Samuel is an artificial intelligence (AI) scientist and professor, leading the Master of Public Informatics programs at Bloustein, Rutgers University. Samuel is also a member of the Scholars Strategy Network.
As artificial intelligence (AI) technologies cross over a vital threshold of competitiveness with human intelligence, it is necessary to properly frame critical questions in the service of shaping policy and governance while sustaining human values and identity. Given AI’s vast socioeconomic implications, government actors and technology creators must proactively address the unique and emerging ethical concerns that are inherent to AI’s many uses.
Open Source versus Black Box AI Technologies
AI can be viewed as an adaptive “set of technologies that mimic the functions and expressions of human intelligence, specifically cognition, and logic.” In the AI field, foundation models (FMs) are more or less what they sound like: large, complex models that have been trained on vast quantities of digital general information that may then be adapted for more specific uses.
Two notable features of foundation models include a propensity to gain new and often unexpected capabilities as they increase in scale (“emergence”), and a growing predisposition to serve as a common “intelligence base” for differing specialized functions and AI applications (“homogenization”). Large language models (LLMs) that power applications like ChatGPT are foundation models with a focus on modeling human language, knowledge, and logic. Advanced AIs and foundation models have the potential to replace multiple task-specific or narrow AIs due to their scale and flexibility, which increases the risk of a few powerful persons or entities who control these advanced AIs gaining extraordinary socioeconomic power, creating conditions for mass exploitation and abuse.
ChatGPT and other large language model applications, that are growing in popularity, use a nontransparent “black box” approach; users of these technologies have little to no access to the inner workings or underlying AI models and can only observe outputs (such as an essay) that result from data inputs (such as a written or spoken prompt) to judge how these applications function. Such opaque foundation models have widespread future AI application potential, displaying homogenization where the base models can be adapted to serve a range of specialized purposes. These exponentials are the risks inherent in a system that allows for private control of advanced AIs. Open-source initiatives, on the other hand, prioritize transparency and public availability of the AI models, including code, process, relevant data, and documentation, so that users and society at large have an opportunity to understand how these technologies function.
For human society, the spirit of the open-source movement is one of the most valuable forces at play in the AI technologies arena. Research and development of AI ethics must emphasize the contributions of open data, open-source software, open knowledge, and responsible AI movements, and contrast these with the challenges presented by relatively opaque AI applications. Closely coupled with open-source, the open-data movement (which refers to “data that is made freely available for open consumption, at no direct cost to the public, which can be efficiently located, filtered, downloaded, processed, shared, and reused without any significant restrictions on associated derivatives, use, and reuse”) can be a significant contributor to the development of responsible AI. Open-source initiatives distribute power and reduce the likelihood of centralized control and abuse by a few AI owners with concentrated power.
Questioning AI Ethics to Inform Regulatory Processes
Drawing from the open-source movement’s prioritization of transparency, decentralization, fairness, liberty, and empowerment to the people, responses to the following five questions should be required of all companies creating opaque AI applications such as ChatGPT.
Is it fair to use an opaque black-box approach for AI technologies when the implications and impacts of complex AI technologies posit many significant risks? The consequences of AIs are great and must be associated with proportionately higher levels of accountability. The volatile impacts of AIs are expected to be exponentially greater over time than past technologies. Therefore, for human society, it would be relatively less risky in the long run for companies to embrace the open-source approach which has already demonstrated the critical value of transparency and open availability of source materials.
If building upon valuable, free, good-faith open-source research, is it then morally correct to build opaque black boxes for private profit? Significant open-source contributions, made in good faith, have laid the foundations for the present-day AIs. Many of the technological modules, such as transformers, used within applications like ChatGPT came from open-source research. Having reaped the benefits of open-source, privatization of critical AI models blocks societal innovation opportunities and even if presently legally permissible, should be considered ethically wrong.
Why should for-profit companies be allowed to deprive people of their right to know all specific details about what data an AI (that they are expected to use, compete against, and perhaps even be subject to in the future) has been developed with? Large language models are trained on vast quantities of data—in the interests of public benefit and transparency, all data “ingredients” used for training must be detailed in a testable manner. If the specific texts on which GPT3,4 /ChatGPT have been trained remain largely undisclosed, it would be difficult for the public or governments to audit for the fair use of data; to gauge if restricted, protected, private or confidential data have been used; and to see if a company has added synthetic data or performed other manipulations causing the AI to present biased views on critical topics. All training data must be declared in real-time.
Why should companies not be held responsible for transparency and be compelled to demonstrate the absence of deliberate bias mechanisms and output-manipulating systems within their applications? Combining complex, risk-inducing, hidden AI technologies with the opacity of data use gives “emergence” to the potential rise and spread of manipulative AIs. Companies must be required to show that their AIs provide a fair and unbiased representation of information, and they must be held responsible to prove in real-time that they are indeed reflecting “facts as they are.” The absence of protections for the public will potentially facilitate mass manipulation of users—consider a future filled with powerful mind-bending manipulative AIs under the control of a few of the self-declared elites.
Why should for-profit AIs not be regulated by enhanced AI-appropriate laws and policies for consumer protection? ChatGPT Plus was released at $20 per month for privileged access in February of 2023. For a company that started as a “nonprofit to develop AI” with a self-declared purpose to “ benefit humanity as a whole,” this rush to monetization can appear opportunistic and shortsighted. When an AI is presented as a commercial product, the company profiting from this product should be held liable for full transparency and all the output the AI produces, and companies should not be allowed to actively or passively coerce users (again) into signing away all their fair rights. Governments must break the habit of acting only after powerful companies and wealthy investors have ensured positive returns on investment, and companies must be challenged to develop profit models which accommodate full transparency of methods and data.
Fear of abuse or global security concerns are feeble excuses for hiding general-purpose scientific discoveries, building opacity, and creating black boxes. Instead of waiting for harm to occur, governments must be proactive; for example, if large language model applications had been covered by proactive AI regulations before ChatGPT’s launch, we would be able to use the technology more confidently and increase productivity with informed concerns on bias, limitations, and manipulation. Monetization and positive return on investment are important and it is necessary to have sustainable business models with some level of opacity of final production systems. However, given the nature and power of AI technologies, utmost care must be taken to provide open availability of AI foundation models and training data and to ensure transparency, fairness, accountability, application of open-source principles, and adoption of responsible AI practices.
Supporting the integration of open data and AI, along with proactive policies built on the principles of the open-source movement, will serve as a sustainable value-creation strategy to ensure that the benefits of AI are truly disbursed equitably to all people. Using a framework derived from the open-source movement will ensure an optimal measure of public power over artificial intelligence, and lead to a much-needed improvement in accountability and responsible behavior by companies and governments who “own” these technologies.
image of U.S. President Donald Trump is displayed on a digital billboard in Times Square in New York on April 8, 2026.
Trump is stuck between two realities. Neither serves the American people
Normally, I worry that events may overtake a column. But not so with the Iran war.
I don’t worry about running afoul of a headline or Truth Social post from the president because what is said about the situation is no longer very relevant to the reality.
On April 8, Nick Catoggio, my Dispatch colleague, dubbed an earlier stoppage with Iran “Schrödinger’s ceasefire.” This was a reference to the famous thought experiment by the physicist Erwin Schrödinger, who was trying to explain the weirdness of “superpositionality” in quantum physics. A cat in a box is both dead and alive at the same time until you open the box. Schrödinger meant to illustrate the absurdity of the idea that particles aren’t any one thing, but a “cloud of probabilities.”
The Trump administration is stuck in a word cloud of probabilities of his own making. The war is over. The war is on. The war isn’t a war. We have a deal, but we don’t have a deal, but we’re about to have a deal. We destroyed Iran’s military. No, we left it intact. We want regime change. No we don’t. We already accomplished it. We “obliterated” Iran’s nuclear program a year ago. We had to go to war in February to prevent nuclear war. The Strait of Hormuz is open, closed, or something in-between. No deal without “unconditional surrender.” Let’s make a deal!
This everything-all-at-once vibe can be disorienting, particularly since most Americans didn’t have a war with Iran on their bingo cards until the shooting had already started. President Trump didn’t prepare the country or consult with Congress beforehand because he thought it would all be a smashing success in a matter of weeks.
The miscalculation that started it all: killing Iran’s Supreme Leader, Ayatollah Ali Khamenei, and much of Iran’s senior leadership, on the first day of the war. To “the great proud people of Iran, I say tonight that the hour of your freedom is at hand,” Trump announced on Feb. 28. “When we are finished, take over your government. It will be yours to take. This will be probably your only chance for generations.”
I support regime change in Iran and shed no tears for Khamenei or his goons. But when you start a war by killing the regime’s top leaders, it’s not unreasonable for the remaining ones to conclude that you really intend regime change.
Khamenei was a murderous fanatic, but he was a fairly cautious one. He liked to threaten closing the Strait of Hormuz or attacking our regional allies, but he was reluctant to actually do it, fearing it would invite a regime change war. The mullahs and IRGC goons believed, not unreasonably, that if they lost their grip on power, they’d be lynched by the Iranian people they’ve brutalized for decades.
By starting with a regime change war, Trump removed any reason for the regime not to go for broke. When you have nothing to lose — particularly when you are a millenarian religious fanatic — a Persian Alamo strategy makes a lot of sense.
So Iran closed the Strait of Hormuz and attacked its neighbors.
But it turns out this wasn’t the Alamo. In the contest of wills, Trump blinked. The Iranian regime’s tolerance for punishment proved — so far — to be greater than Trump’s and that of our gulf allies. Militarily we could finish the job, but that would require ground troops and much greater economic turmoil. In a conflict Trump launched unilaterally without the prior support of Congress, NATO or the American people, Trump doesn’t have the political capital for that.
But that’s only half the problem. Trump wants the war over, but he doesn’t want to pay — militarily, economically, politically — what that would cost. So he wants to make a deal that ends it. But there is no deal available that wouldn’t come at an equally undesirable cost. Any deal that looks like what President Obama struck with the Iranians would be too embarrassing to bear. But the Iranians are convinced that they can get just such a deal, and they’re willing to drag things out as long as it takes.
The result: Trump’s in a box of his own making. He thinks he can talk his way out by simply asserting a reality that doesn’t exist. When the financial markets get nervous, he announces a breakthrough that is, at best, a possibility. When the Iranians agree to a deal that looks similar to one Obama might negotiate, Trump goes back to his threats.
It can’t go on forever. But I’m sure it’ll last until long after this column is forgotten.
Jonah Goldberg is editor-in-chief of The Dispatch and the host of The Remnant podcast. His Twitter handle is @JonahDispatch.