Social media takes a lot of heat for spreading disinformation, but Francesca Tripodi thinks we should look more closely at another fixture of our browsers: search engines. On this episode of "Civic Genius" we learn more about how the same forces that shape our social media diets also drive the information we find when we search online, and how we can boost our own media literacy.
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.
Universal basic income would cost trillions. Wage insurance, a bigger child tax credit, and a tax break for work could help workers left behind by AI at a fraction of the price.
The age of AI will be a revolutionary age. In medicine, engineering, and more, there is the possibility of great benefit to humanity. Consider some Wall Street Journal headlines: “The Quest to Use AI to Help Find New Drugs,” and “Fusion Startup Gets an AI Boost.” Already, AI is a substantial portion of GDP growth.
Yet a rising tide will not lift the boats with holes in their hulls, as Jack Kemp once said. Artificial intelligence is challenging our leaders to think about those possibly left behind. Within the AI community itself, Anthropic CEO Dario Amodei expresses concern that we could see 5-10% GDP growth with 10% unemployment.
We cannot allow this concern about AI’s human costs to go totally unaddressed. “Pull yourself up by only your own bootstraps” is not an appropriate response when there is so much disruption throughout the entire economy—when the owners of capital will thrive as workers and would-be workers struggle. Ideas from both political parties are necessary.
A universal basic income is one possibility being pushed, but the cost may be prohibitive. A decent-sized UBI of $12,000 a year would cost trillions annually. A more realistic approach is needed.
There are two alternate proposals that have not passed Congress in the past, but they could be brought back up for consideration. First is the expansion of a concept called “wage insurance.” The second is an expanded child tax credit. I will contribute a third idea too.
Wage insurance was part of the Trade Adjustment Act’s safety net. A worker 50 or older who lost a job due to foreign trade qualified if the worker found another job paying less (but no more than $50,000). The benefit was half the difference in pay between the old and new jobs, up to $10,000 over two years.
The Obama administration proposed eliminating the trade-impacted requirement. Anyone over age 50 would qualify for wage insurance so long as they were in their previous job for at least three years. The cost was estimated at $3 billion a year. Unlike a universal basic income, Congress could find the savings to offset this spending.
Wage insurance directly addresses the immediate costs of an AI economy. The worker whose career is disrupted by what was only, until recently, science fiction finds a benefit other than only unemployment insurance. The worker may very well never have the old income level again and is approaching retirement. Help for such a person in the throes of capitalism’s “creative destruction” makes sense. We are addressing those in the spaces between what was and what is becoming.
I mentioned an expanded child tax credit as well. Former Sen. Mitt Romney proposed a kind of refundable and advanceable child tax credit—meaning paid even to those who do not owe taxes, and paid monthly before even filing a tax return—of $4,200 per child under age six and $3,000 for other children. This contrasts with the $2,200 per child, with less refundability, under President Donald Trump’s “one big beautiful bill.”
Sen. Romney proposed paying for the cost partly through tax reforms that largely, but not entirely, shift the tax code’s other credits and deductions in a more progressive direction. The “mostly” is why anti-poverty advocates seemed to give the bill two cheers instead of three. Overall, the proposal would net reduce childhood poverty but would cause some families lifted out of poverty to slip right back in.
Still, the Romney proposal could be a countervailing force against AI’s corrosive effects. Even more so if the bill’s pay-for is tipped in an even more progressive direction. We would have a kind of modest universal basic income for families with children. We would be addressing those of greatest need in the economy.
The third suggestion is this: Provide a tax break for work equivalent to the size of what we give to capital. The lower income tax rate for dividend income costs the federal government a little over $40 billion a year. Putting similarly-sized tax relief into workers’ pockets would produce an average tax savings of about $235 per worker.
An extra $235, or for a two-earner family, $470, is not a universal basic income, but it is affordable. The more progressive could very well connect the revenue that can be raised by eliminating the dividend preference to offsetting the cost of lowering the 10% bottom income tax rate, or some other way to put the money in pockets. A more ideologically conservative approach would seek the curtailment or elimination of tax breaks and government programs.
What is important is recognizing that the owners of capital will be great beneficiaries of the AI age. As a matter of public policy, we should look out for labor too. Patch the boat’s holes and let the tide rise. The human cost of not doing anything needs to be addressed along with the fiscal cost of doing something.
Scott Miller is a graduate of Widener School of Law, a former chief of staff in Congress, and the author of 'Christianity & Your Neighbor's Liberty.
