Spinoza’s Ethics offers a way to ask not only what an AI system produces, but how its outputs are formed, what people and institutions make of them, and how those relationships affect shared understanding and power. Recent scholarship applies that lens to lethal autonomous weapons, generative AI, and education. These are distinct arguments—not a single settled Spinozist position on AI, and not proof that today’s models are conscious.
What does Spinoza’s philosophy add to AI ethics?
Much AI ethics asks whether a system follows a rule, meets a standard, or produces a harmful result. A Spinozist approach can widen the inquiry: examine the causes that shape an output, how well people understand those causes, and how interactions with the system affect human judgment and collective life.
Two ideas from Spinoza’s Ethics are especially useful to the scholarship discussed here. First, an idea is adequate when it is understood through its causes and connections; an inadequate idea is partial or confused because those causes are not sufficiently grasped. Second, conatus refers to a thing’s striving to persist in its being. Applied to technology, these concepts are philosophical tools for analysis, not engineering measurements or proof that a machine has human-like intentions.
This framing also keeps two questions separate. Understanding what kind of thing an AI system is does not, by itself, tell us what laws or policies ought to govern it. Conversely, a policy decision may be necessary even when important questions about the system’s nature remain unsettled.
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How has Spinoza been applied to AI and weapons law?
What the legal analysis examines
In “AI and Spinoza: a review of law’s conceptual treatment of Lethal Autonomous Weapons Systems (LAWS),” Moa De Lucia Dahlbeck examines how legal discourse understands LAWS. The article was published online on 10 July 2020 and appeared in AI & SOCIETY, volume 36 (2021), pages 797–805.
Dahlbeck uses Spinoza’s philosophy of mind and knowledge to analyze the difficulty of settling what kind of object AI is in legal discussion. The article then turns to Spinoza’s political philosophy, which complicates any attempt to derive a normative process directly from metaphysical analysis. Its focus includes negotiations toward a new protocol to the Convention on Certain Conventional Weapons (CCW).
Why the distinction matters
The value of this approach is not that a metaphysical theory automatically supplies a weapons-law rule. It is that legal reasoning about LAWS depends partly on how the technology is conceptualized, while decisions about regulation also involve political judgment and institutions. Confusing those tasks can make a contested description of AI look like a ready-made ethical conclusion.
What does Spinoza’s epistemology say about generative AI?
Bodde and Burnside’s argument
Emerson Bodde and Andrew Burnside apply Spinoza’s naturalism, panpsychism, and account of knowledge to generative AI in “Vice and inadequacy: Spinoza’s naturalism and the mental life of generative artificial intelligence.” Published online on 7 October 2025, the article appears in AI & SOCIETY, volume 41 (2026), pages 2077–2091.
The authors argue that large language models (LLMs) have minds fundamentally similar to human minds, but that their ideas are broadly inadequate: they lack a comprehensive account of the causes by which those ideas are generated. They connect this interpretation to bias and socially harmful effects and propose policy responses. The claim that LLMs have minds is their philosophical position, not an established scientific finding or a consensus view about current models.
What “inadequate” does—and does not—mean here
In this argument, calling an idea inadequate is an epistemic judgment about how fully its causes are understood. It is not simply another word for false, and it does not mean that every generated sentence is wrong. Nor does the philosophical use of “mind” settle whether an LLM has consciousness or experience. Readers should keep the authors’ interpretation distinct from empirical studies they discuss; the available article description does not provide grounds here to repeat specific study results or numerical claims.
How the lens can guide a practical assessment
Used cautiously, the adequate/inadequate distinction encourages questions about both the system and the people relying on it. It shifts attention from the fluency of an answer to what is known about its production and how confidently users interpret it.
- What is the claim based on? Identify the system’s role and the information or process that produced the output, to the extent these are known.
- What remains opaque or partial? Separate documented facts from assumptions about why a particular result appeared.
- Who is affected by the result? Consider how outputs and their use shape people’s opportunities, relationships, and understanding.
- What response is justified? Match a policy or practice to the specific risks and institutional setting rather than treating “AI” as one uniform problem.
These questions are an application of the philosophical lens, not a substitute for technical evaluation, legal analysis, or evidence about a particular system.
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A 2025 article in TECCOGS: Revista Digital de Tecnologias Cognitivas, “Artificial Intelligence and the Ethical Labyrinth in Education: Lessons from Spinoza and Black Mirror,” proposes a hybrid problem-based learning (PBL) model involving AI. Its stated aim is to bring fiction and experience closer together while stimulating ethical analysis and critical thinking.
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The proposal makes education another site of AI ethics: students can examine not only what a tool can do, but how its use shapes judgment and social expectations. The available description presents a learning-model proposal, not evidence that the model has been tested or shown to improve outcomes.
How do these Spinozist approaches differ?
The approaches share an interest in causes, understanding, and social consequences, but they address different objects and make different kinds of claims.
| Approach | Main object | Ethical focus | What the claim supports |
|---|---|---|---|
| Dahlbeck on LAWS | Legal concepts of AI and weapons autonomy | How legal understanding relates to political norm-setting and CCW negotiations | A conceptual analysis of law and political process, not a universal rule for AI regulation |
| Bodde and Burnside on LLMs | The epistemic character of generative AI ideas | Adequacy, causal understanding, bias, and harmful social effects | A contestable philosophical interpretation, not proof of machine consciousness |
| Ferrari, Pinto, and Couto on education | AI in an educational learning model | Ethical analysis and critical thinking through hybrid PBL | A proposal, not a demonstrated outcome |
Is there a broader Spinozist alternative to AI alignment?
A ResearchGate record for Tolga Theo Yalur’s “Beyond alignment: algorithmic conatus and the spinozist ethics of composition” reports a June 2026 publication in AI and Ethics. The description presents an alternative emphasis: assess whether encounters with an algorithm increase collective power and reason or decompose them, rather than relying only on alignment with an external standard.
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Where can you start reading Spinoza?
Spinoza’s Ethics uses a geometrical method and can be difficult to approach without context. Oxford Academic lists Spinoza’s Ethics: A Guide, whose introduction supplies background on that method, Spinoza’s biography, and philosophical predecessors. It is an optional secondary guide for readers who want context before or alongside the primary text.
What should readers take away?
Spinoza’s Ethics does not deliver a ready-made code for AI. Its value in the scholarship here is as a set of questions: what causes and relations shape an output, how adequate our understanding is, and what effects the technology and its institutions have on shared human life. The answers vary by setting, and philosophical interpretation should not be mistaken for empirical proof or policy consensus.
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