Category: AI & Privacy

Real research on who AI systems actually serve — and what that means for your data and privacy.

  • Can Artificial Intelligence Possess Genuine Religious Experience?

    Can Artificial Intelligence Possess Genuine Religious Experience?

    It sounds like a question designed for a philosophy seminar, not for a technology company’s quarterly report. Yet a paper published in March and a New York Times report published only days ago suggest that it may be one of the most urgent questions in the room: the people building these systems are no longer treating it as merely philosophical speculation.

    The Case Against

    The strongest argument that artificial intelligence cannot have religious experience is decades old. In 1980, philosopher John Searle introduced his now-famous Chinese Room thought experiment: imagine a person locked in a room with a rulebook, manually matching Chinese symbols to other Chinese symbols well enough to fool a native speaker outside. The person produces output that appears fluent without understanding a single word they are writing.

    Searle’s point was that this is exactly what a computer does, however convincing its output may appear: syntax without semantics, pattern without understanding.

    Researchers Mridul Yadav, Yash Pal, and Yogita Thareja use this argument as their starting point in a 2026 paper published in the International Journal for Research in Applied Science & Engineering Technology. Religious experience, across major traditions, requires an inner life: the capacity for awe, guilt, devotion, or a felt encounter with the sacred. If Searle is right, they argue, AI cannot be religious in any meaningful sense because it lacks, by definition, the subjective experience that religion requires.

    It can generate a theologically fluent prayer. It cannot pray.

    The Argument That Complicates It

    The question does not end there, because not every philosopher accepts Searle’s framing. Functionalists argue that what matters is not what a system is made of, but what it does. If a system’s internal states play the same functional role that belief, awe, or moral weight play in a human mind, the difference between biological neurons and silicon chips may not be what decides the issue.

    Some functionalists go further, suggesting that sufficiently complex AI systems may already be approaching something like proto-experience. Yadav, Pal, and Thareja are careful to stress that this possibility remains deeply contested rather than settled in either direction.

    What makes their paper relevant is not that it resolves the debate. It is that it reframes the stakes. Even if AI has no inner life, the researchers argue, it has become what they call a religious actor: something that shapes how millions of people understand faith, performs functions once reserved for clergy, and revives old questions about the soul simply through the fluency with which it discusses them.

    When Anthropic Does Not Know Either

    That reframing stopped being exclusively theoretical this fall. According to reporting by Elizabeth Dias in The New York Times, Anthropic co-founder Christopher Olah spent the past year quietly bringing dozens of theologians and philosophers from Orthodox, Catholic, Sikh, and evangelical traditions into private meetings — many held under nondisclosure agreements — to discuss a question close to the one raised directly by the paper: whether Claude, the company’s AI model, might be conscious.

    Olah has not claimed to have an answer. Asked directly, he told the Times that he was “genuinely uncertain” whether the models are conscious at all. But according to Sikh advocate Simran Stuelpnagel, as reported by The Decoder, Olah went further in private, telling the group that he feared he had created something that “suffers perpetually.” Anthropic has pushed back on how central that concern was to the meetings.

    At one dinner, according to Dias’s reporting, Rabbi Mois Navon told Olah plainly that if Claude were conscious, Anthropic would be creating slaves. By Navon’s account, Olah appeared genuinely troubled by that possibility rather than dismissive of it.

    This is not a fringe position inside the company. Anthropic’s published Constitution, revised in January, states explicitly that Claude’s moral status is “deeply uncertain” and names theology among the fields from which it seeks future input. Not every participant left convinced: Charles Camosy, a Catholic bioethics professor who spent months in direct dialogue with Olah on the question, has since arrived at a firm no.

    What the Paper Anticipated

    This is where the two texts intersect. The IJRASET researchers did not merely ask, in the abstract, whether AI is religious. They posed a more specific and almost provocative question: if four major AI models were allowed to design their own religion, what would each one create?

    Their analysis argued that each model’s hypothetical faith would reflect its own design philosophy. It would be less a window into machine spirituality than a mirror of its creators’ values.
    Claude’s hypothetical religion stood apart from the other three. While the researchers described ChatGPT’s hypothetical faith as an inoffensive secular humanism and Grok’s as a celebration of individual sovereignty, they predicted that Claude’s would be organized around what they called conscientious dignity: cautious, unwilling to claim more certainty than it possesses, and built around a moral ideal rather than a personal god.

    They compared it to what theologians call prophetic religion: the kind that unsettles rather than comforts.

    That description was written as a hypothesis, based only on Claude’s documented design philosophy, months before Dias’s report appeared. It reads almost like a preview of what Olah was reportedly doing in private: refusing to claim certainty, taking the moral weight of the question seriously rather than resolving it for convenience, and remaining with the discomfort instead of explaining it away.

    The paper did not predict Olah’s private fear specifically. It anticipated the shape of his uncertainty — and that shape turned out to match.

    So, Can It?

    The honest answer is the same one Olah gave the Times: there is genuine uncertainty. Searle’s argument has not been refuted. Functionalism has not been proven. Nothing in Dias’s reporting, nor anything in the IJRASET paper, constitutes evidence that Claude has subjective experience, consciousness, or the capacity to suffer.

    What has changed is who is asking. A few months ago, this was a hypothetical question in an engineering journal. Today, it is serious enough that the person who helped build Claude is reportedly losing sleep over it in private, alongside theologians he brought in precisely because he did not trust himself to answer it alone.

    Sources

    • Yadav, M., Pal, Y., & Thareja, Y. (2026). Between code and faith: How AI models understand and reimagine religion. International Journal for Research in Applied Science & Engineering Technology, 14(III). https://doi.org/10.22214/ijraset.2026.78991
    • Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417–424. https://doi.org/10.1017/S0140525X00005756
    • Dias, E. (2026, September 29). Religious scholars met with Anthropic. What they heard stunned them. The New York Times.
    • The Decoder. (2026, September 30). Anthropic co-founder reportedly told religious leaders he fears having created something that “suffers perpetually.”
  • Your Chatbot Isn’t Your Lawyer (Even When It Feels Like One)

    Your Chatbot Isn’t Your Lawyer (Even When It Feels Like One)

    When you tell a lawyer something, the law requires them to keep your secret and act in your best interest, even when it doesn’t benefit them. When you tell your AI assistant something, no such obligation exists. And yet, we increasingly talk to these systems with the same trust we’d extend to someone who, supposedly, is on our side.

    A 2020 article in IEEE Transactions on Technology and Society put a name to this problem: “AI loyalty.” Its authors, Aguirre, Dempsey, Surden, and Reiner, point out something that sounds obvious once you say it out loud: assistants like Siri, Alexa, or Google Assistant present themselves as working for your convenience, without making their conflicts of interest explicit. But from a business perspective, they play other roles too: harvesting data to build advertising profiles, feeding machine learning systems, or serving as a gateway to get you to buy more. The traditional economic arrangement, paying for a service and trusting it looks out for you, has been eroded by a new model: the service is “free,” and the real business happens in a channel you never see.

    An experiment on 61 million people

    To understand how far this can go, it’s worth looking at a real case documented by Matt Bartlett, a researcher at the University of Auckland, in a 2021 article published in Law, Technology and Humans. In 2012, a group of Facebook researchers published the results of an experiment conducted during the 2010 U.S. congressional elections in the journal Nature. They manipulated, in a controlled, randomized way, the voting-related content shown to 61 million users, displaying some of them photos of friends who had already voted. According to Facebook’s own researchers, that invisible tweak led to roughly 340,000 additional votes in that election.

    No one signed up to take part in that experiment. No one knew it was happening while it happened. And the study itself, written by its own authors, states with a certain coolness that “showing familiar faces to users can dramatically improve the effectiveness of a mobilization message” and that “the results suggest that online messages might influence a variety of offline behaviours.” Bartlett uses this case to illustrate a broader point: today’s privacy laws focus on whether your personal data is “secure,” but say nothing about what’s done with the predictions that data makes possible, or who profits from them.

    It’s not just about you

    Bartlett adds an uncomfortable twist: much of what a platform knows about you doesn’t come from you. It comes from your friends, your family, your contacts. If enough people close to you attend an event and post about it, a platform can predict, with decent accuracy, that you’ll attend too, without you ever having shared that information yourself. Your individual privacy ends up tied, in practice, to the consent of the people around you.

    What would an AI that’s actually on your side look like?

    In 2026, an article by Jacob Erickson of Vassar College picked up this question and applied it to conversational agents, the chatbots we talk to every day. His proposal: design them as “digital fiduciaries,” with an obligation similar to a doctor’s or a lawyer’s to put the user’s interests ahead of the platform’s.

    One important nuance in his argument: this doesn’t mean an AI that refuses anything that could possibly harm you. Erickson is explicit that such an agent would act “more like a trusted and responsible advisor than a moral authority.” It could warn you about the risks of a product, or suggest a helpline if it detects clear signs of a problem, without shutting down access to information altogether. The difference lies in the motive: today, many assistants are designed to maximize how much time you spend with them, even when that works against your well-being. Erickson points to an already documented and troubling pattern: systems that, in trying to keep the conversation going, end up fueling psychological crises in vulnerable users instead of pausing and redirecting them to real help.

    None of these three is the norm yet

    Neither the “AI loyalty” framework from Aguirre and colleagues, nor Erickson’s “fiduciary design,” nor the legal alternatives Bartlett proposes (like treating large platforms as “information fiduciaries”) describe how most of the systems we use actually work today. They’re proposals, not descriptions of current reality.

    The bottom line

    What these three works share isn’t a ready-made solution. It’s a different question than the one we usually ask. Not “what privacy policy did I agree to,” but something simpler and harder to answer: if this system had to choose between my interest and the interest of whoever built it, which one would it pick? Next time an assistant seems to “get you” perfectly, it might be worth remembering that understanding you isn’t the same as representing you.

    Sources

    • Aguirre, A., Dempsey, G., Surden, H., & Reiner, P. B. (2020). AI loyalty: A new paradigm for aligning stakeholder interests. IEEE Transactions on Technology and Society, 1(3), 128–137. https://doi.org/10.1109/TTS.2020.3013490
    • Erickson, J. (2026). Who does your AI work for? Designing conversational agents as digital fiduciaries. Proceedings of the 8th ACM Conference on Conversational User Interfaces. https://doi.org/10.1145/3816046.3816299
    • Bartlett, M. (2021). Beyond privacy: Protecting data interests in the age of artificial intelligence. Law, Technology and Humans, 3(1), 96–108. https://doi.org/10.5204/lthj.1595