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REVIEW 3 major objections 3 minor 63 references

The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?

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

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Pith's one-line read Deep learning revives pseudosciences by mistaking correlation for causation

desk verdict A coherent and readable position essay arguing that ML can revive pseudoscience by treating correlations as causal, but its central harm estimate overreaches by equating false-positive flags with wrongful convictions; the qualitative thesis survives the flaw. read the letter →

arxiv 2411.18656 v1 pith:R5N3L2CN submitted 2024-11-27 stat.ML cs.AIcs.LG

Jérémie Sublime This is my paper · ORCID
classification stat.MLcs.AIcs.LG
keywords correlationvscausationdeeplearningethicspseudosciencerevivalphysiognomyLombrosianismalgorithmicharmqualitymetricstheory-freeAI
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 argues that machine learning and deep learning systems, despite their high accuracy, routinely confuse correlation with causation, and that this confusion effectively resurrects pseudosciences such as physiognomy, Lombrosianism, and social astrology. The author contends that the primary ethical danger of AI is not just biased training data but the models themselves, which implicitly treat input features as causal variables. Using examples from justice, surveillance, and facial analysis, the paper claims that even well-intentioned bias-reduction efforts are insufficient. The paper also shows that standard quality metrics like accuracy and recall hide the harm caused by false positives, and it quantifies this harm in four major cities. The conclusion is that safe AI requires rethinking model design, adopting harm-sensitive metrics, and keeping human oversight.

What carries the argument

The central mechanism is the implicit reversal of causal direction in deep learning inference: models are trained to map features to labels, so they treat symptoms, facial traits, or background data as causes of the predicted outcome, whereas in reality the direction of causation may be opposite or absent. The paper also uses a simple algebraic argument with classification metrics (accuracy, recall, precision) to show how false positive counts scale with population size, crime rate, and model precision, giving concrete estimates of harm for four cities. The 'theory-free' myth—the idea that data-driven models are value-free and unbiased—is identified as the ideological support that legitimizes these pseudoscientific applications.

What would settle it

A concrete test would be to examine the actual false positive and conviction rates of deployed AI systems like OASys or similar risk-assessment tools in the UK or US, comparing the number of false positives to the number of wrongful convictions actually caused by these systems. If the conviction rate from automated flags is negligible, the paper's quantitative harm claims would be overstated.

Watch

Extended reading notes

Core claim

The central claim is that current machine learning and deep learning methods, because they are fundamentally inductive statistical tools, implicitly infer causation from correlation: input variables become de facto causal variables in their decision processes. This implicit causation, combined with the neglect of domain knowledge and historical context, revives discredited pseudosciences such as physiognomy, Lombrosianism, and social astrology. The paper further argues that the 'theory-free' ideal of data-driven AI is a fallacy, that bias removal from training data cannot achieve fairness because all data are biased and models can reconstruct sensitive features, and that the prevailing emphasis on accuracy and recall metrics obscures the real social harm of false positives, particularly in criminal justice and security applications. The paper demonstrates this harm with a quantitative simulation for London, Beijing, Hyderabad, and New York, showing that even at 95% accuracy, precision, and recall, thousands of false positive identifications would occur, potentially leading to thousands of wrongful convictions.

Load-bearing premise

The quantitative harm estimates assume that a false positive from an AI system, such as a CCTV flag or risk-assessment score, is equivalent to a wrongful conviction, without evidence that an automated flag leads to a legal conviction.

Editorial extensions

If this is right

  • If the paper is right, high-accuracy AI systems deployed at scale in policing and justice will produce large absolute numbers of false positives, potentially leading to wrongful arrests or convictions, even in the absence of deliberate bias.
  • Fairness interventions that only curate training data or remove sensitive features will fail to prevent harm, because models can reconstruct protected attributes from correlated features and because bias is inherent to the application itself.
  • Evaluation practices need to shift from accuracy and recall toward precision and specificity, and toward metrics that explicitly capture the social cost of false positives, especially in high-stakes domains.
  • AI systems should not be treated as oracles that replace domain experts; human oversight and integration of domain knowledge are necessary to avoid repeating historical errors of pseudoscience.
  • The revival of physiognomy and Lombrosianism through facial-analysis and criminal-prediction AI is not a marginal curiosity but a systemic risk that demands a rethinking of core model design, not just data curation.

Reading between the lines

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

  • The paper's harm simulation could be extended to a formal analysis of how precision and recall trade-offs affect false positive counts under different base rates; the author's assumption of equal accuracy, precision, and recall is conservative, and real systems with lower precision would show even larger harms.
  • The argument suggests a concrete testable hypothesis: if deep learning models are indeed treating features as causes, then interventions that change the causal structure of the data (e.g., do-calculus style manipulations) should change model predictions in ways that are inconsistent with purely correlational learning.
  • The paper's critique of 'theory-free' AI could be connected to the broader debate on whether AI explainability methods can recover causal structure; one could test whether post-hoc explanation tools actually reveal causal mechanisms or merely rationalize correlations.
  • The author's conclusion implies that regulatory frameworks should mandate not only fairness audits but also harm-based impact assessments that quantify false positive rates in the deployment context, not just in the training distribution.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 3 minor

Summary. This position paper argues that modern machine learning and deep learning systems, by relying on correlational patterns without causal understanding, risk reviving pseudoscientific practices such as physiognomy, Lombrosianism, and social astrology. It surveys controversial applications in justice, surveillance, and profiling; critiques the 'theory-free' ideal and the sufficiency of bias-reduced training data; and presents a quantitative simulation in Section 4.2 that estimates false-positive counts for hypothetical crime-prediction systems in four cities. The paper concludes that high-accuracy models can cause large-scale harm and calls for rethinking model design, using harm-relevant metrics, and maintaining human oversight.

Significance. The paper addresses an important and timely topic: the historical continuity between discredited pseudosciences and some contemporary AI applications. Its central conceptual claim—that high accuracy on correlational tasks does not justify causal or high-stakes decisions—is sound and well-aligned with existing critiques in the ML fairness and causality literature. The paper usefully catalogs problematic applications and clearly explains why accuracy and recall alone are inadequate for evaluating harm. The quantitative section, however, contains a load-bearing overstatement that currently undermines the paper's credibility, as it equates false-positive flags with wrongful convictions without any evidence of the intervening legal process. If that overstatement is corrected, the paper could serve as an effective perspective piece for a broad ML audience.

major comments (3)
  1. [§4.2, Table 3] The statement that London 'would potentially feature 4800 to 9600 wrongly convicted people' equates a false-positive classification with a wrongful conviction. The computation in Table 3 counts distinct individuals flagged by a one-time hypothetical screening under assumed accuracy, precision, and recall; it does not count arrests, prosecutions, or convictions. No evidence is provided that a false flag from a CCTV or risk-assessment system would lead to conviction, especially since the cited deployed systems (e.g., OASys) are advisory risk scores feeding human decisions. This leap is load-bearing because the paper uses these numbers to argue that high-accuracy models 'would be a serious danger if implemented at large scale' and to justify a 'complete rethinking' of model design. The text should be revised to say 'wrongly flagged individuals' or 'false positive identifications,' and the downstream decision chain should be discussed explicitly.
  2. [§4.2, Eqs. (6)–(7), Table 3] The simulation's headline numbers are highly sensitive to assumptions that are not defended. First, the crime rate estimates are taken from unspecified 'estimates available online' and vary by two orders of magnitude across cities (0.56 to 93 per 1000), which directly drives the false-positive counts. Second, the assumption that accuracy, precision, and recall are equal is acknowledged but not varied independently, even though real classifiers often have trade-offs between precision and recall; lower precision at fixed accuracy would sharply increase false positives. Third, the 'Number of misclassified people' rows are trivially N*(1-accuracy) and do not inform harm. The paper should either provide a sensitivity analysis over realistic ranges or clearly state that the table illustrates a hypothetical scenario, not a prediction.
  3. [§3.1] The claim that 'input variables are de facto causal variables for Machine Learning Models to make their decisions' is imprecise and overstates the matter. A discriminative classifier learns a function f(x) that maps features to labels; it does not itself assert a causal relation. The causal misattribution typically occurs when designers or users interpret the model's predictions as causal or when they choose features based on causal assumptions. The paper's argument would be more rigorous if it distinguished between the model's statistical dependence and the human interpretation or deployment that turns correlation into an implicit causal claim. This distinction is central to the 'correlation does not imply causation' thesis and should be clarified.
minor comments (3)
  1. [Throughout] There are numerous typographical errors and missing words: 'real-wold' (abstract), 'and orientation' should be 'and sexual orientation' (Section 1), 'ssystems' (Section 2), 'countries countries' (Section 2), 'a,d' should be 'and' (Section 3.2), 'bow' should be 'now' (Section 3.2), 'mitiate' should be 'mitigate' (Section 4.1), 'Mister Lombroso' should be 'Lombroso' (Section 3.2), 'ethic courses' should be 'ethics courses' (Section 6), and 'humans oversight' should be 'human oversight' (Section 6).
  2. [Section 5] The citation placeholder '[61 ? ]' appears in the sentence 'biases have shown to be a problem [61 ? ]'; this should be resolved to a proper reference or removed.
  3. [References] Some references have malformed URLs or missing publisher locations (e.g., [6], [44], [45]); these should be cleaned up for publication.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found; simulated false-positive table is arithmetic from stated assumptions, not a fitted prediction.

full rationale

The paper's load-bearing argument is philosophical: deep learning models exploit correlations and are misread as causal, while accuracy/recall metrics obscure false-positive harm. Section 4.2 computes false-positive counts via Equations (6)-(7) from explicitly assumed accuracy, precision, recall, and crime-rate estimates; it fits no parameter to data and does not present Table 3 as an empirical prediction. The 'wrongly convicted' phrasing in Section 4.2 exceeds the model—a false-positive flag is not a conviction—but that is an unsupported inference or overstatement, not circularity: the number 4800-9600 is definitionally the FP count, not the output of a derivation that presumes the conclusion. The paper contains no load-bearing self-citations; references [49] and [53] on the theory-free ideal are external works by other authors, and the argument against data curation is supported by cited examples and literature rather than by the simulation. The central claims about correlation-versus-causation and metric inadequacy are asserted and illustrated, not derived from a fitted input, so the derivation chain is self-contained. The only flagged weakness is the unexamined causal chain from automated flag to wrongful conviction, which is a missing-support concern, not a circular reduction.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper's quantitative illustration depends on assumed metric levels, unverified crime rates, and an assumed chain from misclassification to conviction. These assumptions are presented as limitations but are load-bearing for the magnitude claims. The conceptual argument rests on the standard statistical principle that correlation does not imply causation.

free parameters (3)
  • Assumed accuracy, recall, and precision levels = 90%, 95%, 99%
    Section 4.2 tests equal values for all three metrics across three levels; chosen by hand as plausible scenarios, not estimated from a real system.
  • Crime rate estimates per city = London 1-2%, Beijing 0.1-0.2%, Hyderabad 0.2-0.5%, New York 1-2%
    Section 4.2 says estimates are 'available online' and acknowledges they are not ideal; false-positive counts scale linearly with these rates.
  • Population and CCTV exposure figures = Populations and camera counts in Table 3
    Contextual inputs from online sources without explicit citations; used to frame the scale of the illustration.
assumptions (5)
  • domain assumption Accuracy, precision, and recall can be treated as equal and known for the simulated AI systems
    Stated in Section 4.2; the paper acknowledges this is rarely the case for real systems.
  • domain assumption The proportion of criminals in the population can be estimated from public crime rates
    Section 4.2 acknowledges this approach is not entirely accurate.
  • domain assumption Each person is tested once and the AI is consistent across repeated observations
    Stated in Section 4.2; the paper notes that if violated, false positives could be higher.
  • domain assumption A false positive flag in such systems leads to wrongful conviction or other life-altering harm
    Implied in Section 4.2 when Table 3 is summarized as 'wrongly convicted people'; no evidence for this causal chain is provided.
  • standard math Correlation does not imply causation is the governing statistical principle for assessing ML inference
    Core premise of the paper, stated in the abstract and used throughout to argue that pattern-based predictions are causal overreach.

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Cite this review

Pith. "Pith review of The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?." pith.science (2026). https://pith.science/paper/R5N3L2CN

@misc{pith2026241118656,
  author       = {Pith},
  title        = {Pith review of: The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R5N3L2CN}},
  note         = {Machine review of arXiv:2411.18656}
}
read the original abstract

In today's world, AI programs powered by Machine Learning are ubiquitous, and have achieved seemingly exceptional performance across a broad range of tasks, from medical diagnosis and credit rating in banking, to theft detection via video analysis, and even predicting political or sexual orientation from facial images. These predominantly deep learning methods excel due to their extraordinary capacity to process vast amounts of complex data to extract complex correlations and relationship from different levels of features. In this paper, we contend that the designers and final users of these ML methods have forgotten a fundamental lesson from statistics: correlation does not imply causation. Not only do most state-of-the-art methods neglect this crucial principle, but by doing so they often produce nonsensical or flawed causal models, akin to social astrology or physiognomy. Consequently, we argue that current efforts to make AI models more ethical by merely reducing biases in the training data are insufficient. Through examples, we will demonstrate that the potential for harm posed by these methods can only be mitigated by a complete rethinking of their core models, improved quality assessment metrics and policies, and by maintaining humans oversight throughout the process.

Discussion (0). Continue with ORCID to comment.

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Pith tools

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