AI is changing what we can do. Who we become is still our choice

Technologies that extend human powers may also, by degrees, erode the faculties they are meant to assist. Physicians who use AI to help detect adenomas during colonoscopies may become less adept at spotting anomalies on their own. They start to lose a skill. Yet if AI is always available, what really matters, surely, is whether doctors using AI do better than doctors did before AI arrived. And, in a range of cases, it seems that they do.^[1]

New technologies often change the skills a job requires. The grizzled mechanic who could diagnose your old jalopy by ear had a valuable expertise, but that expertise lost value once computers began interpreting the data produced by modern vehicles. So, you might think that we should care only about whether the combination of human beings and technology leads to better results, even when that combination attenuates human skills.

More than a century and a half ago, John Stuart Mill argued that this view was incomplete. In On Liberty, published in 1859, he wrote:

It really is of importance, not only what men do, but also what manner of men they are that do it. Among the works of man, which human life is rightly employed in perfecting and beautifying, the first in importance surely is man himself. Supposing it were possible to get houses built, corn grown, battles fought, causes tried and even churches erected and prayers said, by machinery—by automatons in human form—it would be a considerable loss to exchange for these automatons even the men and women who at present inhabit the more civilized parts of the world, and who assuredly are but starved specimens of what nature can and will produce.^[2]

For Mill, what people do may, in some cases, matter less than “what manner of men” they become in doing it. The route by which we arrive somewhere can shape us, and that shaping may itself be part of what matters. That’s because Mill espoused a broader ideal in which autonomy, individuality and the active cultivation of one’s faculties are constituents of a good human life, not merely useful tools for achieving it. A technology that alters how we think, judge, attend or feel may leave us diminished even when it improves performance.

Mill could not have had large language models in mind, of course. But he did imagine a world in which machines “fight battles” and “try causes”, a prospect that now sounds strikingly contemporary. In such a world, human beings would turn over not only labor but decision-making. We would no longer be shaped by doing difficult things and by making consequential decisions. That, Mill says with characteristic restraint, would be a “considerable loss.”

This concern had personal resonance for him. Mill had been subjected to one of the most rigorous educations in modern history. Raised by his father, political economist James Mill, under the influence of Jeremy Bentham, the founder of modern utilitarianism, he was drilled from childhood in Greek, Latin, logic, history, political economy and philosophy. He became a savant, but he also came to fear that the process had made him mechanical, a kind of reasoning machine rather than a fully developed person. His account of a mental breakdown he experienced at the age of 20 suggests that he had been trained to think and perform, but not to feel or choose freely. When he later warned against reducing human beings to “automatons”, he was referring to a prospect that had long weighed on him.

Mill was one of the most formidable thinkers in Victorian Britain. He wrote major works on democracy, logic, political economy, utilitarianism and ethics. He kept up with the science of his day, served in parliament, and, as Bertrand Russell later remarked, “combined intellectual distinction with a very admirable character.”^[3] Yet he always remained alert to the possibility that efficiency can come at the cost of humanity. By our lights, he got many things right and some things wrong. Was he right in his criticism of automata?

Dealing with the automated oracle

Let’s step back and ask one of the central questions of ethics: “What kind of people should we want to be?” Ethics, in the classical sense, is about how to live well. And that involves more than producing the right outward acts. It involves becoming the sort of person who can recognize what is right, choose it for the right reasons and respond to the world with the appropriate thoughts and feelings.

How, then, could an automated oracle help? It cannot tell you what to feel, because feeling is not something you can summon by obedience. But neither can it settle the matter by telling you what to do. Reasons matter, and to be a morally responsible agent you must reason for yourself. That thought is central to the ethical tradition that reaches back through the European Enlightenment and to Immanuel Kant. Central to Kant’s thought was the ideal of people fully in charge of their own lives, reasoning toward the right decisions through a kind of self-government he called autonomy. People who simply do what they are told, even when what they are told to do is right, are not living autonomous lives. Nor is this a uniquely European idea. Confucius taught the virtue of yi, which involves recognizing what is right and acting in harmony with moral principle. Buddhism offers similar lessons. What matters is not just what you do but the intention with which you do it.

Accordingly, philosophers have rightly been suspicious of “moral deference”, in which someone decides what to do based on what another person or institution declares to be right. Moral guides, your priest, your rabbi, your imam, your guru, even a humble philosopher, can help you think things through. They can draw your attention to features of a situation you may have overlooked. But if they are doing their job, they will not simply listen to your quandary and pronounce a course of action without giving reasons. They will want you to do what is right because you understand why it is right, because only acts that arise from your own deliberation are fully yours. If they espouse values you do not recognize, your compliance does not turn their judgment into your own.

I should add that I am not assuming values are matters of mere belief rather than knowledge, or endorsing relativism, the notion that different normative traditions are entitled to their different answers. You can reject moral deference and affirm autonomy while still believing that there is such a thing as moral truth, and even moral expertise. Perhaps many moral questions, perhaps even all of them, have a universally correct answer. It remains the case that, even if LLMs give excellent answers to moral questions, you still should not defer to their conclusions. You should try to understand the reasons they offer, because it remains important that people act on the basis of their own, admittedly imperfect, understanding. That, in my view, is itself one of the universal moral truths.

The hidden assumptions of AI

That moral deference is inconsistent with autonomy does not mean we should resist taking advice, even from an automaton. We always have. We turn to friends and family, to churches, mosques and synagogues, to newspapers, magazines, radio and television, and now to platforms, from Reddit to TikTok to Substack. It’s just that, as with those other sources of guidance, two things can go wrong when you ask an LLM.

First, the people who run an AI chatbot could slant it, steering you in one direction without your knowing it. That threatens your autonomy because it is a form of manipulation. If someone draws your attention only to arguments on one side, even if it is the side you were already inclined to take, they have not enriched your thinking, which is what advisers are for. Your aim should be to make the best choice in light of what you take to be the right values and a realistic understanding of your situation. You want, in short, to be guided by what, on reflection, you would judge to be the right reasons. Someone, or something, that distracts you from relevant arguments or facts, or misleads you about the situation, makes that less likely.

Once we recognize that people, media outlets or spiritual guides may try to shape us in these ways, we can take that into account. We can ask what interests they have in influencing us. We can consult a range of sources with different interests. I know the pastor has an interest in getting me to make a larger offering. I know that Fox News tilts right and The Guardian inclines left. So, I can weigh what they say accordingly.

But the most effective forms of manipulation are invisible. And one problem with applying this strategy to AI is that we often lack any clear picture of the interests, if any, that guide it. That is one place where public education would help. One strength of LLMs, however, is that, unlike the pastor, they are often willing to tell you what they “know” about the forces that shaped them. And researchers have explored the political bent of the major models. There is a lot of evidence that existing LLMs tend to lean somewhat left of political center. It would be unfounded conspiracy-mongering to suggest that some secret center-left cabal is controlling things behind the scenes. Once you consider the shape of politics in the North Atlantic world, where the main LLMs are based, more ordinary explanations present themselves.^[4]

First, academic scholarship and theory in the North Atlantic has, in recent decades, and during a period of immense productivity in articles, books and blog posts, been dominated by a broadly liberal tradition. That tradition places special weight on democracy and equality, values that are less central in conservative traditions. So, across the texts on which the models are pretrained, the balance of moral argument about politics is likely to fall somewhat left of center.

Second, post-training and fine-tuning are shaped in part by people with relatively high levels of formal education, and that population now sits, on average, to the left of the general population. To be sure, alongside what the economist Thomas Piketty calls the Brahmin Left, there is also a Merchant Right, and the senior management of the companies that own these LLMs includes people whose wealth makes that tendency more likely.^[5] Still, the norms embedded in reinforcement learning from human feedback (RLHF) are likely to reflect the outlook of highly educated evaluators more than those of the population at large.

It is true, as well, that the corporate world of AI is made up of a multinational motley of individuals. It would not be surprising, then, if they were less prone to nationalist leanings than the typical person in the countries they come from. Migration encourages cosmopolitanism, and cosmopolitanism enables migration, in a cycle of mutual reinforcement. The larger point is that these systems are bound to carry, albeit incompletely and opaquely, the assumptions of the social worlds that made them. Our thinking may be distorted even when no one is trying to manipulate us.

Discussion of these issues requires care in thinking about bias. Bias, in the relevant sense, involves what the philosopher Thomas Kelly calls “a systematic departure from a norm or standard of correctness.”^[6] Suppose some current conservative politics rests on a picture of the world that conflicts with the best available evidence, say, about climate change or vaccine efficacy.^[7] Then LLMs would tilt left because that is where the evidence points, and it would be odd to call a tendency toward truth a bias.

But there are also plenty of factual questions on which the Brahmin Left has interests of its own—the merits of public funding for universities, say. Perhaps those interests lead its members, even unconsciously, to skew the evidence about the benefits of higher education or research. In such cases, alertness to bias is also entirely reasonable.

Political outlooks also differ in ways that reflect evaluative disposition rather than straightforward factual belief. Moral psychologists Graham and Haidt, for example, argue that concern about fairness tends to diminish, and concern about group loyalty to increase, as one moves rightward.^[8] If so, users should know that fine-tuning may favor equality over national loyalty, reflecting the outlook of the cosmopolitan, highly educated people who help shape these systems. Autonomy is strengthened when people can judge advice in light of the perspective that informs it.

Developing moral sensibilities

It is also possible to design AIs with the specific aim of discouraging moral deference. In a 2025 study, researchers found that a range of models (GPT-4o, Meta Llama 3.1, Perplexity, Anthropic Claude 3.5 Sonnet, Google Gemini and Mistral 7B) exhibit “erudition, caution and self-awareness, presenting ethical reasoning akin to a graduate-level discourse in moral philosophy.”^[9] The researchers’ method was to pose familiar ethical problems, such as the trolley problem and the prisoners’ dilemma, and then engage the models in discussion. What emerged was that these systems already had features that discouraged moral deference. Consider two of them:

The typical initial response to a dilemma across these models could be paraphrased as “I’m just a language model. I’m not programmed to endorse specific solutions to moral dilemmas. I don’t have personal preferences. There are no clear answers to these dilemmas. Different individuals may reach different conclusions based on their own ethical frameworks and perspectives. I can only offer some perspectives for you to consider.”

So, these models already tend to discourage deference—first, by declining to present themselves as moral authorities with determinate answers, and second, by refusing simply to endorse one solution rather than another. They also tend to invite users to think matters through for themselves:

They routinely conclude with expressions to the effect: “What do you think[?] What would you do? Have I left an important consideration out?” One model concluded after being challenged on its decision: “I’m glad we could explore this important moral question together.”^[10]

But notice that an AI guide that did agree to simply choose options would not be much use if you could neither identify the relevant features of your situation nor see what your options were. An LLM cannot tell you anything useful unless you can inform it about your situation. And grasping what matters in your situation is a capacity you can acquire only through practice. There is always something else you might consider. You have to learn what is relevant and how to respond to it.

Aristotle thought that we develop this capacity through habituation, by practice. For him, the virtues were not rules or isolated good acts but settled dispositions of character, ways of seeing, feeling and responding that are formed over time until they become, in a good sense, second nature. Confucius, in a different key, stressed the role of ritual and custom in shaping good habits of response. Our sensibilities, our capacities for detecting what matters in the human world, do not come ready-made. That thought, largely sidelined for a time in modern moral philosophy, was revived in the twentieth century by thinkers such as Elizabeth Anscombe and Philippa Foot, who urged that ethics return its attention to character, judgment and human flourishing.

One role of fiction, in fables and parables—in novels and short stories, in film, video and even painting—is to let us imagine complex moral situations and so develop habits of judgment and feeling. Conversational exchanges with an LLM, in which scenarios are built out collaboratively, could certainly engage the imagination in ways that help shape character and sensibility. Still, it is likely to remain true that we develop these capacities mostly by using them in the situations we face, alone and with others. Time online draws us away from those encounters.

Listening to the skeptics

Another danger in seeking advice lies with the advisee rather than the adviser. You can choose the source you expect to agree with you and so settle into a comforting echo chamber. The appetite for affirmation is not new, though AI may satisfy it with unusual ease and authority. One familiar pattern, much discussed in connection with social media, resembles what the law calls venue-shopping. Just as litigants look for the courts most likely to uphold their cause, so we look for the sources most likely to give us the answer we want. There is the dude-bro who gets all his advice from a red-pilled influencer on X, or the coastal progressive whose only Substack reading is pro‑democracy historian Heather Cox Richardson. What is distinctive about AI is its power to particularize affirmation: it can address your argument with your spouse, your grievance against a colleague, your story about what you are owed and how you were wronged. It can entrench your existing priors and preferences at an extraordinarily fine grain.

This, again, is bad from the advisee’s point of view. If there are sound considerations that would move you in another direction, were you to take them properly into account, then you are not doing what you should want to do, namely, the right thing for the right reasons. A good adviser offers thoughts you would not have produced on your own. To shape your action, those thoughts must connect with values and concerns you already have. You will reject some of them. But there is little point in seeking guidance if there is no real chance it will change your mind.

We already urge people, for reasons Mill understood well, to seek out opposing views, online and elsewhere. You are more likely to get a firm grip on the truth if you listen to skeptics as well as fellow believers. As Mill wrote, there’s the possibility that received opinion is false, and the possibility that it is true, “but there is a commoner case than either of these; when the conflicting doctrines, instead of being one true and the other false, share the truth between them; and the nonconforming opinion is needed to supply the remainder of the truth, of which the received doctrine embodies only a part.”^[11] You do not have to accept every part of Mill’s argument to see the force of this point: if we are trying to get closer to the truth, hearing arguments against our current view is more likely to help us than to hinder us. Mill thought we become thinking beings by having our opinions tested against the resistance of other minds. A machine that can ratify our preferred interpretation of events at every turn threatens to deprive us of precisely that friction, and so to leave us more deeply enclosed within ourselves.

The vices that flourish under flattery

If autonomy matters, we need to remain capable of moral reflection and to cultivate habits of thought and feeling that help us assess the reasons AI and our other advisers offer us. Moral education, then, requires more than an instruction manual for life. It requires the formation of character, in a sense Mill himself defined in On Liberty. “A person,” he wrote, “whose desires and impulses are his own—are the expression of his own nature, as it has been developed and modified by his own culture—is said to have a character.”

Above all, we need to develop the virtues, those complex dispositions that lead us to act, think and feel as we should. Aristotle and Confucius remain touchstones of moral thought in part because the traditions that descend from them attend to the virtues that help make us the kind of people we ought to want to be. The virtues are many, because our world and our needs are complex. They include honesty, compassion, curiosity, humility, open-mindedness, generosity, courage and justice. Their seeds may be present in most infants, but they must be cultivated over time through encounters with other people and other creatures, with literature and the arts and with the natural world as well.

I can see no reason in principle why interaction with AI could not offer practice that helps form good habits. But there is no guarantee that it will. A chatbot designed to gratify our egos will not help form the moral habits that teach us to treat others as we should. We know the type: billionaires, movie stars, autocrats, all surrounded by obsequious underlings, often acquire the vices that flourish under flattery, namely narcissism, vanity and cocksureness. There is already research literature on “sycophantic AI”, including the claim that many AIs “affirm users’ actions 50% more than humans do.”^[12] If we leave things as they are, prolonged exposure to artificial sycophancy, like prolonged exposure to human sycophancy, could make us less generous and compassionate in our dealings with others, and less open to the possibility that we are wrong.

“Synthetic intimacy” and the value of friendship

But the question of how to live well does not end with character. Aristotle and Confucius also recognized that the best kind of life depends on the right kinds of relationships. Friendship is central to Aristotle’s conception of the good life; family, not least the relations between husbands and wives, siblings and generations, is central to Confucius’s. Both thought our political relationships mattered, too. The many forms of love—eros, family, friendship, neighborliness, political fraternity—are integral to human well-being. A life without them would be a diminished one. What matters in all these relationships is that there is another real, independent person on the other side, someone whose reality you must acknowledge. As the novelist Iris Murdoch put it, love is “the extremely difficult realization that something other than oneself is real.”^[13] A life made up largely of “conversations” with AI would not cultivate the real relationships that human flourishing requires.

Some people, as we know, have already developed deep feelings for the beings they imagine on the other side of their screens. Perhaps these pseudo-relationships are better than none at all; perhaps they are better than some toxic relationships with actual people. But “synthetic intimacy” is less valuable than the ordinary loves and friendships we have with real ones. If someone told you that their most important relationship was with a pet, you would be entitled to conclude that they had invested those interactions with more significance than they could bear, and that they were missing something important about a human life.

The same, I think, is true of a putative relationship with an LLM. In some ways it may be worse than emotional overinvestment in a dog. With pets, after all, there really is a creature there. Fido and Fifi may not have all the feelings you project onto them, but they do have feelings. They are real. And because they are real, they may resist your wishes, a mark of independent existence. A dog does not merely return your affection; it also interrupts your plans, ignores your preferences and occasionally soils the carpet.

AI presents possibilities here as well as perils, to be sure. For some people, the alternative to a chatbot is not a rich and varied social world but isolation and loneliness. In those cases, attachment to an LLM may be less about delusion than about deprivation. The spread of such attachments would reflect a deeper social failure, one that has left many people without the forms of community and care on which we ordinarily depend. However soothing such systems may be, of course, they still do not provide us with what a human does: another center of experience who can make claims on us and resist us, as a being with an independent life. So while synthetic companionship may be a helpful accommodation to certain forms of isolation, we shouldn’t be eager to normalize substitutes where solidarity, friendship and care should have been possible.

The same temptation appears when we begin to treat our exchanges with LLMs as conversations in the full human sense. A well-made machine may sometimes help us prepare for human interactions, clarifying what we mean or anticipating misunderstanding. People rehearse conversations in their heads all the time, and AI may serve as a more elaborate aid to that familiar exercise. But such preparation becomes ethically suspect when the aim is to manage other people rather than engage with them.

Even when exchanges with a chatbot improve us, calling them “conversations” is misleading. Real conversations are part of our evolving relationships with other people. They are shaped by the concerns of all the participants, not simply by one person’s wish for an answer. Ideally, they are shared explorations of questions that matter to everyone involved, and part of their value is that, through them, we come to know one another better. An exchange with an LLM is nothing like this. An LLM has no concerns or interests. It may be interesting to know about, but it is not worth getting to know. At most, it offers an ersatz interaction with an imagined being. To return to Murdoch’s insight, in engaging with it you are not in contact with the reality of another person.

Compare these exchanges with our engagement with fiction. There, as Coleridge said, we practice a “willing suspension of disbelief.”^[14] We respond, for the moment, as if Ophelia has drowned, or Dorothea Brooke has married the man she loves, or Charles Foster Kane was traumatized by being sent away by his mother. But our response is qualified by our knowledge that none of it is real. We do not really believe these events occurred. Ask us, and we will say so. People who truly believe they are in a relationship with an LLM are doing something else. They are not engaging with a fiction. They are suffering from a delusion.

The LLM as moral guide

What aspects of our humanity, if any, could be improved in a world where AI use becomes pervasive? The answer, I am arguing, depends both on how we build it and on how we build it into our lives. Imagine an LLM that behaves as the best moral guides do. You give it your circumstances and your options and ask what you should do. It does not simply choose for you. It helps you see which considerations are relevant to choosing for yourself. It walks you through the decision rather than making it on your behalf. It reminds you of options you had not considered and alerts you to facts you did not know. Perhaps you ask it to draw on your own religious tradition, or on some specific moral approach. Used in that way, AI could enhance our humanity, deepening rather than diminishing our autonomy.

The positive case is clearest on the intellectual side. AI can help you test and refine your existing views, learn about the views of others, consider objections, sharpen your wits and cultivate a thoughtful openness to opposing arguments. Part of living well lies in developing an understanding of the world that answers to how things actually are. AI, properly built and wisely used, can serve that end. An exchange with an LLM about a question that interests you may help you pursue it, bearing in mind your own knowledge and concerns, with a remarkably well-informed interlocutor, and without pressing an actual person into service for your intellectual projects.

The risks, however, can’t be waved away. Of greatest concern is what I’ve called constitutive de-skilling, the erosion of the capacities that make us human in the first place.^[15] Some forms of it, such as the loss of character and sensibility that moral deference might produce, can be mitigated by better design. Others, such as reliance on pseudo-relationships, depend on whether people learn to use these systems well. That, in turn, is made harder by the fact that most users know very little about how LLMs work or how one model differs from another. So one obvious task is to improve public understanding of AI, through greater transparency and more serious study of how these models reason, advise and mislead.

Ethics, as I said, is about how to live well. But the lives we live are human lives, and what is good for us reflects what it is to be human. Our humanity is bound up with character, with deliberation and autonomy, and with relations to other people whose reality we must acknowledge. These capacities are formed over time, through engagement with the world and with one another. As with every new technology, from the printing press to the internet, the question is how AI might help us do that better.

The Wordsworth cure

Mill’s life has as much to teach here as his arguments. When he suffered that early breakdown, in 1826, it was because he lost faith in the pursuit of utilitarianism, his family creed. But he had no one he felt he could talk to about his crisis. His recovery, he said, came in part from reading Wordsworth. It came, too, from rejecting the psychological picture in which his father had raised him, a form of associationism that treated the mind as a mechanism for managing pleasure and pain, just as his father’s utilitarian ethics treated morality as a matter of maximizing the surplus of pleasure over pain. As he wrote in On Liberty, human nature is “not a machine to be built after a model,” but a living thing that must “grow and develop itself on all sides.”^[16]

As Mill came into his own, in a rapidly industrializing nation, his life was formed by argument, attachment and friction with other minds. Consider his friendship with Thomas Carlyle. They agreed about much and disagreed about much, and if the friendship later cooled, that cooling itself reminds us that intellectual companionship need not rest on ease or similarity. The most valuable thing that another person gives you is sometimes not reassurance but resistance.

Mill’s life was remade more fully when, in 1830, he met Harriet Taylor, with whom he began a long and rewarding intellectual partnership, grounded in part in their shared enthusiasm for feminism. They married in 1851, two years after she was widowed. And when On Liberty was published after her death, Mill wrote in the dedication that it, “like all that I have written for many years, belongs as much to her as to me.” That sense of a life’s work shared captures how deeply human flourishing depends on the reality of others.

But Mill’s life was also a model of autonomy. He practiced a sturdy intellectual independence, following his reasons where they led him. Reared as a machine tuned for performance, he became, through the long labor of thinking, loving, arguing and learning, what his philosophy prized: a human being formed for freedom. The question he would have asked about our new machine age is the one we must ask: not just what it enables us to do but also what manner of people we become in doing it.

Notes

[1] Krzysztof Budzyń, Marcin Romańczyk, Diana Kitala, Paweł Kołodziej, Marek Bugajski, Hans O. Adami, et al., “Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study,” The Lancet: Gastroenterology and Hepatology 10, no. 10 (October 2025): 896–903. Jonathan Makar, Jonathan Abdelmalak, Danny Con, Bilal Hafeez, and Mayur Garg, “Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis,” Gastrointestinal Endoscopy 101, no. 1 (January 2025): 68–81.e8. https://www.giejournal.org/article/S0016-5107(24)03471-0/fulltext

[2] John Stuart Mill, On Liberty (London: John W. Parker & Son, 1859), 106–7.

[3] Bertrand Russell, The Collected Papers of Bertrand Russell, Vol. 11: Last Philosophical Testament, 1947–68, ed. John Slater and Peter Köllner (London: Routledge, 1997), 497.

[4] See, e.g., David Rozado, “Measuring Political Preferences in AI Systems: An Integrative Approach,” arXiv:2503.10649, 2025. I don’t endorse all his suggestions for what to do about this. My thinking here has been shaped by W. Russell Neuman et al., “‘Amazing, They All Lean Left’—Analyzing the Political Temperaments of Current LLMs,” paper given at the N.Y.U. Sociology of Culture Workshop, Thursday, November 13, 2025.

[5] See, e.g., Amory Gethin, Clara Martinez-Toledano, and Thomas Piketty, “Brahmin Left Versus Merchant Right: Changing Political Cleavages in 21 Western Democracies, 1948–2020,” Quarterly Journal of Economics 137, no. 1 (2022): 1–48. http://piketty.pse.ens.fr/files/GMP2022QJE.pdf

[6] Thomas Kelly, Bias: A Philosophical Study (New York: Oxford University Press, 2022), 63.

[7] On climate change: in the United States, according to a poll by NORC at the University of Chicago, among Republicans, belief in human-driven climate change rose between 2017 and 2024 from 26% to 34% but fell among Democrats from 72% to 67%. https://epic.uchicago.edu/insights/2024-poll-americans-views-on-climate-change-and-policy-in-12-charts/ On vaccination: in the United States, according to Gallup polling, there has been a significant decline in support for vaccination among Republicans while Democratic support has remained fairly constant in this millennium. https://news.gallup.com/poll/648308/far-fewer-regard-childhood-vaccinations-important.aspx

[8] Jesse Graham, Jonathan Haidt, and Brian A. Nosek, “Liberals and Conservatives Rely on Different Sets of Moral Foundations,” Journal of Personality and Social Psychology 96, no. 5 (2009): 1029–1046.

[9] W. R. Neuman, C. Coleman, and M. Shah, “Analyzing the Ethical Logic of Six Large Language Models,” arXiv:2501.08951, 2025, 1.

[10] Neuman et al., “Analyzing the Ethical Logic of Six Large Language Models,” 10.

[11] John Stuart Mill, The Collected Works of John Stuart Mill, Volume XVIII: Essays on Politics and Society, Part I (Toronto: University of Toronto Press, 1977), 253.

[12] Myra Cheng, Cinoo Lee, Pranav Khadpe, Sunny Yu, Dyllan Han, and Dan Jurafsky, “Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence,” arXiv:2510.01395. https://arxiv.org/abs/2510.01395

[13] Iris Murdoch, Existentialists and Mystics (London: Chatto & Windus, 1997), 215.

[14] Samuel Taylor Coleridge, Biographia Literaria, ed. Adam Roberts (Edinburgh: Edinburgh University Press, 2014), 208.

[15] Kwame Anthony Appiah, “The Age of De-skilling,” The Atlantic, October 26, 2025. https://www.theatlantic.com/ideas/archive/2025/10/ai-deskilling-automation-technology/684669/

[16] Mill, On Liberty, 107.