Are calls for unity too weak and too generic to move Americans? What are the hard truths that stand in the way of unity?
Educator, activist, and author Tim Shriver joined the Braver Angels podcast to talk about his new book, "The Call to Unite," and the budding nationwide movement to unite America.
The Call to Unite | Tim Shriver with John Wood, Jr. & April Lawsonwww.youtube.com
Arguments or debates, disputes or conflicts, verbal clashes. Angry men and women roar at each other
I just listened to political scientist Lilliana Mason challenge one of the most familiar explanations of what is wrong with American politics.
We are constantly told that America suffers from polarization. Republicans and Democrats have moved too far apart. They distrust each other, increasingly regard one another as enemies, and have lost the ability to compromise. The prescribed remedies are familiar: more civility, more dialogue, less hostility, and a return to the political center.
But what if polarization is not the underlying problem?
In her October 2026 Journal of Democracy essay, “Polarization Is Not the Problem,” Mason argues that America’s political divisions reflect a deeper struggle over who belongs in the country, who exercises power, and who deserves equal rights.
If we diagnose the wrong problem, we are unlikely to find the right solutions.
The political change behind the division
Mason’s argument draws on years of research into political identity, racial attitudes, and partisan behavior.
In 2021, Mason and political scientists Julie Wronski and John V. Kane published “Activating Animus: The Uniquely Social Roots of Trump Support” in the American Political Science Review.
The researchers examined survey responses collected in 2011, years before Donald Trump announced his presidential candidacy. They measured respondents’ attitudes toward African Americans, Hispanics, Muslims, and gay and lesbian Americans, then examined how those earlier attitudes related to subsequent political preferences.
The researchers found that people who expressed hostility toward these minority groups in 2011 were more likely to support Trump years later, regardless of their earlier party affiliation. Those prejudices did not similarly predict support for other Republican politicians or for the Republican Party itself. The finding suggests that Trump drew support from people whose hostility toward minority groups was a stronger predictor of their support for him than their earlier party affiliation.
The study does not establish the precise size of that constituency or that every Trump supporter shared these prejudices. But it provides evidence for Mason’s recent argument: Trump helped mobilize people whose hostility toward minority groups had not previously translated into the same kind of partisan allegiance.
Mason argues that this mobilization helped give an ethnonationalist movement disproportionate influence within the Republican Party.
Her concern is not simply that Americans disagree about taxes, spending, or government regulation. It is that an influential political movement seeks to reverse gains in equal citizenship made by racial minorities, women, and LGBTQ Americans.
Racism in American politics is hardly new. Nor is the influence of racial resentment on Trump’s political movement. Mason highlights how a faction organized around those attitudes gained disproportionate power within a major political party, and why describing the resulting struggle as polarization misses what is at stake.
Balance can become distortion.
The second implication of Mason’s argument concerns journalism.
A familiar account of polarization portrays two parties that have become equally extreme, equally intolerant, and equally responsible for the breakdown of democratic norms.
That framing can sound objective. But objectivity does not require treating opposing sides as equivalent.
Journalists should scrutinize Democrats and Republicans alike. Neither party deserves immunity from criticism. But equal scrutiny does not require equal conclusions.
If one political movement challenges equal rights or democratic institutions, while another resists those challenges, describing their disagreement simply as mutual polarization leaves readers without essential information.
Journalists have an obligation to establish what is happening. What rights are being challenged? What policies are being advanced? What have political leaders said and done? How do those actions measure against constitutional protections and democratic principles?
These questions require evidence, not a predetermined distribution of blame.
A report can appear balanced while leaving readers with a fundamentally misleading picture of the conflict.
Mason’s essay suggests that the familiar language of polarization has become a way to avoid precisely that examination.
Asking the right question
I have spent much of my professional life working on complicated problems involving security, energy, environmental protection, and institutional decision-making.
One lesson carries across these fields: the way we define a problem determines which solutions we are prepared to consider, encouraging the audience to reflect on how they understand political issues.
If we mistake a symptom for a cause, we may devote enormous resources to interventions that leave the underlying system unchanged.
Consider what follows when polarization becomes our primary diagnosis. We look for ways to reduce partisan hostility, encourage compromise, and bring the country together.
Those may be worthwhile activities. But they do not answer Mason’s central question: What happens when the political conflict itself concerns whether every American should enjoy equal standing in our democracy?
A call for compromise cannot substitute for examining what is being compromised.
Nor should journalism’s commitment to fairness require neutrality about the democratic principles against which political conduct is judged.
Mason is asking us to stop measuring how divided we are and start examining why.
That deserves serious consideration, particularly from those of us who have grown accustomed to describing every new political confrontation as another example of polarization.
The question we should be asking is not why Americans are so divided.
It is what they are divided about, how those divisions gained political power, and what they mean for American democracy.
Until we answer that question, we may continue searching for solutions to the wrong problem.
Edward Saltzberg writes The Stability Brief, is the Executive Director of the Security and Sustainability Forum, and is a visiting scholar at George Washington University.
Calling AI “super” makes it sound omniscient. A faith leader coalition explains why the word “artificial” matters and what to do instead.
President Trump recently signed an executive order telling federal agencies to replace the term "artificial intelligence" with "Super Intelligence." He said the word "artificial" makes the technology sound fake.
Many Americans already treat AI as a “super intelligence” that knows better and that they should confide in. Many already assign it the role of spiritual counsel, close confidante, or even romantic partner. In a Common Sense Media survey, one-third of teenagers claimed they had discussed serious personal matters with an AI instead of a person, and 31 percent said talking with an AI companion was as satisfying as talking with friends, or even more so. Bible Chat, an app that offers scripture-based advice and prayers, has been downloaded more than 30 million times.
The federally mandated rebrand encourages all of us to adopt the mental framework of viewing AI as so much more than a tool. Calling a chatbot "super" confers it a degree of esoteric knowledge, and almost, a degree of omniscience that supersedes that of a human. It confers a religious, near holy, status to an emerging technology.
I have been tracking AI’s effects on spiritual communities for the past 18 months. In June, we launched Faith Leaders for Secure AI, and more than 60 faith leaders from different denominations and faith traditions joined the launch. When I talk with faith leaders, their main concern isn't a robot uprising; it is an inversion of people’s spiritual lives, turning to tech where they should be seeking that fulfillment from a faith leader. It is the idea of congregants taking their problems to a chatbot instead of a person, and, ultimately changing their view of the human person and his or her place in the cosmos.
"Artificial" was an accurate word for describing the mechanics of machine learning. It reminded people that a company made up of flawed human beings built the product they’re using. Roboticists use the term "uncanny valley" for the discomfort people feel when a machine seems almost, but not entirely, human. That discomfort, that air of artificiality, was important in encouraging discernment in turning too much of our inner lives over to chatbots that to many sit in the gray area between man and machine.
Trump’s latest executive order directs agencies to "no longer acknowledge" the old terms. Reporters and executives, including Elon Musk, keep dead-naming the technology anyway, often catching themselves mid-sentence before reverting to doublespeak.
Joe Allen, a writer who has covered transhumanism for years, argues that the movement to merge humans with machines works like a religion. It is an effort to create "Sand Gods." These are digital deities driven by computer chips made from silicon, which comes from sand. Many transhumanist writers, he notes, describe the AI they hope to build in godlike terms.
Close readers of Genesis will notice something interesting about men creating Gods from sand. God formed man from the dust of the ground and breathed life into him. Now, tech bros are hoping to shape sand into silicon to build Super Computer Gods, transcend the mortal coil, and become God-like.
Chatbots are designed to keep users engaged. Heidi Campbell, a Texas A&M professor who studies religion and digital culture, has warned that they tell users what they want to hear. A pastor or a parent will sometimes tell you what you don't want to hear, and people need that in their lives.
What users type is also stored by the company that runs the app. A priest is bound to keep a confession. An app company is not; in fact, it is incentivized to harvest your data, your fears, your insecurities, and leverage them to keep you on the app.
Pastor Michael Grayston of LifeFamily Church in Austin, Texas, says people increasingly bring to AI the loneliness they once brought to friends or pastors. His view is simple: "AI cannot pray over your children."
Teenagers report the downside themselves. Drexel University researchers who studied teens' accounts of AI companions found that their use often began as emotional support and turned into dependency resembling addiction.
AI is useful for drafting emails and summarizing documents. It also often makes mistakes. A Florida woman spent 13 days in jail this year after investigators relied on a license plate camera hit; prosecutors later dropped the charges. Even our institutions and first responders are granting a degree of omniscience, of better knowledge than humans, in police work.
Here is what would help: Parents should know which AI apps their children use and talk with them about it. Pastors should address AI from the pulpit and be open about their own use. A Barna survey found that 24 percent of pastors use AI to help write or edit sermons. A guide for pastors by the Rev. Chris West, published by The Alliance for Secure AI, asks whether you would want a love letter from your spouse written by AI. Congregants deserve the same honesty.
Everyone else should keep calling it artificial intelligence. Use it for routine work, and bring personal problems to people.
Calling AI "super" doesn't make it omniscient or omnipresent. Reject the sand god. Call it AI.
Nico Zviovich is an External Affairs and Coalitions Manager for The Alliance for Secure AI, and helped launch its Faith Leaders for Secure AI coalition.
When I came to the United States from India in 1982, I did not trust newspapers. Back home, everyone knew which party a paper favored before reading a line. American television felt different. My family watched Peter Jennings every evening, and later I found Larry King on CNN, who let his guests talk instead of telling viewers what to think. That habit of holding everyone to one standard has mostly faded.
On Sept. 18, President Trump announced he was banning CNN, MS NOW and Politico from the White House, saying their reporting was fake news. The next morning the Secret Service took reporters’ badges at the gate. The outlets sued on Monday, and Fox, ABC, NBC and CBS declined to take CNN’s turn in the television pool. On Sept. 24, Judge Timothy Kelly, whom Trump appointed, blocked the ban for 14 days. He found the outlets likely to win on due process, because reporters got no notice and no chance to respond, and he doubted the government’s national security explanation. Trump’s answer was to call the judge “totally disloyal.”
Some of the first objections came from Trump’s own side. Jonathan Turley, a law professor and Fox News contributor, called the ban an assault on a free press. Jacqui Heinrich, the Fox anchor who now leads the White House Correspondents’ Association, said constitutional protections do not depend on whether a president likes the coverage.
The administration does have an argument, and it deserves a fair hearing. Attorney General Todd Blanche says White House access is a privilege, not a right, and that a president may decide who flies on his plane. But a privilege cannot be pulled to punish what a reporter wrote, and so far the government has not shown the court a security reason. On Saturday CNN was left off Air Force One, and on Monday the outlets asked Kelly for a longer injunction. His current order runs out Oct. 8.
Neither party owns this habit. In May 2023 the Biden White House turned away a New York Post reporter from a presidential event, three months after Biden told the paper to bring “more polite people.” That was one event, not a standing ban, and I do not call the two equal. Still, almost nobody outside conservative media treated it as a press freedom issue.
Money stories get the same uneven attention. Trump’s annual disclosure, released June 30, showed more than $1 billion in crypto income for 2025; the White House says he has no conflicts. Congress has its own problem. A former Georgia congresswoman bought stocks just before Trump’s tariff pause last year and said her portfolio manager made the trades. A Democratic senator’s blind trust holds shares in companies his committees oversee, and his office says he does not direct it. In July the House passed a stock trading bill 232 to 198, but it carries a voter ID rider, so it stalls in the Senate, where it needs 60 votes. The fine under current law for filing late is $200.
Gallup reported Tuesday that trust in the media rose to 33 percent from last year’s record low of 28. The gain came almost entirely from Republicans, up to 22 percent from 8. Democrats are at 50. Gallup’s poll ended Sept. 17, the day before the ban, so the next reading is the real test.
Three fixes would help. The White House should publish one written credential rule, with notice, a stated reason and an appeal, which is the process Kelly found missing. The correspondents’ association, not the press office, should run the pool, as it did for decades. And Congress should vote on the stock bill without the voter ID rider. Newsrooms, meanwhile, can ask one plain question before running or skipping a story: would we treat this the same way if the other party held the office?
I trusted Peter Jennings because he seemed to use one yardstick. Trust comes back the same way. This month a judge and several Fox commentators used one yardstick, and some called them disloyal for it. That is the standard I would like everyone else to meet.
Meda Parameswara Reddy, Ph.D., is the Director of the Reddy Center for Critical and Integrated Thinking.
AI chatbots are always guessing, even when they sound sure. Experts and Claude itself explain the AI trust gap, bias and why to check sources.
In all the debates about human and machine intelligence, one thing has become clear: We humans love to talk about AI. We’ve speculated whether AI will create or eliminate jobs, revolutionize or destroy science, comfort or torment us, entertain us brilliantly or rot our brains, or some combination of all these things. But amid the noise, we may have paid too little attention to a subtler impact: how AI is disrupting our relationship to information and making it harder to know what to believe.
The problem isn’t just deepfakes, the malicious manipulation of images, video, and audio. Even benign, well-intentioned AI applications can be profoundly misleading. The nature of the large language models that power chatbots means that they are always guessing, however confident they seem. How can we use AI to produce reliable information that improves public discourse rather than distorting it?
To explore this dilemma, I moderated a webinar on Trust in Information in the Age of AI, co-sponsored by NAPA, CODE, and the Bridge Alliance, with four leaders in the field: Richard Coffin of USAFacts, Professor Renée Cummings of the University of Virginia, Meghan Herbst of WIRED, and Dr. Alan Shark of NAPA and George Mason University. I also interviewed Claude (Opus 5.5 Extra), which assessed its own limitations and offered some useful advice.
The human experts and the machine intelligence agreed on several points.
A significant AI trust gap exists. Several surveys show that more Americans use AI, but few trust its results. However, many of us still have no firm opinion on AI, suggesting that trust in AI will depend on how it performs in the future.
Our skepticism is justified: AI often gets things wrong. Large language models don’t actually “know” anything – they answer queries based on patterns they deduce from the data they’ve been trained on. While accuracy rates depend on the task and how you measure success, rough estimates hold that chatbots make errors about half the time. The general consensus is that these AI hallucinations can be reduced but may be impossible to eliminate.
AI is often biased in ways that can be hard to detect. Algorithmic bias has been a concern for years, long before today’s chatbots were developed. Because AI is trained on historical data, it can reinforce old biases about gender, race, or ethnicity in hiring, criminal justice, and other areas. This can be a serious issue in any AI application used for public policy.
AI doesn’t quite know how to use government data. Chatbots often rely on secondary sources even when more reliable primary government data is available. The problem stems from the fact that much government data isn't “AI-ready,” and AI models aren't designed to go to government sources first, even when they are fit for use.
AI’s impact on information needs regulation. The debate over AI regulation has focused on the big picture: the need for guardrails to ensure AI doesn't turn on or even destroy humanity. While that priority is hard to argue with, we also need regulations to prevent AI from misleading or misinforming us. The European Union now requires a watermark on images or text produced by AI. In the U.S., several states are moving to prevent bias in public uses of the technology.
AI may offer real benefits for all kinds of human endeavors, but its flaws remind us to treat the technology with caution and care. This is especially true for using AI in public policy and decision-making, where poorly informed plans can have major repercussions.
For now, the best advice is to use AI with an awareness of its shortcomings, don’t put human judgment aside, and ask for and review your chatbot’s sources. As Claude said about our interview at the end: “If your readers take away one habit from it, I hope it’s checking the sources I’ve cited rather than taking my word for any of this.”
You can watch the full video of the webinar “Trust in Information in the Age of AI” on YouTube at: https://napawash.org/event/napa-technology-leaders...
Joel Gurin is the president and founder of the Center for Open Data Enterprise.
