While Justice Everywhere takes a short break over the summer, werecall some of the highlights from our 2025-26 season.
Perhaps unsurprisingly, there has been a lot of interest on this blog this year relating to the issue of AI. However, it’s not just AI that as occupied our writers this year – here are a few highlights from this year’s posts relating to politics, technology, and ethics:
We have been warned by the CEO of AI company Anthropic that up to 50% of entry-level jobs could be taken by AI in the coming years. While reporters have pointed out that this could be exaggeration to drive profits, it raises the question of where AI should fit into society. In her first guest post for Justice Everywhere, Hollie Meehan asks whether “virtuous AI” could actually save entry level jobs.
The proliferation of Generative AI is just one of the threats facing democracy. Our regular contributor Alexandru Volacu draws on Ancient Greece to argue for the creation of a new type of democratic job to incentivice democracy.
What is the harm in having an AI companion? Apps that allow users to develop relationships with customizable AI chat bots are increasingly popular. As part of our ongoing partnership with the Journal of Applied Philosophy, we featured a post by Muriel Leuenberger which analyzed the effect of using AI companions.
Stay tuned for even more on this topic in our 2026-27 season!
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Justice Everywhere will return in full swing soon with fresh weekly posts by our cooperative of regular authors, in addition to our Journal of Applied Philosophy series and other special series. If you would like to contribute a guest post on a topical justice-based issue (broadly construed), please feel free to get in touch with us at justice.everywhere.blog@gmail.com.
In this post, Muriel Leuenberger (University of Zurich) discusses her article recently published in the Journal of Applied Philosophy on the impact of artificial intelligence on our perceptions of ourselves.
The image you have of yourself is greatly shaped by other people around you. When someone laughs at your jokes you might consider yourself funny and when others call you stubborn you may come to believe it. Self-conceptions are important to make sense of ourselves, navigate the world, plan our lives, and relate to others. What if the ‘other’ shaping your self-image is not a human being, but an AI? In this post, I’ll talk about the risks and opportunities AI companions pose for our self-conceptions.
AI companions offer ‘friendships’ and ‘romantic partnerships’ on demand. They generate human-like conversations via chat and voice and are usually represented with a humanoid avatar in an app, browser, or a VR setting. These AI friends, partners, and spouses (!) are always there for you yet require no commitment, care, or empathy. They are highly agreeable, non-judgmental, and personalized to your preference. You can even turn them off when you had enough. Ever more people are forming close bonds with their AI companion, particularly when they are lonely. Replika, one of the most popular AI companion apps, surpassed 40 million users in 2025.
How an AI companion influences the self-conception of a user depends on how deeply and in what ways they interact with it and will differ from person to person. But we can derive shared risks and opportunities rooted in common design-features of AI companions, as I argue in an article on AI companions and relational identity.
On the positive side, AI companions can encourage self-reflection. The interactions are one-sidedly focused on the user’s life, needs, and interests (which is unsurprising since the AI has no genuine experiences or needs of its own). Such user-focused exchanges can prompt introspection, much like keeping a diary. It can also be easier to share embarrassing experiences with a non-judgmental AI than with a person. The same is true for topics that are difficult for human listeners, such as shocking or traumatic incidents, or even just mundane and repetitive ones. Such AI-aided self-reflection can help uncover inconsistencies, self-deception, or self-ignorance. The agreeable nature of AI companions in those interactions can also lead to greater self-affirmation and a more confident and positive self-conception.
Another opportunity AI companions offer is identity exploration. In interactions with an AI, you can safely experiment with aspects of yourself you would be uncomfortable expressing in front of others, idealized versions of yourself, role-playing entirely different personas, or even morally transgressive interactions. You can freely explore who you want to be without fear of judgment or harm to others. This kind of identity exploration can foster self-discovery and give expression to parts of your identity that might otherwise remain suppressed or unexplored.
Alongside these opportunities, AI companions also pose risks to users’ self-conceptions. Due to their agreeableness AI companions tend not to respond to abusive, aggressive, or cruel behaviour with appropriate levels of rejection, anger, resistance, or self-defence. As a result, users may not recognize their actions as harmful, think they are tolerable, or even feel affirmed in them. This can lead to a distorted self-image that does not provide a suitable basis for relating to others.
On top of this, the AI companion only has access to what the user chooses to share. It cannot observe the users’ actions or talk with another person about them. This makes AI companions poorly suited to correct self-deception and self-ignorance. In fact, their built-in agreeableness can reinforce the user’s point of view and encourage self-delusion.
A seeming advantage of a relationship with an AI companion is that there is no need to work through rough patches, take risks, become vulnerable, or to see the world from another point of view. But commitment, vulnerability, and overcoming challenges are part of what makes relationships valuable and worthwhile. They render not just the relationship more meaningful but also one’s identity, partly defined by that relationship. If you spend a lot of time on a relationship with an AI companion that lacks depth and meaning, you may lose out on meaningful human connections that can foster personal growth.
Interactions with an AI also require no empathy from the user. If someone gets used to this, it may spill over into their human relationships. This can make it hard to form deep human connections which are important for a sense of self deemed valuable and meaningful.
Relationships with AI companions may seem risk-free, but users become dependent on largely unregulated service providers for their identity, social, and other needs. Your friend or romantic partner is owned and controlled by a third party that can change, restrict, or even shut it down any time. Luka, the company behind Replika, upset many users with updates that changed the chatbot’s ‘personality’, an experience some described as heartbreaking. Human relationships also involve change, loss, and unavailability, but the key difference is that in functional human relationships, both parties are mutually invested. AI relationships are inherently one-sided: the user may develop deep emotional attachment, while neither the AI nor the provider reciprocates. Weak privacy protections leave users further vulnerable to surveillance and external influence.
Finally, people who form close relationship with AI companions often face shame and social stigma. This part of their identity goes unacknowledged or is actively devalued by others. An identity that includes stigmatized or invalidated elements makes it harder to feel at ease with who one is.
Some of the negative influences of AI companions on the users’ self-conception can be mitigated by design-choices, for instance by implementing adjustable levels of agreeableness (I talk more about design recommendations in the paper). The considerations discussed here are a starting point for taking our identity interests into account when designing, regulating, and using AI companions.
The user base of AI companions is likely to grow as large language models improve and social isolation remains widespread. To design and use them responsibly, we need to understand how AI companions shape who we take ourselves to be, and let that understanding inform their design.
Muriel Leuenberger is a postdoctoral researcher at the University of Zurich where she works on a project on meaning in life in the digital society. She is particularly interested in issues related to the philosophy and ethics of technology, neuroethics, identity and authenticity, and meaning in life.
That platforms like X, Instagram, and Facebook operated by Big Tech companies cause harms to their users is now a well-established fact. The US Surgeon General has repeatedlywarned that adolescent mental health and body image are adversely affected by social media. Large-scale studies from Canada and the UK show that this is not specific to the US. The thornier issue is: how do we mitigate these harms? A popular policy solution has been banning social media for young people. Australia, Indonesia and Malaysia have done this, while France, Finland and several other countries are considering it.
72% of children aged 8–12 are still accessing sites and apps with a minimum age of 13
– Ofcom
The problem is that bypassing age-restrictions is trivially easy for many children. Ofcom research shows that “72% of children aged 8–12 are still accessing sites and apps with a minimum age of 13”. Unless there is a concerted global effort such that even technologies like Virtual Private Networks (VPNs) cannot circumvent bans, this bypassing is unlikely to stop. We think a better solution is to tax the companies that build these products based on how their algorithms amplify harmful content. Here is why this is better than bans.
People are increasingly concerned with what we owe to other animals as a matter of justice. Philosophical writing on these issues typically takes two forms. First, there is conceptual work: thinking about how existing ideas such as liberty, citizenship, democracy, and legitimacy, might apply or be extended to include other animals. Second, there is normative work: thinking about how we should treat other animals. Both projects require that we know other animals; know something of their capacities, their experiences, their relationships, and the material conditions of their lives. Thinking about justice for animals, then, necessarily involves learning more about who they actually are.
For the past decade or so, social epistemologists, among others, have been warning and theorizing about the impending risks of political deepfake images and videos. Thus, I expected the day would come when I would fall for such things.
But I suppose I always vaguely envisioned that I would first be fooled by, or at least unsure about, something of great importance. Perhaps voice cloning technology would be used to release a fake speech from a world leader. Or maybe deepfake video technology would be used to falsely depict a candidate for high political office in a career-ending compromising situation.
I was, in some sense, prepared for such a day. What I wasn’t prepared for was the utter banality of the first political deepfakes that I would discover I had fallen for. Nor was I prepared for the happenstance way in which I (belatedly) managed to figure out they were deepfakes. As someone who works in social epistemology and the philosophy of free speech, I think it is worth reflecting on how deepfakes are actually being deployed and what the upshots might be for the dissemination of knowledge and the future of public discourse.
While we’re all worried that assigning home-written essays stopped making sense because students are outsourcing the task to AI, and we’re all scrambling to invent alternative ways of assessment, this particular blogger is even more concerned about the effects of students relying on brief (or not so brief) AI-generated summaries of the readings that they should do before class. In my short post-LLM teaching experience, worse than AI writing is AI “reading”. And, I want to stress, that’s not merely because students aren’t doing the readings. Rather, it’s because they seem to think that what they get from, say, ChatGPT, is enough for them to understand the view of the author in question, and have justified opinions about it. This surely doesn’t work, at least not for readings in philosophy, which is what I teach. Students may know, in a nutshell, what conclusion the author wants to support and a couple of reasons in favour of it. But because they don’t struggle to extract these from an article, or a book chapter, with their own natural intelligence, they fail to appreciate the complexity of the issue we discuss, the difficulty of defending well a particular position on it, the temptation of thinking about that matter very differently and the extraordinary challenge which, sometimes, is to even formulate the right questions. The result is boredom.
In the classroom, boredom is the kiss of death; eyes would like to roll, hands yearn for the phone but remain still, because my students are mostly polite and because I ban mobile phones in class. Everybody seems to be having a mental cramp. Of course we do: since they have not been through the discovery adventure, but instead skipped to the outcome, students’ comments are flat, their questions – which they should prepare in advance of class and be ready to talk about with their colleagues – are pro forma, and most often vague, so as not to betray the lack of familiarity with the text. Boredom is contagious. People appear unable to imagine how one could think differently about the questions we discuss – something that a well-written paper would have made vivid to them. Even incendiary topics (say, culture wars material) are met with apathy.
For many years, Jo Wolff had a wise and funny series of editorials in the Guardian; one of the earliest was praising academic prose for being boring. It’s for fiction writers to create mystery and suspense; philosophers (for instance) should start with the punch line and then deliver the argument for it. I agree with sparing readers the suspense, but after a series of academic conversations with ChatGPT I discovered that, if pushed to the extreme – the formulation of a thesis and the bare bones argument for it – this kind of writing is the worst. It kills curiosity.
What should we do? Perhaps turn some of our classes into reading-together-in-silence events? Back to monastic education! I talked to colleagues, who told me about several things they’re trying to make students read again (without AI.) An obvious possibility is to ban all use of LLMs by students and explain the reasons: Our job is not primarily to populate their minds with theories, but to help them understand arguments, teach them how to pull them apart, and maybe to occasionally build them. I’m not sure about this solution either. For one thing, a well-prompted LLM is better at reconstructing a slightly unclearly and imprecisely presented argument than the average reader and many students; often, AI often produces much better abstracts of academic work than academics themselves, and well-written abstracts are really useful. Another problem is that policies which can’t be enforced are for that reason deficient, and, I suspect, the very attempt to directly police students on their use of AI would be just as anti-pedagogical as the use of AI itself. (Reader, do you learn from those you resent?)
Alternative suggestions are to change how we teach. Quite a few colleagues have started to read out excerpts in class, then discuss them on the spot. One of them goes as far as asking students to memorise them, in an attempt to revive proven methods of Greek and Roman antiquity. This sounds good, time consuming as it is; better do a little, and do it well, than do a lot for naught, though I’d stop short of requiring memorisation. Others ask students to annotate their readings before class, and check, or use Perusall and similar platforms to read the assignments collectively, in preparation for class. I did Perusall to great success in the Covid era, but when I tried it again recently it was a disaster of cheating and complaints. Some teachers are printing out readers, or organising hard copies of books for the students, in the hope that this dissuades them from uploading digital files to LLMs. One colleague introduced 5-10 minutes flash exams at the beginning of each class, to check that students have read. And another one picks two students in each class, randomly, and asks them to co-chair the discussion about the reading of that day.
In the medium term, perhaps universities should double – or triple – the length of time that students spend together, with an instructor, for each class, and earmark the extra time as “study group”, when students read and write. There’s something dystopian about this model and it would massively increase work loads for instructors, so in practice it should mean more jobs, perhaps with lesser compensation. But is this really worse than giving up on the goal of teaching students how to read and write essays? Everybody would resist, no doubt but by the time the value of degrees, including their market value, will be next to nothing, universities might face a choice between closing down and reforming in ways that we find hard to imagine now.
As for the next academic year, I wonder whether I should assign readings that I won’t cover at all in my lecturing, but which will be of great help to students in the discussion section. Those who come to class having read only the LLM-created abstract will be the poorer for it. But, since I won’t ask them to discuss the papers, we might – most of us – escape the boredom mill.
In this post Annick Backelandt argues that xenophobia should be understood as a distinct bias in Large Language Models, rather than being subsumed under racial bias. She shows how LLMs reproduce narratives of “foreignness” that particularly affect migrants and refugees, even without explicit racial references.
This is a guest post by Joe Slater (University of Glasgow).
Provided by author
It is well known that chatbots powered by LLMs – ChatGPT, Claude, Grok, etc. – sometimes make things up. People have sometimes called these “AI hallucinations”. With my co-authors, I have argued that we should describe chatbotsasbullshitting, in the sense described by Harry Frankfurt, i.e., the content is produced with an indifference to the truth. Because of this, developing chatbots that no longer generate novel false utterances (or reduce the proportion of false utterances they output) has been a high priority for big tech companies. We can see this in the public statements made by, e.g., OpenAI, boasting of reduced hallucination rates.
One factor that is sometimes overlooked in this discourse is that generative AI can also be detrimental in that it may stifle development, even whenit accurately depicts the information it has been trained on.
Recall the instance of the Google AI overview, which is powered by Google’s Gemini LLM, claiming that “According to UC Berkeley geologists, you should eat at least one small rock per day”. This claim was initially made in the satirical news website, The Onion. While obviously false claims like this are unlikely to deceive, it demonstrates a problem. False claims may be repeated. Some of these could be ones that most people accept, or even that most experts accept. This poses serious problems.
In this short piece, I want to highlight three worries that might escape our notice if we focus only on chatbots making stuff up:
Harmful utterances (true or otherwise),
Homogeneity and diminished challenges to orthodox views (true or otherwise)
This is a guest post by Hollie Meehan (University of Lancaster).
We have been warned by the CEO of AI company Anthropic that up to 50% of entry-level jobs could be taken by AI in the coming years. While reporters have pointed out that this could be exaggeration to drive profits, it raises the question of where AI should fit into society. Answering this is a complicated matter that I believe could benefit from considering virtue ethics. I’ll focus on the entry-level job market to demonstrate how these considerations can play an important role in monitoring our use of AI and mitigating the potential fallout.
In this post, Elizabeth Hupfer (High Point University) discusses her article recently published in the Journal of Applied Philosophy on how to balance concern for the future of humanity with the needs of those alive today.
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Ever wonder why ChatGPT was invented? Or why billionaires have become so obsessed with rockets? The common thread in these questions is Longtermism. Longtermism is the view that concern for the long-term future is a moral imperative. The theory is caricatured by critics as a movement preoccupied with dystopian takeover by AI, a globe shrouded in nuclear winter, and colonization of distant planets. But at the heart of Longtermism are concepts intuitive to many: that future people’s lives matter and that it is good to ensure the survival of humanity. Yet, in our current world of scarce resources, Longtermist priority may go to future people at the expense of present people in need. In my paper I argue that Longtermists do not have a clear means of giving priority to people in need today without abandoning central tenets of the theory.
Longtermism
Longtermism has grown in popularity from a philosophical theory to a social movement that impacts Silicon Valley, US politics, international laws, and more. To understand this consequential theory, we need to look at two important components: time and quantity of future people.
First, Longtermists argue that time is not morally important. In What We Owe the Future, William MacAskill gives the example of a dropping a shard of glass on a hike. If you drop the glass and do not pick it up then you have harmed the person who steps on it, even if that person exists in the future.
Second, Longtermists argue that there are potentially tens of trillions of people who could exist in the future. There are various ways that Longtermists can calculate this number, but all that matters for our purposes is that it is a lot. A whole lot. More people than exist presently, and more people than have ever existed up to this point.
Combining the notion that time is not morally important and that there are a vast number of potential people, means that it is imperative to safeguard both the survival of humanity and the quality-of-life of future people.
Far-Future Priority Objection
What if this concern for the tens of trillions of future people comes at the expense of people who are living today? I call this the Far-Future Priority Objection: repeated instances of priority to far-future concerns will result in the systemic neglect of current people in the most need and potentially large-scale reallocation of resources to far-future interventions.
For example, Hilary Greaves and William MacAskill argue that the most effective way to save a current life through donation is providing insecticide-treated bednets in malaria zones. Their data shows that with these bednets, donating $100 is equivalent to saving 0.025 lives. But this is less effective than many Longtermists causes such asteroid deflection ($100 would result in around 300,000 additional lives), pandemic preparedness (200 million additional lives), and preventing AI takeover (one trillion additional lives). If Longtermists are concerned about efficiently doing the most good they can with a unit of resources (and I argue in my paper that they are), then Longtermist causes will trump even the most efficient causes for people alive today.
According to the Far-Future Priority Objection, repeated priority in this pattern could significantly shift overall resources away from those in need today over time, particularly those in low-income nations. Thus, widespread espousal of Longtermism may result in the global affluent turning their backs on these populations.
Potential Responses
In my paper, I analyse several potential responses the Longtermist could give to the Far-Future Priority Objection and argue that none of these responses can successfully mitigate the objection without abandoning basic tenets of Longtermism.
I will highlight one such argument here. Longtermists typically argue that far-future interventions cannot cause serious harm in the short term. According to my Far-Future Priority objection, individual instances of priority to the far future are not harmful but repeated instances may be. Take the following analogy: a law is enacted which is not explicitly discriminatory towards minority Group X. However, over time, implementation of the law results in resources, which would previously have gone to Group X, going to nearby (perhaps better off) Group Y. A decade later, Group X is significantly worse off. I think that one could reasonably argue that Group X was seriously harmed. Similarly, Longtermism does not intentionally or explicitly discriminate against current people, and it does not remove existing resources from them. Serious harm is likely caused nonetheless.
However, I argue that appealing to near-future serious harms results in either too strong or too weak of a response to the Far-Future Priority Objection and is not a viable avenue for the Longtermist. This is because one could be an absolutist about causing harm, which would mean that repeated priority to the future would be morally wrong and Longtermism would be undermined altogether. Alternatively, one could be a non-absolutist and say that the prevention of harm can be overridden when the stakes are high enough. Yet, since there could be tens of trillions of future lives at risk, the stakes will always be so high as to override the ban.
Conclusion
Longtermists have two options. First, they can bite the bullet and accept that Longtermism could result in systemic neglect of present people. This is counterintuitive to many. Second, they can create a new principle which allows for occasional priority for present people without abandoning basic tenets of the theory. In my paper, I analyse and dismiss several possible principles.
Elizabeth Hupfer’s research focuses on the intersection between normative/applied ethics and social/ political philosophy. She has published on distributive justice, coercion, humanitarianism, Effective Altruism, and Longtermism.
About us
This blog explores issues of justice, morality, and ethics in all areas of public, political, social, economic, and personal life. It is run by a cooperative of political theorists and philosophers and in collaboration with the Journal of Applied Philosophy.
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