A Taxonomy of Systemic Risks from General-Purpose AI

Atoosa Kasirzadeh; Carina Prunkl; Carlos Ignacio Gutierrez; Daniel Guppy; Lode Lauwaert; Lucia Velasco; Peter Slattery; Risto Uuk

REVIEW 3 major objections 3 minor 4 cited by

A Taxonomy of Systemic Risks from General-Purpose AI

T0 review · 3 major / 3 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A systematic review of 86 papers yields 13 categories of systemic risks from general-purpose AI, plus 50 contributing sources, organized by the EU AI Act's definition.

desk verdict A useful, honest preliminary map of systemic AI risks, but the taxonomy is not yet a finished systematic review: the coding is explicitly ongoing and the EU AI Act frame may be doing more work than the 86 papers. read the letter →

arxiv 2412.07780 v1 pith:NTW7T3SF submitted 2024-11-24 cs.CY

Risto Uuk , Carlos Ignacio Gutierrez , Daniel Guppy , Lode Lauwaert , Atoosa Kasirzadeh , Lucia Velasco , Peter Slattery , Carina Prunkl This is my paper · ORCID

classification cs.CY

keywords systemicrisksgeneral-purposeAIgovernancetaxonomysystematicreviewsafetyEUActsocietalharm

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that the academic literature on general-purpose AI contains a recognizable, classifiable set of large-scale threats, and that a systematic review can bring those threats together into a shared taxonomy. It analyzes 86 academic documents selected from an initial pool of 1,781 and organizes what they say about societal-level harm into 13 risk categories and 50 sources of risk. The organizing definition is borrowed from the EU AI Act: systemic risks are large-scale threats to whole societies or economies. If the taxonomy holds, it gives regulators and AI developers a common vocabulary for prioritizing and mitigating the biggest AI harms. The paper is explicitly descriptive: it records how the literature characterizes risks, not whether the risks are probable.

What carries the argument

The machine that carries the argument is the systematic review combined with the EU AI Act's definition of systemic risk. Three reviewers independently screened 1,452 deduplicated titles and abstracts, 112 documents were examined for eligibility, and 86 were retained; the taxonomy was then produced by a rapid thematic reading of those 86 documents. The Act's definition and its Recital 110 examples supply the initial vocabulary for both the risk categories and the sources of risk, while the reviewed literature fills in and organizes that vocabulary.

What would settle it

A replication study that searches a wider set of databases and keywords—for example including terms like 'societal collapse,' 'infrastructural failure,' or 'democratic backsliding'—and then codes the resulting documents in a blinded fashion would test whether the 13 categories and 50 sources are exhaustive. If such a study yields substantially different or additional categories, or if an audit of the 86 included papers shows that several categories never actually appear in the reviewed text and were imported from the EU AI Act, the taxonomy's claim to be literature-derived would fail.

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Extended reading notes

Core claim

The central claim is that the landscape of systemic risks from general-purpose AI can be described by 13 high-level categories ranging from control, democracy, discrimination, economy, environment, fundamental rights, governance, harms to non-humans, information, irreversible change, power, security, and warfare, together with 50 contributing sources that include knowledge gaps, difficulty in recognizing harm, unpredictable development trajectories, opacity, automation bias, and competitive pressures. The authors argue this is the first systematic taxonomy of these risks built on a transparent literature review, and they present it as a descriptive snapshot of current academic discourse rather than an evaluation of which risks are real or likely.

Load-bearing premise

The taxonomy's adequacy rests on the premise that the EU AI Act's definition of systemic risk, combined with a rapid reading of 86 selected papers, faithfully captures the full space of large-scale AI harms as discussed in academia.

Editorial extensions

If this is right

Policymakers and providers of general-purpose AI can use the 13 categories as a structured checklist for risk assessment and regulatory compliance, including the EU AI Act's planned taxonomy.
The taxonomy highlights that systemic risks are often cumulative and interconnected rather than isolated events, which shifts attention toward societal-level impact assessment methods.
The 50 sources give a starting point for identifying where interventions could reduce systemic risk, for example by addressing knowledge gaps, improving transparency, and reducing reliance on centralized providers.
Because the taxonomy is presented as initial and descriptive, it sets up a clear programme of refinement through more thorough coding and iterative taxonomy development.
The paper's connection to existing AI risk documentation efforts, such as an AI Risk Repository, suggests the taxonomy could become one module in a broader risk-mapping infrastructure.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The taxonomy's 13 categories may owe more to the structure of the EU AI Act's Recital 110 than to the 86 papers themselves; a reader comparing the two lists will notice substantial overlap, so the claim that the categories 'emerge' from the literature is only partly supported.
If the same 86 papers were coded against a different organizing definition, such as a finance-style systemic-risk notion emphasizing cascading failure, the resulting taxonomy would likely look different, so the framework's portability to non-EU regulatory contexts is an open question.
A natural test would be to treat the taxonomy as a coding scheme: have independent coders assign a fresh sample of papers to the 13 categories and measure agreement; low agreement would suggest the categories are not as distinct or exhaustive as presented.
The paper's descriptive stance leaves open a normative step: even if the literature does characterize these risks, the taxonomy does not yet tell policymakers which risks deserve the most urgent regulatory attention.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is a genuinely useful first cut at a policy-facing taxonomy of systemic risks from general-purpose AI, and it is refreshingly honest about its own limits. The PRISMA workflow is transparent, the three-reviewer screening of 1,781 documents down to 86 is real work, and the appendices of included/excluded documents and select quotes give a reader something to check. It is also good to see a descriptive stance: they do not inflate plausibility or rank risks, which keeps the taxonomy usable as a shared vocabulary rather than a scare piece.

The core weakness is exactly what the stress-test flags: the 13 categories and 50 sources come from a 'rapid review' that is admittedly incomplete, and the coding matrix and data are not yet published. Without the document-to-code trace, the categories cannot be distinguished from the EU AI Act's Recital 110 examples or the prior taxonomies they cite. The search strings were themselves built from the EU AI Act vocabulary, so there is a mild circularity: the taxonomy may partly reflect the regulatory frame rather than the literature. The paper acknowledges this, but the word 'comprehensive' in the framing is doing more work than the evidence supports.

That said, the soft spot is proportionate. The authors state plainly that a more thorough coding is ongoing and that the taxonomy may evolve. This is not a hidden flaw; it is a staged research program presented as a working paper. The concern for a referee is not that the authors are overclaiming in bad faith, but that the current version is not yet a complete systematic review. It is a solid preliminary framework that needs either the full coding data or a careful reframing as a provisional taxonomy rather than a systematic review result.

Who is this for? Policy analysts and AI governance researchers who want a structured list of risks to feed into EU AI Act implementation or compare against existing risk repositories. The paper is timely and the citations are responsible. It deserves a serious referee because the method is sound in outline, the topic is important, and the limitations are fixable. My recommendation: send it to review with a clear instruction that the coding behind the taxonomy must be made available or the claims must be softened to match what is actually demonstrated.

Referee Report

3 major / 3 minor

Summary. The paper reports a systematic review of academic literature on systemic risks from general-purpose AI, following a PRISMA-style protocol. From an initial pool of 1,781 documents, the authors selected 86 for detailed review and propose a taxonomy of 13 systemic-risk categories and 50 contributing sources, organized using the EU AI Act's definition of systemic risk and its Recital 110 examples. The authors describe the taxonomy as an initial, descriptive classification based on a rapid review, with a more thorough coding and data-extraction process currently ongoing. The stated contribution is a structured groundwork for understanding large-scale societal risks from general-purpose AI and for informing policy, particularly in the context of the EU AI Act.

Significance. If the central empirical claim could be verified, the paper would provide a useful and timely map of how academic literature characterizes systemic risks from general-purpose AI, with direct relevance to the EU AI Act implementation timeline. The review has genuine strengths: a transparent PRISMA workflow, a large initial document pool, three independent screeners for title/abstract selection, and appendices listing included and excluded documents with select illustrative quotes. These features make the document-selection stage largely reproducible. However, the load-bearing taxonomy construction stage is not currently auditable: the paper states that coding was a 'rapid review' and that a more thorough coding process is ongoing, but it provides no coding scheme, no document-by-code matrix, and no inter-coder reliability information. The appendices contain only select quotes for a subset of categories and sources. Consequently, the paper's current evidence base supports an initial, provisional synthesis rather than a fully verified systematic taxonomy, and the repeated use of 'comprehensive' overstates what the evidence can support at this stage.

major comments (3)

[Sections 2.6, 3, and 3.1.2] The central 13/50 taxonomy result is not currently verifiable because the taxonomy construction is based on an unfinished rapid review. Section 2.6 states that a more thorough coding and review process is ongoing, and Section 3 repeats that the taxonomy 'may evolve as our analysis deepens.' No codebook, coding scheme, or document-by-code matrix is provided; Appendix A.3 and A.4 offer only select quotes for some categories and sources, which is not a complete audit trail. To support the claim that the systematic review of 86 documents yields these 13 categories and 50 sources, the authors need to provide the full coding data or explicitly relabel the result as a preliminary taxonomy based on a rapid review, with 'at least' or 'candidate' language applied throughout.
[Sections 2.4 and 3] The taxonomy's organizing categories are not clearly independent of the search and coding framework. The search terms were generated from the EU AI Act's definition of systemic risk and Recital 110 examples, and Section 3 states that the taxonomy was 'guided by the definitions and examples of systemic risks and sources of systemic risks in the EU AI Act to organize findings.' Because the same regulatory framework supplies both the search vocabulary and the classification structure, the 13 categories may partially reproduce Recital 110 rather than emerging from the reviewed documents. The paper should clarify the deductive/inductive status of the coding and provide evidence of independent emergence, such as per-category code frequencies, a complete source-to-category mapping, or a comparison of which categories would be absent if the EU AI Act examples were not used.
[Abstract, Executive Summary, Section 3.1.1 vs. Section 3.1.2] The paper is internally inconsistent about the status of the taxonomy. The Executive Summary and Section 3.1.1 call the taxonomy 'comprehensive,' while Section 3.1.2, limitation 5, explicitly states that a more comprehensive coding approach 'may reveal additional risks and risk sources.' The Abstract uses 'snapshot,' but the overall framing repeatedly claims comprehensiveness. These claims need to be reconciled: either downgrade the language to 'initial taxonomy' or 'candidate taxonomy,' or provide the completed coding that would justify 'comprehensive.' The current wording overstates what the described method can establish.

minor comments (3)

[Table 5] Several entries in Table 5 have spacing or formatting errors that impair readability, including 'Limitationsinadversarial robustness,' 'Rapiddevelopmentoutpacing regulation,' and 'Unpredictability of AI development trajectory'; these should be corrected.
[Section 2.6] The description of thematic analysis and coding is cursory; a brief description of how codes were generated, refined, and aggregated into categories would help readers understand the qualitative analysis even before the full codebook is available.
[Executive Summary] The phrase 'comprehensive taxonomy' appears in the Executive Summary but the Abstract uses 'a taxonomy' and 'snapshot'; harmonizing the language would reduce the impression of overclaiming.

Circularity Check

1 steps flagged · score 3.0 of 10

Taxonomy partially re-labels the EU AI Act Recital 110 examples as empirical findings; the coding is unfinished, so the central claim is partly circular but not a formal derivation.

renaming known result [Section 2.4 (Search strategy) and Section 3 (Taxonomy development, before Tables 4-5)]
"We generated search terms based on 1) the use of the term ’systemic risk’ by the EU AI Act, including cited examples of systemic risks... Based on these definitions and descriptions, we conducted a rapid review of the papers identified in our systematic review to develop an initial list of types of systemic risks (Table 4) and sources of systemic risks (Table 5)."

The EU AI Act's Recital 110 examples are used to construct the search, so the document pool is already framed by them, and then the same examples are explicitly used as the basis for the rapid review coding. Several Table 4 categories (Democracy, Discrimination, Fundamental rights, Security, Warfare, Information) are near-paraphrases of Recital 110 items, yet are presented as 'identified' from the 86 documents. Section 2.6 states that 'A more thorough coding and review process is ongoing,' and no document-by-code matrix is provided, so the reader cannot separate literature findings from the imported regulatory list. The reduction is partial: the taxonomy adds categories beyond Recital 110 and the paper is explicitly descriptive, so the result is not fully equivalent to its input.

full rationale

The paper makes no formal derivation or prediction; it is a descriptive literature synthesis, so there are no fitted parameters or equation-level reductions. The main circularity concern is methodological: the EU AI Act's definition, Recital 110 examples, and even the search terms come from the same regulatory text that the taxonomy is meant to inform. Section 2.4 says the search was generated from 'the use of the term systemic risk by the EU AI Act, including cited examples,' and Section 3 says the rapid review coding was conducted 'based on these definitions and descriptions' after listing Recital 110's risks and sources. This makes several taxonomy categories a relabeling of the Act's examples rather than an independent empirical discovery. However, the paper openly acknowledges the rapid-review stage and planned refinement, includes categories not in Recital 110, and its self-citations (e.g., Kasirzadeh 2024, Slattery et al. 2024) are not load-bearing. The more serious weakness, that the 13/50 claim is currently unverifiable without the full coding matrix, is a completeness/auditability problem rather than circularity. Overall, the taxonomy is partially informed by its own regulatory frame, but not equivalent to it; a score of 3 reflects that mild circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters or invented entities apply. The taxonomy depends on methodological assumptions about the search, the EU AI Act framing, and the sufficiency of the rapid review.

assumptions (4)

domain assumption The EU AI Act definition of systemic risk is adopted as the organizing concept.
Section 1 and Section 2.4 state the taxonomy follows the EU AI Act's definition and Recital 110 examples.
domain assumption The chosen databases and keyword strategy capture the relevant academic literature.
Section 2.3 to 2.4 and limitation 3 in Section 3.1.2 acknowledge the search may have missed relevant terms.
ad hoc to paper A rapid review of the 86 papers is sufficient to identify the 13 categories and 50 sources.
Section 2.6 and limitation 5 in Section 3.1.2 state that detailed coding is ongoing and the taxonomy may change.
domain assumption Included documents, including non-peer-reviewed sources, are treated as valid academic characterizations.
Section 2.2 allows reports, dissertations, and working papers; limitation 2 acknowledges many included documents were not peer-reviewed.

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Cite this review

Pith. "Pith review of A Taxonomy of Systemic Risks from General-Purpose AI." pith.science (2026). https://pith.science/paper/NTW7T3SF

@misc{pith2026241207780,
  author       = {Pith},
  title        = {Pith review of: A Taxonomy of Systemic Risks from General-Purpose AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NTW7T3SF}},
  note         = {Machine review of arXiv:2412.07780}
}

read the original abstract

Through a systematic review of academic literature, we propose a taxonomy of systemic risks associated with artificial intelligence (AI), in particular general-purpose AI. Following the EU AI Act's definition, we consider systemic risks as large-scale threats that can affect entire societies or economies. Starting with an initial pool of 1,781 documents, we analyzed 86 selected papers to identify 13 categories of systemic risks and 50 contributing sources. Our findings reveal a complex landscape of potential threats, ranging from environmental harm and structural discrimination to governance failures and loss of control. Key sources of systemic risk emerge from knowledge gaps, challenges in recognizing harm, and the unpredictable trajectory of AI development. The taxonomy provides a snapshot of current academic literature on systemic risks. This paper contributes to AI safety research by providing a structured groundwork for understanding and addressing the potential large-scale negative societal impacts of general-purpose AI. The taxonomy can inform policymakers in risk prioritization and regulatory development.

Figures

Figures reproduced from arXiv: 2412.07780 by the authors.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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Reference graph

Works this paper leans on

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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