects. Such laws, for instance, predict what happens when you mix colored pigments, when you view contrasting colors simultaneously or successively, and when you look at colored objects in various lighting conditions.

The philosophers Hilary Putnam and Willard van Orman Quine made famous what is known as the indispensability argument. The basic idea is that if something is indispensable to science, then it must be real and objective – otherwise, science wouldn’t work as well as it does.

For example, you may wonder whether unobservable entities such as electrons and electromagnetic fields really exist. But, so the argument goes, the best scientific explanations assume the existence of such entities and so they must exist. Similarly, because mathematics is indispensable to contemporary science, some philosophers argue that this means mathematical objects are objective and exist independently of a person’s mind.

Blue damselfish, seeming iridescent against a black background
The color of an animal can exert evolutionary pressure.
Paul Starosta/Stone via Getty Images

Likewise, we suggest that color plays an indispensable role in evolutionary biology. For example, researchers have argued that aposematism – the use of colors to signal a warning for predators – also benefits an animal’s ability to gather resources. Here, an animal’s coloration works directly to expand its food-gathering niche insofar as it informs potential predators that the animal is poisonous or venomous.

In fact, animals can exploit the fact that the same color pattern can be perceived differently by different perceivers. For instance, some damselfish have ultraviolet face patterns that help them be recognized by other members of their species and communicate with potential mates while remaining largely hidden to predators unable to perceive ultraviolet colors.

In sum, our ability to determine whether objects are colored the same or differently and the indispensable roles they play in science suggest that colors are as real and objective as length and temperature.The Conversation

Elay Shech, Professor of Philosophy, Auburn University and Michael Watkins, Professor of Philosophy, Auburn University

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‘Extraordinary claims require extraordinary evidence’ − an astronomer explains how much evidence scientists need to claim discoveries like extraterrestrial life

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theconversation.com – Chris Impey, University Distinguished Professor of Astronomy, University of Arizona – 2025-04-25 07:54:00

The universe is filled with countless galaxies, stars and planets. Astronomers may find life one day, but they will need extraordinary proof.
ESA/Euclid/Euclid Consortium/NASA, image processing by J.-C. Cuillandre (CEA Paris-Saclay), G. Anselmi

Chris Impey, University of Arizona

The detection of life beyond Earth would be one of the most profound discoveries in the history of science. The Milky Way galaxy alone hosts hundreds of millions of potentially habitable planets. Astronomers are using powerful space telescopes to look for molecular indicators of biology in the atmospheres of the most Earth-like of these planets.

But so far, no solid evidence of life has ever been found beyond the Earth. A paper published in April 2025 claimed to detect a signature of life in the atmosphere of the planet K2-18b. And while this discovery is intriguing, most astronomers – including the paper’s authors – aren’t ready to claim that it means extraterrestrial life exists. A detection of life would be a remarkable development.

The astronomer Carl Sagan used the phrase, “Extraordinary claims require extraordinary evidence,” in regard to searching for alien life. It conveys the idea that there should be a high bar for evidence to support a remarkable claim.

I’m an astronomer who has written a book about astrobiology. Over my career, I’ve seen some compelling scientific discoveries. But to reach this threshold of finding life beyond Earth, a result needs to fit several important criteria.

When is a result important and reliable?

There are three criteria for a scientific result to represent a true discovery and not be subject to uncertainty and doubt. How does the claim of life on K2-18b measure up?

First, the experiment needs to measure a meaningful and important quantity. Researchers observed K2-18b’s atmosphere with the James Webb Space Telescope and saw a spectral feature that they identified as dimethyl sulfide.

On Earth, dimethyl sulfide is associated with biology, in particular bacteria and plankton in the oceans. However, it can also arise by other means, so this single molecule is not conclusive proof of life.

Second, the detection needs to be strong. Every detector has some noise from the random motion of electrons. The signal should be strong enough to have a low probability of arising by chance from this noise.

The K2-18b detection has a significance of 3-sigma, which means it has a 0.3% probability of arising by chance.

That sounds low, but most scientists would consider that a weak detection. There are many molecules that could create a feature in the same spectral range.

The “gold standard” for scientific detection is 5-sigma, which means the probability of the finding happening by chance is less than 0.00006%. For example, physicists at CERN gathered data patiently for two years until they had a 5-sigma detection of the Higgs boson particle, leading to a Nobel Prize one year later in 2013.

YouTube video
The announcement of the discovery of the Higgs boson took decades from the time Peter Higgs first predicted the existence of the particle. Scientists, such as Joe Incandela shown here, waited until they’d reached that 5-sigma level to say, ‘I think we have it.’

Third, a result needs to be repeatable. Results are considered reliable when they’ve been repeated – ideally corroborated by other investigators or confirmed using a different instrument. For K2-18b, this might mean detecting other molecules that indicate biology, such as oxygen in the planet’s atmosphere. Without more and better data, most researchers are viewing the claim of life on K2-18b with skepticism.

Claims of life on Mars

In the past, some scientists have claimed to have found life much closer to home, on the planet Mars.

Over a century ago, retired Boston merchant turned astronomer Percival Lowell claimed that linear features he saw on the surface of Mars were canals, constructed by a dying civilization to transport water from the poles to the equator. Artificial waterways on Mars would certainly have been a major discovery, but this example failed the other two criteria: strong evidence and repeatability.

Lowell was misled by his visual observations, and he was engaging in wishful thinking. No other astronomers could confirm his findings.

An image of Mars in space
Mars, as taken by the OSIRIS instrument on the ESA Rosetta spacecraft during its February 2007 flyby of the planet and adjusted to show color.
ESA & MPS for OSIRIS Team MPS/UPD/LAM/IAA/RSSD/INTA/UPM/DASP/IDA, CC BY-SA

In 1996, NASA held a press conference where a team of scientists presented evidence for biology in the Martian meteorite ALH 84001. Their evidence included an evocative image that seemed to show microfossils in the meteorite.

However, scientists have come up with explanations for the meteorite’s unusual features that do not involve biology. That extraordinary claim has dissipated.

More recently, astronomers detected low levels of methane in the atmosphere of Mars. Like dimethyl sulfide and oxygen, methane on Earth is made primarily – but not exclusively – by life. Different spacecraft and rovers on the Martian surface have returned conflicting results, where a detection with one spacecraft was not confirmed by another.

The low level and variability of methane on Mars is still a mystery. And in the absence of definitive evidence that this very low level of methane has a biological origin, nobody is claiming definitive evidence of life on Mars.

Claims of advanced civilizations

Detecting microbial life on Mars or an exoplanet would be dramatic, but the discovery of extraterrestrial civilizations would be truly spectacular.

The search for extraterrestrial intelligence, or SETI, has been underway for 75 years. No messages have ever been received, but in 1977 a radio telescope in Ohio detected a strong signal that lasted only for a minute.

This signal was so unusual that an astronomer working at the telescope wrote “Wow!” on the printout, giving the signal its name. Unfortunately, nothing like it has since been detected from that region of the sky, so the Wow! Signal fails the test of repeatability.

An illustration of a long, thin rock flying through space.
‘Oumuamua is the first object passing through the solar system that astronomers have identified as having interstellar origins.
European Southern Observatory/M. Kornmesser

In 2017, a rocky, cigar-shaped object called ‘Oumuamua was the first known interstellar object to visit the solar system. ‘Oumuamua’s strange shape and trajectory led Harvard astronomer Avi Loeb to argue that it was an alien artifact. However, the object has already left the solar system, so there’s no chance for astronomers to observe it again. And some researchers have gathered evidence suggesting that it’s just a comet.

While many scientists think we aren’t alone, given the enormous amount of habitable real estate beyond Earth, no detection has cleared the threshold enunciated by Carl Sagan.

Claims about the universe

These same criteria apply to research about the entire universe. One particular concern in cosmology is the fact that, unlike the case of planets, there is only one universe to study.

A cautionary tale comes from attempts to show that the universe went through a period of extremely rapid expansion a fraction of a second after the Big Bang. Cosmologists call this event inflation, and it is invoked to explain why the universe is now smooth and flat.

In 2014, astronomers claimed to have found evidence for inflation in a subtle signal from microwaves left over after the Big Bang. Within a year, however, the team retracted the result because the signal had a mundane explanation: They had confused dust in our galaxy with a signature of inflation.

On the other hand, the discovery of the universe’s acceleration shows the success of the scientific method. In 1929, astronomer Edwin Hubble found that the universe was expanding. Then, in 1998, evidence emerged that this cosmic expansion is accelerating. Physicists were startled by this result.

Two research groups used supernovae to separately trace the expansion. In a friendly rivalry, they used different sets of supernovae but got the same result. Independent corroboration increased their confidence that the universe was accelerating. They called the force behind this accelerating expansion dark energy and received a Nobel Prize in 2011 for its discovery.

On scales large and small, astronomers try to set a high bar of evidence before claiming a discovery.The Conversation

Chris Impey, University Distinguished Professor of Astronomy, University of Arizona

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