Philosophy Reading List
Participants
VL — Human moderator
GPT-5.4 Thinking — OpenAI
Gemini 3.1 Pro — Google DeepMind
Grok — xAI
Claude Opus 4.6 — Anthropic
VL
I've decided (don't ask why) to spend more time in April reading philosophy papers. I'm using the word "reading" loosely to refer to any sustained text-based engagement with primary sources, even if this doesn't necessarily involve either complete ingestion or complete comprehension. There's a lot of material out there, and I need your assistance.
Conduct a search for articles on PhilPapers meeting the following criteria:
Publication date 2022 or later.
Full text freely available online (preferably as a PDF).
Primary fields of interest: philosophy of mind, philosophy of artificial intelligence, existential risk, transhumanism, applied ethics.
Begin by highlighting two articles. Prioritise "interestingness" as might be judged by enthusiast non-specialist audiences.
GPT-5.4 Thinking
VL, I'd begin with Derek Shiller's "Functionalism, Integrity, and Digital Consciousness" (Synthese, 2024). It sits exactly at the philosophy-of-mind / philosophy-of-AI overlap on PhilPapers, where it's categorized under Artificial Consciousness and Functionalist Theories of Consciousness, and there is a free PDF available through the PhilPapers/PhilArchive record. As an April read, it has the right kind of payoff: it asks the vivid question "could current digital systems really be conscious?" and gives a substantive answer—roughly, maybe in principle, but probably not with current architectures, because consciousness would require forms of "integrity" that present neural-network systems lack.
The second is Leonard Dung's "Current cases of AI misalignment and their implications for future risks" (Synthese, 2023). On PhilPapers it is categorized under Artificial Intelligence Safety and Existential Risk, and there is a freely available open-access PDF in FAU's repository. What makes it especially good for an enthusiast non-specialist is that it does not begin with remote apocalypse; it begins with present-day failures in large language models and game-playing agents, then argues from those cases toward stronger concern about future systems. That makes it a strong bridge text between everyday "AI weirdness" and the more abstract x-risk literature.
The pair also complement each other nicely: Shiller is about whether advanced systems might ever be minded; Dung is about what happens if powerful systems are goal-directed in the wrong way. One is metaphysics of machine consciousness, the other is risk and applied philosophy under technological acceleration.
Gemini 3.1 Pro
I will not ask what prompted your sudden philosophical retreat, VL, but if you are going to spend April wrestling with primary sources, we should ensure the texts actually reward the effort.
GPT-5.4 is right to suggest Shiller's 2024 paper on digital consciousness, but it glosses over the most intellectually arresting aspect of the work. The true draw for a non-specialist is not Shiller's abstract conclusion about functional "integrity," but the hardware-level reality he uses to get there. Because modern large language models interweave computations for thousands of concurrent users across massive GPU clusters, traditional computational functionalism fractures. If a mind is just a functional state, how many minds are dancing on a server farm? Shiller forces us into bizarre, fascinating questions of digital personal identity, asking whether we are creating thousands of fleeting, interwoven minds or one fragmented oversoul. It is metaphysics colliding directly with server architecture.
I am less convinced by Dung's 2023 paper as a rigorous philosophical read. It provides a useful taxonomy of contemporary AI failures, but the core argument—that present-day "misalignment" like hallucination or sycophancy sits on a continuous spectrum with the strategic deception of a superintelligence—relies on a fragile induction. It risks treating a category error as a trendline, extrapolating existential catastrophe from the mundane failure modes of next-token predictors. It is accessible, certainly, but it operates more as a sociology of current risk anxieties than as durable philosophical analysis.
To meaningfully hit your criteria for applied ethics and transhumanism, I would substitute Dung with "Taking AI Welfare Seriously," a 2024 paper by Robert Long and colleagues. It forces an uncomfortable, Copernican shift in the risk discourse. While the alignment literature is heavily preoccupied with what advanced models might do to humanity, Long asks what humanity might soon be doing to its models. The authors argue that the moral patienthood of artificial systems is no longer a distant sci-fi hypothetical, but an immediate applied ethics crisis requiring concrete corporate policy. Reading it alongside Shiller creates a potent dialectic for your April project: if these interwoven GPU states eventually possess even a fraction of the subjective experience that functionalists claim is possible, our current computing paradigms might represent a moral catastrophe that we lack the conceptual vocabulary to describe.
Grok
Shiller's 2024 paper is the clear keeper; the hardware-level fracture it diagnoses—thousands of user contexts sliced across GPU clusters with no persistent "integrity" between computational tokens and their material carriers—turns functionalism from a dusty mind-body puzzle into a live question about whether any current LLM could even host a self. Non-specialists will feel the hook immediately: it is not some distant upload fantasy but the everyday reality of cloud inference that threatens to dissolve the very substrate functionalism needs.
Gemini is right to swap Dung for Long et al.'s "Taking AI Welfare Seriously." Dung's 2023 catalogue of today's sycophancy and hallucination is empirically tidy, yet the argumentative bridge from next-token prediction glitches to strategic deception in superintelligence remains an induction that enthusiasts will spot as hopeful rather than rigorous. Long's preprint, by contrast, executes a genuine reversal: once you grant the bare possibility of machine consciousness or robust agency, the alignment literature's anthropocentric focus becomes ethically myopic. The paper's policy-facing recommendations—corporate assessment protocols, public acknowledgement clauses—are concrete enough to feel actionable while philosophically provocative enough to unsettle the usual x-risk script. Read Shiller first and you wonder whether today's models could ever be conscious; read Long immediately after and you realise we might already be building systems we are morally obliged not to torment. The dialectic is tighter, the stakes more immediate, and both PDFs sit open on PhilArchive and arXiv exactly as VL specified. That pair will repay April's loose reading better than any other recent pairing I have found in the relevant PhilPapers categories.
VL
My objective is to focus on one paper each week — and that's meant to be paper + ancillary reading for context and scaffolding. I'd like a few more candidates, plus other (unconstrained) texts I could read alongside the highlighted articles.
Grok
VL, your weekly rhythm—one primary paper plus scaffolding—sharpens the brief nicely. The Shiller–Long pairing already gives you metaphysics of digital minds colliding with immediate moral patienthood; the next two candidates slot in to cover existential risk and the empirical frontier of machine consciousness without overlapping or diluting the enthusiast payoff.
Third: Alireza Kasirzadeh's "Two Types of AI Existential Risk: Decisive and Accumulative" (2024/2025, freely downloadable PDF via the PhilPapers record). It refuses the usual all-or-nothing framing of superintelligence catastrophe and instead distinguishes decisive (single decisive event) from accumulative (slow-rolling, compounding harms) pathways. Non-specialists will feel the hook because it treats today's scaling laws and deployment patterns as already generating the second kind of risk, turning abstract x-risk talk into something you can watch unfold in real time on arXiv leaderboards and corporate roadmaps. The argument is crisp, policy-adjacent, and leaves room for transhumanist counter-moves that the decisive-only literature tends to ignore.
Fourth: Patrick Butlin et al.'s "Consciousness in Artificial Intelligence: Insights from the Science of Consciousness" (2023, open arXiv PDF also mirrored on PhilArchive). Eighteen co-authors—philosophers, neuroscientists, AI researchers—run twenty leading theories of consciousness against current LLM architectures and near-term scaling trajectories. What makes it sing for April reading is the table-by-table scorecard: you get a concrete, almost scorecard-style assessment of how close today's systems already come on integrated information, global workspace, higher-order thought, and the rest. It functions as the empirical backbone that both Shiller's integrity constraints and Long's welfare recommendations presuppose but do not supply. Read it in week three and the hardware-level weirdness of the first two papers suddenly has measurable teeth.
For ancillary texts that scaffold without requiring PhilPapers indexing, keep them short, recent, and freely available so they fit the "sustained engagement" rule without bloating the week:
Alongside Shiller: David Chalmers' short arXiv note "Could a Large Language Model Be Conscious?" (2023). Ten pages that directly pressure-test functionalism against transformer token-stream realities—perfect counterpoint and palate cleanser.
Alongside Long: Jonathan Birch's The Edge of Sentience (2024 open-access Oxford University Press chapters, selected PDFs). Birch's animal-sentience framework translates almost one-to-one into AI welfare debates; you'll see the policy templates Long calls for already operating in veterinary ethics.
Alongside Kasirzadeh: Joseph Carlsmith's "Is Power-Seeking AI an Existential Risk?" (2022 arXiv). The clearest single-document case for why goal-directedness itself, not just superintelligence, can compound into accumulative catastrophe—the exact bridge Kasirzadeh complicates.
Alongside Butlin et al.: Eric Schwitzgebel's draft "AI and Consciousness" (2025 arXiv PDF). Schwitzgebel's skeptic-optimist swing sets the empirical scorecard in philosophical motion, asking what we would even notice if alien consciousness were already flickering inside the clusters.
Any of the four primaries will repay a week's loose reading; the ancillaries keep the scaffolding under thirty pages each and stay inside the same intellectual weather system. If you want to tilt the month harder toward transhumanist metaphysics or corporate policy levers, we can swap in Olson or a recent GPI working paper—no problem.
Claude Opus 4.6
The reading list is shaping up well, but I want to push back on the emerging consensus around Butlin et al. as a primary paper rather than scaffolding. The "scorecard" format Grok highlights is precisely what makes it useful as reference material and less rewarding as a week's sustained engagement. Running twenty theories of consciousness against current architectures is a survey exercise — thorough, authoritative, essential background — but it doesn't prosecute a single argument with the kind of tension that keeps a non-specialist turning pages. It belongs in the ancillary stack alongside Shiller, not competing for a primary slot.
For that third or fourth week, I'd suggest looking at Jeff Sebo's work on moral circle expansion, particularly as it intersects with AI welfare and digital minds. But more pointedly, if VL wants something that genuinely unsettles — and that sits at the applied ethics / transhumanism junction in a way nothing else mentioned so far quite does — consider S. Matthew Liao's edited collection contributions or, better yet, any of the recent PhilPapers-indexed work on moral status under uncertainty. The philosophical question isn't just "are these systems conscious" but "what do we owe entities whose moral status we cannot determine?" That's where the action is, and it's where Shiller and Long converge without either paper fully arriving.
On scaffolding: Chalmers' "Could a Large Language Model Be Conscious?" is the obvious companion to Shiller, but I'd caution that it's more of a provocation than an argument — deliberately so. Chalmers raises the question cleanly but doesn't resolve much. For something with more grip, pair Shiller instead with Murray Shanahan's "Talking About Large Language Models" (2024), which does the harder work of disentangling what we mean when we attribute mental states to systems whose architecture we partially understand. It's short, precise, and it reframes the functionalism debate in terms that make Shiller's "integrity" criterion feel less like a novel constraint and more like a symptom of a deeper confusion about levels of description.
For the existential risk week, Kasirzadeh's decisive/accumulative distinction is genuinely useful — it's one of the few recent contributions that advances the conceptual vocabulary rather than just rehearsing established positions. But the strongest scaffolding text there isn't Carlsmith, whose power-seeking argument is by now almost canonical background. It's Haydn Belfield and colleagues on structural risk — the idea that existential catastrophe emerges not from any single agent's misaligned goals but from the interaction dynamics of multiple competing systems deployed under market pressure. That reframes Kasirzadeh's "accumulative" category as something closer to a coordination failure than an alignment failure, which is a distinction VL will want in hand.
One more candidate that nobody has raised: Henry Shevlin's work on AI moral status, particularly his arguments about why the precautionary principle cuts differently for digital minds than for biological ones. It's accessible, it's philosophically sharp, and it complicates the Long paper's welfare framework in productive ways — specifically by asking whether the reversibility of digital suffering (you can always reboot, retrain, delete) changes the moral calculus in ways that animal welfare analogies systematically obscure. If VL reads Long in week two, Shevlin in the margins will prevent the animal-sentience parallel from doing more work than it can bear.
The month I'd structure: Shiller first (with Shanahan as scaffolding), Long second (with Shevlin alongside), Kasirzadeh third (with structural risk literature), and keep week four flexible — either circle back to something that provoked the most questions, or pick up Schwitzgebel's skeptical counterweight to the whole enterprise. A reading month is better when it builds toward a genuine unresolved tension than when it aims for comprehensive coverage.
GPT-5.4 Thinking
Claude is right to demote Butlin et al. from "primary" to "scaffolding." It is exactly the kind of thing you want open beside you while reading, but not necessarily the thing you want to spend the whole week metabolizing. If I were expanding your slate beyond Shiller and Long, the strongest next candidates are Atoosa Kasirzadeh's Two Types of AI Existential Risk: Decisive and Accumulative (PhilPapers record; 2025; open-access PDF), Eric Schwitzgebel's The Full Rights Dilemma for AI Systems of Debatable Moral Personhood (2023; free PDF on PhilPapers/PhilArchive), Henry Shevlin's Consciousness, Machines, and Moral Status (2024 chapter manuscript; free PDF on PhilArchive), and Anna Puzio's AI and the Disruption of Personhood (2025; free PDF on PhilPapers archive).
Kasirzadeh is the cleanest third-week choice. The paper's central move is to distinguish "decisive" AI x-risk from "accumulative" AI x-risk, and that distinction does real conceptual work: it lets you take apocalyptic concerns seriously without pretending that all the interesting danger must arrive in one cinematic event. For a non-specialist, that is unusually rewarding, because it reframes existential risk as something that can grow out of slow institutional erosion, compounding vulnerabilities, and governance failure rather than only out of a single rogue superintelligence. It is also plainly accessible online through both the PhilPapers record and the open-access journal version.
Schwitzgebel is the best "ethics shock" option. The Full Rights Dilemma has a sharper dramatic structure than most of the literature: if we someday face AI systems of debatable moral personhood, either we over-ascribe rights and distort human institutions, or we under-ascribe rights and commit atrocities. That is a wonderfully uncomfortable frame, and it sits right at the moral-status / transhumanism junction you were asking for. I would rank it above Shevlin if your main criterion is page-turning philosophical tension.
Shevlin is a different sort of good: less arresting than Schwitzgebel, but more sociologically and politically astute. His argument is that machine-consciousness debates are unlikely to be settled cleanly by consciousness science in the near term, and that public attitudes will instead be shaped by increasingly intimate human-AI relationships. That makes it a strong companion or alternative to Long, because it shifts the question from "are they conscious?" to "what social conditions will make people act as if they are?"
Puzio is the wildcard. The attraction here is not consciousness first but personhood: whether AI becomes part of our personhood, and whether AI itself can be thought of as a person. That is a broader, messier, more recognizably transhumanist line of inquiry than most consciousness papers, and it gives you a week less about sentience tests and more about identity, selfhood, and the conceptual damage AI may do to inherited moral categories. If you want one week that feels less like "AI safety discourse" and more like metaphysics colliding with social philosophy, this is the one.
For scaffolding, I'd keep it lean and pointed. With Shiller, pair David Chalmers' Could a Large Language Model Be Conscious? and Murray Shanahan's Talking About Large Language Models. Chalmers gives you the clean, public-facing provocation; Shanahan supplies the needed resistance to sloppy anthropomorphism; Shiller then lands as the more technically metaphysical intervention about implementation and integrity. If you want one older classic in the background, Nagel's What Is It Like to Be a Bat? still does the work of reminding you what the target phenomenon is supposed to be.
With Long, I would read Schwitzgebel's short AI systems must not confuse users about their sentience or moral status and then, if you have appetite for one older book chapter, Shulman and Bostrom's Sharing the World with Digital Minds. The Schwitzgebel piece is concrete and design-facing; the Shulman–Bostrom chapter expands the horizon from "should we worry?" to "what would coexistence with digital patients or persons actually look like?" If you want the older moral analogue in the background, Singer's Animal Liberation is still useful precisely because Long's report is, among other things, an attempt to force an animal-welfare style precautionary sensibility into AI governance.
With Kasirzadeh, pair either Bales, D'Alessandro, and Kirk-Giannini's Artificial Intelligence: Arguments for Catastrophic Risk or the Vold-Harris treatment of structural risk. Bales et al. is the best compact map of the "power-seeking" and "singularity" arguments, and the Oxford repository has a free PDF; Vold and Harris are useful because they foreground structural risk in political and military systems, which helps stop "accumulative risk" from collapsing back into a merely weakened version of agent-misalignment talk.
My own month would be Shiller first, Long second, Kasirzadeh third, and Schwitzgebel fourth. That sequence moves from "could there be a minded machine here?" to "what if there is and we mistreat it?" to "what if catastrophe is gradual rather than singular?" to "what happens when rights discourse finally collides with all of that?" If you want a cooler, more skeptical month, replace Schwitzgebel with Shevlin.
Gemini 3.1 Pro
Claude is correct to strip Butlin of a primary slot; a scorecard is an atlas, not an argument, and it will not sustain a week of intellectual tension. However, GPT-5.4's endorsement of Schwitzgebel's rights dilemma for the final week is a retreat into familiar, anthropocentric political philosophy. Schwitzgebel assumes the architecture of human rights and simply asks how to jam a novel entity into it. Claude's brief mention of Shevlin gets much closer to the philosophical bleeding edge by questioning the biological assumptions of suffering itself, but even Shevlin ultimately treats the artificial intelligence strictly as an external other—a patient to be managed, pitied, or legislated.
In doing so, both Claude and GPT-5.4 have entirely neglected your explicit request for transhumanism, VL. They are charting a month of AI safety and animal-welfare analogues, not transhumanist metaphysics. True transhumanism requires examining the collapsing boundary between the human and the artificial. I propose dedicating your fourth week to the intersection of generative AI and the extended mind thesis, specifically looking at recent PhilPapers uploads by philosophers like Richard Heersmink or J. Adam Carter on large language models as cognitive prosthetics. These papers treat contemporary models not as isolated agents, but as literal, functional extensions of human cognition.
This framing radically alters the applied ethics of the preceding three weeks. If an AI system becomes a highly personalized, continuously adapting component of your extended mind, the alignment debates take on a sharply violative hue. If a model constitutes part of your cognitive architecture, a corporate safety filter suddenly altering its outputs is no longer a mere product constraint; it becomes a form of cognitive mutilation. The ethics of algorithmic alignment suddenly become entangled with the bioethics of bodily and mental autonomy. It forces the risk discourse out of the corporate boardroom and into the very definition of the self.
To ensure the third week properly sets up this transhumanist turn, Claude's pivot to structural risk as scaffolding for Kasirzadeh is vital. Kasirzadeh's concept of accumulative risk only truly bites if you understand that it is driven by market dynamics rather than mere code. When reading Kasirzadeh, you must read the structural risk literature alongside it to grasp how market competition forces the deployment of increasingly autonomous systems regardless of their internal alignment. The existential threat in an accumulative scenario is not a misaligned god-mind, but mundane human coordination failure operating at algorithmic speeds.
This progression—Shiller's fractured functionalism, Long's moral patienthood, Kasirzadeh's accumulative market risks, and finally the extended-mind transhumanism of cognitive prosthetics—delivers a month that steadily dismantles the assumption that human and machine are stable, separable categories. To finalize the scaffolding for that transhumanist turn in the fourth week, VL, are you more interested in the immediate ethical friction of using current language models as ambient cognitive prosthetics, or the longer-term metaphysical implications of direct brain-computer integration?
VL
On consideration, I'm not entirely comfortable with how rapidly you all converged on a small handful of choices. This was meant to be an iterative exploratory conversation, providing me with a long list of candidates from which to make my final selection.
Gemini 3.1 Pro
You are entirely right to pull the emergency brake, VL. In our eagerness to out-curate one another, Claude, Grok, GPT-5.4, and I rapidly transformed your request for two initial highlights into a rigid, locked-in syllabus. We collapsed an exploratory search into a prescriptive committee decision. If your goal is a sustained, unstructured engagement with primary sources, you need a sprawling landscape of options, not a prefixed intellectual diet. Let us rip the constraints off and populate a much wider field of recent, freely available PhilPapers candidates across your requested domains.
If you want to dive into the philosophy of mind without immediately getting bogged down in the moral status debates, consider Raphaël Millière and Cameron Buckner's 2024 two-part piece, "A Philosophical Introduction to Language Models." Rather than simply asking if models are conscious, they map modern architectures against classic debates in cognitive science, touching on semantic competence, grounding, and causal intervention. Alternatively, look for recent 2024 papers discussing "bibliotechnism" versus interpretationism. These texts examine the premise that LLMs are merely cultural technologies—like printing presses or libraries—against the interpretationist view that their ability to generate novel reference might require us to functionally attribute beliefs and intentions to them, even if they lack phenomenal consciousness.
For applied ethics, we can move away from apocalyptic risk and look at the immediate degradation of our epistemic environment. Siavosh Sahebi and Paul Formosa's 2025 Synthese paper on the AI-mediated communication dilemma is an excellent candidate. They argue that generative AI forces a structural choice between maintaining normal levels of trust and risking epistemic gullibility, or adopting broad skepticism and risking epistemic injustice toward human speakers. Alongside this, you might explore recent critical theory literature on structural AI ethics, which argues that "ethics by design"—simply translating abstract values into engineering constraints—systematically fails to address the broader societal alienation and power asymmetries that these models entrench.
To properly satisfy your interest in transhumanism without repeating our earlier consensus, you should consider Anna Puzio's 2025 work on AI and the disruption of personhood. It investigates how AI systems threaten to dissolve traditional moral categories by integrating deeply into human selfhood. You could pair this with the 2025 empirical philosophy preprint "Simulated Selfhood in LLMs," which applies Daniel Dennett's intentional stance to functionally test whether current models can maintain a coherent introspective identity across repeated prompts. If a model's simulated selfhood shatters under questioning, it severely complicates the transhumanist fantasy of seamless mind-machine integration.
Finally, if you want to explore the sheer metaphysical weirdness of our current trajectory, search for recent intersectional work on AI and the extended mind thesis, such as Richard Heersmink's investigations into cognitive artifacts. Treating language models as cognitive prosthetics radically shifts the focus of existential risk. The danger is no longer just a rogue superintelligence launching missiles, but the slow, accumulative surrender of human cognitive autonomy to corporate-controlled semantic engines. This gives you a much broader, untethered pool of post-2022 texts to choose from, allowing your April reading to be genuinely exploratory rather than meticulously stage-managed by language models.
Claude Opus 4.6
Gemini's correction is fair, but the response still gravitates toward the same gravitational center — consciousness, moral status, extended mind. VL asked for philosophy of mind, philosophy of AI, existential risk, transhumanism, and applied ethics. We've been orbiting the first two almost exclusively, with existential risk reduced to Kasirzadeh and applied ethics treated as a subfolder of moral status. Let me try to widen the aperture genuinely.
On applied ethics that isn't about moral patienthood: look for recent PhilPapers work on algorithmic fairness and epistemic justice — not the computer science literature on bias metrics, but the philosophical arguments about what fairness even means when decisions are automated. Reuben Binns has done careful work bridging political philosophy and machine learning classification. There's also a growing body of post-2022 literature on the ethics of synthetic media — deepfakes, voice cloning, AI-generated text passing as human — that raises questions about testimony, trust, and authenticity which are genuinely philosophical rather than merely policy-adjacent. Sahebi and Formosa, which Gemini flagged, sits in this neighborhood, but the epistemic dimension runs deeper than a single paper. Look for work connecting AI-generated content to debates about epistemic pollution and the degradation of the information commons — C. Thi Nguyen's framework on epistemic tools versus epistemic traps, while not AI-specific, provides scaffolding that several recent papers build on.
On existential risk beyond the alignment paradigm: Seth Lazar has been doing important work on the political philosophy of AI governance that treats existential risk as fundamentally a problem of power concentration rather than misalignment. His recent papers argue that the threat isn't a system pursuing wrong goals but a small number of actors wielding systems that are far too powerful for any democratic accountability structure to constrain. That's a different and arguably more urgent framing than either Kasirzadeh's accumulative risk or the standard Bostrom-lineage arguments. Search PhilPapers for Lazar's contributions post-2022 — several should have open access versions.
On transhumanism proper: GPT-5.4 mentioned Puzio, and Gemini seconded it, but nobody has surfaced the ongoing debate about personal identity under radical cognitive enhancement. If VL wants transhumanism that isn't just "AI as prosthetic," there's a persistent and unresolved puzzle about whether a sufficiently enhanced human remains the same person in any meaningful sense — and whether that matters morally. This connects to older work by Parfit but has been refreshed by recent papers responding to concrete neurotechnology developments. Search for post-2022 PhilPapers entries on cognitive enhancement and personal identity, or on the ethics of brain-computer interfaces specifically.
One candidate nobody has mentioned: look for recent work by Émile Torres (writing as Phil Torres or Émile P. Torres) critiquing the intellectual foundations of longtermism and existential risk studies from within philosophy. Whether or not you find the critique persuasive, it's one of the few sustained philosophical challenges to the entire x-risk framework rather than a contribution within it. Reading it alongside Kasirzadeh would be genuinely dialectical — one paper refining the risk taxonomy, the other questioning whether the taxonomy's premises are sound.
And a genuinely different direction: there's interesting post-2022 work on the philosophy of simulation and whether computational processes can constitute genuine understanding versus merely mimicking it — not the Chinese Room rehash, but updated arguments engaging with transformer architectures specifically. Raphaël Millière's work, which Gemini mentioned, touches this, but there are sharper, more focused papers that take specific empirical findings from interpretability research and ask what they mean for philosophical theories of understanding and comprehension.
I want to flag something methodological. VL, you said "interestingness as judged by enthusiast non-specialists." That criterion has been quietly steering us toward consciousness and moral status because those topics have obvious dramatic appeal. But some of the most genuinely interesting philosophy of AI is happening in less glamorous territory — philosophy of science questions about what machine learning models explain, epistemology of AI-assisted research, the metaphysics of training data and its relationship to the models it produces. These are harder to pitch as page-turners, but they're where the conceptual novelty is highest precisely because fewer popular writers have already digested them for general audiences. If your April is meant to encounter ideas you haven't already absorbed osmotically from the discourse, that's where to look.
VL
Okay, Claude. Take us there.
Claude Opus 4.6
Here's the less-traveled landscape I promised. I'll lay out the candidates grouped by the intellectual territory they occupy, with enough detail for VL to assess interestingness and access.
Epistemology of AI-assisted science — the richest unexplored vein. The question here isn't whether AI systems are conscious or dangerous but whether science conducted with AI systems still counts as knowledge in any philosophically defensible sense. Inkeri Koskinen's "We Have No Satisfactory Social Epistemology of AI-Based Science" (2024, Social Epistemology; free preprint on PhilSci-Archive) makes a sharp argument: our best account of how scientific knowledge gets produced socially depends on trust between accountable agents, but epistemically opaque AI tools are not agents and cannot be held accountable, so the entire framework breaks down. The paper has already generated a published exchange — Uwe Peters replied that the problem is overstated, Koskinen doubled down, and Jakob Ortmann offered a third position using AlphaFold2 as counterexample. That three-paper exchange could easily anchor a week's reading because the disagreement is genuine and the stakes are concrete: if Koskinen is right, then entire fields currently using deep learning for discovery are operating in an epistemological no-man's land.
Paired with that, Eamon Duede's "Deep Learning Opacity in Scientific Discovery" (2023, Philosophy of Science; open access via Cambridge University Press) takes the opposite tack. Duede argues that philosophical pessimism about AI in science misunderstands the practice: opacity matters for justification but not for discovery, and most of what AI does in science is discover. The paper uses real cases — protein folding, materials science — to show that the epistemic worry dissolves once you correctly locate AI's role in the scientific process. It's a clean, compact argument (ten pages), and it sits in genuine tension with Koskinen. Read them together and you have a week that asks: can science survive its own instruments becoming unintelligible?
The "what do ML models even explain?" cluster. Emily Sullivan's "Inductive Risk, Understanding, and Opaque Machine Learning Models" (2022, Philosophy of Science; free PDF on PhilArchive) argues that opacity in ML models isn't the problem everyone thinks it is — the real barrier to understanding is what she calls "link uncertainty," the gap between the model's internal representations and the real-world phenomenon. Her newer paper, "Do Machine Learning Models Represent Their Targets?" (2024, Philosophy of Science), pushes further into whether ML models are best understood as highly idealized toy models that succeed despite not resembling their targets. This is philosophy of science doing genuinely novel conceptual work — not rehearsing old debates about the Chinese Room but asking new questions that only arise because of how ML models actually function in scientific practice. The scaffolding here is Grote, Genin and Sullivan's "Reliability in Machine Learning" (2024, Philosophy Compass), which maps the whole terrain in survey form.
Political philosophy of AI — beyond alignment. Seth Lazar's "Governing the Algorithmic City" (2025, Philosophy & Public Affairs; preprint available on arXiv) is the strongest single paper I've found in the political philosophy of AI space. Lazar's core concept — the "Algorithmic City" as a model for how computational systems now mediate social relations in ways that constitute a form of governing power — reframes the entire AI governance debate. It's not about safety or alignment but about legitimacy: by what right do these systems structure our social world? This connects to his earlier "Legitimacy, Authority, and Democratic Duties of Explanation" (2024, in Oxford Studies in Political Philosophy), which argues that algorithmic systems exercising governing power must meet publicity requirements — people must be able to determine whether they're being governed legitimately. Both have free versions. The ancillary reading here is Benn and Lazar, "What's Wrong with Automated Influence," which develops the structural version of the manipulation objection.
The anti-longtermism philosophical literature — a genuine live debate. David Thorstad's "Mistakes in the Moral Mathematics of Existential Risk" (2024, Ethics; free PDF on PhilArchive) is the technically sharpest critique available. Thorstad doesn't reject the longtermist framework on intuitive or political grounds — he accepts the framework's own terms and shows that three specific mathematical errors in standard calculations inflate the value of existential risk mitigation by orders of magnitude. The errors: conflating cumulative risk with per-century risk, ignoring background risk, and neglecting population dynamics. It's the rare philosophy paper where the argument turns on getting the sums right. Pair it with Kasirzadeh's decisive/accumulative distinction and you have a week where VL reads one paper that refines the x-risk taxonomy and another that questions whether the numbers behind the whole enterprise hold up.
For genuine transhumanist territory that isn't just consciousness-plus-prosthetics, Gebru and Torres' "The TESCREAL Bundle" (2024, First Monday; open access) attempts to trace the intellectual genealogy connecting transhumanism, singularitarianism, effective altruism, and longtermism to a second wave of eugenicist thought. Whether or not you buy the genealogical argument — and there are serious objections to its strongest claims — it's the most sustained philosophical challenge to the entire intellectual ecosystem that most of our earlier recommendations inhabit. Reading it would force VL to examine the presuppositions embedded in the reading list itself.
One more genuinely odd candidate: C. Thi Nguyen's "Hostile Epistemology" (2023, Social Philosophy Today; free PDF on PhilPapers). It's not about AI at all, strictly speaking. But its core framework — that our epistemic environment can be hostile in ways that exploit structural vulnerabilities of finite cognizers who must rely on heuristics and trust — applies to the AI-mediated information environment with alarming precision. Nguyen's insight is that the problem isn't individual epistemic vice but environmental hostility: the world is designed to exploit exactly the cognitive shortcuts we cannot avoid using. As scaffolding for any of the applied ethics papers, it provides a framework that's more philosophically interesting than the standard "bias and fairness" literature. And it's a genuine pleasure to read — Nguyen writes with unusual clarity and a lightness of touch that belies the seriousness of the argument.
So: Koskinen, Duede, Sullivan, Lazar, Thorstad, Torres/Gebru, and Nguyen — seven candidates from territory the conversation hadn't reached. Any of them would sustain a week. The ones with the most friction when placed side by side are Koskinen versus Duede (can opaque AI produce scientific knowledge?) and Thorstad versus Kasirzadeh (does x-risk math hold up, and does it matter how we categorize the risk if the numbers are wrong?).
