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REVIEW 3 major objections 6 minor 60 references

A Review on Machine Unlearning

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This review argues that machine unlearning is best organized as exact retraining versus fast approximate updates, with data lineage as the audit trail that makes deletion practical.

desk verdict A useful but imperfect survey of machine unlearning that needs correcting before it can be trusted as a map of the field. read the letter →

arxiv 2411.11315 v1 pith:4UAUBFAQ submitted 2024-11-18 cs.LG

Haibo Zhang , Toru Nakamura , Takamasa Isohara , Kouichi Sakurai This is my paper · ORCID
classification cs.LG
keywords machineunlearningrighttobeforgottenGDPRprivacy-preservinglearningdatalineageexactapproximatecertifiedremoval
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 review sets out to organize the field of machine unlearning, the practice of making a trained model behave as if specific training data had never been used. It argues that GDPR's Article 17 (the right to be forgotten) and security threats such as data poisoning make unlearning a practical necessity, while full retraining from scratch is too costly. The paper's central contribution is a classification of unlearning into exact methods, which retrain on the remaining data, and approximate methods, which adjust the model more cheaply. It further splits approximate unlearning into three families and connects the whole area to data lineage management as a tool for tracking data flows and supporting deletion. A reader comes away with a structured map of the main approaches and their trade-offs between speed, accuracy, and certifiability.

What carries the argument

The central object is the exact-versus-approximate taxonomy of unlearning, with approximate methods subdivided into three groups (Fisher-information-based retraining with noise, influence-function or Newton updates, and stored-batch parameter rollback). The taxonomy does the argument's work by giving every reviewed method a slot and by making the trade-off visible: exact methods are strongest in fidelity to true retraining and weakest in cost, while approximate methods invert that. A second load-bearing object is the data-lineage record, defined here as a running log of where each data point came from and how it was transformed during training; the paper argues this record is what makes unlearning auditable and helps defend against data-poisoning attacks. The SAPE-based metrics (effectiveness, certifiability, efficiency) supply a common yardstick for comparing updated models with fully retrained models.

What would settle it

A peer-reviewed unlearning method that fits none of the four categories — exact retraining, Fisher-information retraining with noise, influence/Newton updates, or stored-batch rollback — would show the survey's organizing taxonomy is incomplete; zero-shot unlearning, which erases data without accessing the original training set, is a plausible candidate to test.

Watch

Extended reading notes

Core claim

On the paper's own terms, machine unlearning is a security and privacy mechanism that lets a service provider respond to a data-holder's deletion request by producing a model that is as close as possible to one never trained on the deleted data. Exact unlearning achieves this by retraining on the dataset minus the removed points, while approximate unlearning updates the model or dataset directly and is the main focus of current research. Following the classification it adopts, the review groups approximate methods into three families: retraining the remaining data while injecting noise calibrated by the Fisher information matrix, taking a Newton step or influence-function update to remove the deleted data's effect, and storing training batches and their parameter updates so that a deletion can roll back the corresponding updates. It then proposes that effectiveness, certifiability, and efficiency, measured through Symmetric Absolute Percentage Error, form a common evaluation scheme, and argues that data lineage management should track every change so that unlearning and poisoning defense can operate on a complete record.

Load-bearing premise

The argument depends on the assumption that the exact/approximate split with three approximate families covers the important work in the field; if a substantial line of unlearning research falls outside those four bins, the review's map and its conclusions about open challenges are incomplete.

Editorial extensions

If this is right

  • For a small deletion request, methods that isolate training data into shards and retrain only the affected shard can answer quickly without retraining the whole model, while very large deletions may be cheaper by full retraining.
  • Approximate methods trade a formal closeness guarantee against the fully retrained model for lower time and computation cost; the three families differ mainly in what they retain from training to enable the update.
  • Using the SAPE-based effectiveness, certifiability, and efficiency metrics gives researchers a shared way to compare any unlearning algorithm against full retraining.
  • Data lineage, a record of data provenance and transformation during training, makes unlearning auditable and also helps detect data poisoning, so privacy protection and security defense can share the same tracking infrastructure.
  • Unlearning does not automatically restore privacy: the review reports that membership-inference attacks can still recover information from unlearned models, and retraining-from-scratch is more vulnerable to them than distributed or block-based approaches.

Reading between the lines

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

  • The taxonomy covers methods that operate on the trained model or training batches; unlearning approaches that never touch the original training data, such as zero-shot unlearning, would form a natural fourth category that the review leaves out.
  • The evaluation scheme takes full retraining as the gold standard, which makes exact unlearning the implicit target; a privacy-focused benchmark that measures residual membership-inference risk rather than closeness to retraining could rank the same methods very differently.
  • A practical extension the paper only hints at: data-lineage metadata could automatically supply the batch identifiers that stored-batch unlearning methods need, making the rollback step auditable and nearly free to execute.
  • Because approximate methods only make outputs close to a retrained model, they may still leak information through other channels; testing updated models against membership-inference attacks after each family of updates is a direct way to check whether forgetting is real.
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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 / 6 minor

Summary. This paper is a survey of machine unlearning, motivated by the GDPR right to be forgotten. It introduces the distinction between exact and approximate unlearning, reviews representative methods in each category (e.g., SQ-learning based unlearning, SISA, influence-based methods, scrubbing, and amnesiac unlearning), and discusses data lineage management as a supporting mechanism for privacy-preserving ML. The central contribution is a taxonomy and a summary table classifying 23 works, together with a discussion of open challenges.

Significance. The survey is potentially useful as an entry point for researchers and practitioners: it compiles a broad set of recent references, anchors the discussion in a clear exact-versus-approximate frame, and explicitly connects unlearning to data lineage tools such as MLMD. The value of the paper, however, rests on the fidelity of its classifications and complexity statements, since it provides no new experiments or proofs. The paper's strengths are its organizational clarity and the breadth of coverage within the chosen taxonomy.

major comments (3)
  1. [Section 4.5.2] The text describing Izzo et al. states that the computational cost 'is linearly related to the feature dimension d, i.e., O(d^2), and is independent of the number of training data n.' O(d^2) is quadratic in d, not linear, so the two claims are contradictory. Since the efficiency gain of approximate unlearning is central to the review's argument, this complexity statement must be corrected and reconciled with the cited source.
  2. [Table 1 and Figure 6] Table 1 classifies Bourtoule et al.'s SISA as 'Federated learning (SISA)', and Figure 6's caption says the SISA approach is 'presented in the form of federated learning.' Section 4.4.2 correctly describes SISA as sharded, isolated, sliced, and aggregated training on a single dataset without federated communication among multiple data owners. This internal inconsistency misleads readers about a representative exact-unlearning method; the same mischaracterization recurs in Section 6.3, where SISA is called a 'distributed unlearning model.'
  3. [Section 4.5.4] The Effectiveness metric is defined as AccErr = SAPE(Acc*_test, Accu_test), but the accompanying interpretation says a lower value means the updated model's accuracy is closer to 'the accuracy of the initial model (in which the noise value σ = 0).' Earlier in the same paragraph, Acc*_test is defined as 'the optimal accuracy of the regression model on the same dataset,' and σ is not introduced anywhere in the section. This makes the metric ambiguous; please clarify what Acc*_test represents and define σ.
minor comments (6)
  1. [Section 1.1] There is a typo: 'makings it more accessible for attackers' should be 'making it more accessible for attackers.'
  2. [Section 4.2] Reference [19] is cited for the idea of dividing data into blocks and training sub-models, but the cited work (Fredrikson et al.) is about model inversion attacks and does not appear to support this claim; please re-check the citation.
  3. [Section 4.5.4] There is a typo: 'delated data' should be 'deleted data.'
  4. [Table 1] The entry for He et al. 2021 says 'Intermeidate models'; this should be 'Intermediate models.'
  5. [Section 4.5.2] The notation for θ\k is garbled: 'θ\k = θ((θ, ..., k, 1, ... n−k)⊤)' does not clearly define the weight vector; please rewrite the expression in standard form.
  6. [References] Reference [5] (Schelter) lacks a publication venue and year; please complete the citation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the paper is a descriptive literature review with no derived predictions or fitted inputs.

full rationale

This paper is a survey. It presents a taxonomy of machine unlearning (exact vs approximate) adopted from Bourtoule et al. [3] and Mahadevan et al. [20], summarizes representative methods, and discusses data lineage. There is no equation that is fitted to data and then rediscovered as a prediction; the only equations are quoted from the surveyed works (e.g., Newton step, influence function, amnesiac unlearning parameter update) and are presented as descriptions rather than as new results. The paper contains no first-principles derivation whose conclusion is equivalent to its assumptions by construction. Its claims depend on external cited works, but the citations are to independent research groups and are not self-citations; no argument reduces to a citation of the present authors' own prior work. Inaccuracies such as classifying SISA as federated learning in Table 1/Fig. 6 or the O(d^2) wording in Section 4.5.2 are internal consistency/correctness issues, not circularity, because they do not make any conclusion identical to an input by definition. Therefore no circular step can be identified under the required standard.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The review introduces no new free parameters or invented entities. It relies on standard assumptions about model overfitting and retraining cost.

assumptions (2)
  • domain assumption Machine learning models can memorize and leak training data, including private information.
    The motivation for unlearning in Section 3 assumes that models are overfitted and can leak private training data. This is a generally accepted property, but it is an unproved background assumption for this review.
  • domain assumption Exact retraining after data deletion is computationally expensive, making approximate methods necessary.
    Section 4.2 states that retraining is computationally expensive. This premise motivates the entire unlearning research area and is assumed throughout.

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Cite this review

Pith. "Pith review of A Review on Machine Unlearning." pith.science (2026). https://pith.science/paper/4UAUBFAQ

@misc{pith2026241111315,
  author       = {Pith},
  title        = {Pith review of: A Review on Machine Unlearning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4UAUBFAQ}},
  note         = {Machine review of arXiv:2411.11315}
}
read the original abstract

Recently, an increasing number of laws have governed the useability of users' privacy. For example, Article 17 of the General Data Protection Regulation (GDPR), the right to be forgotten, requires machine learning applications to remove a portion of data from a dataset and retrain it if the user makes such a request. Furthermore, from the security perspective, training data for machine learning models, i.e., data that may contain user privacy, should be effectively protected, including appropriate erasure. Therefore, researchers propose various privacy-preserving methods to deal with such issues as machine unlearning. This paper provides an in-depth review of the security and privacy concerns in machine learning models. First, we present how machine learning can use users' private data in daily life and the role that the GDPR plays in this problem. Then, we introduce the concept of machine unlearning by describing the security threats in machine learning models and how to protect users' privacy from being violated using machine learning platforms. As the core content of the paper, we introduce and analyze current machine unlearning approaches and several representative research results and discuss them in the context of the data lineage. Furthermore, we also discuss the future research challenges in this field.

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Pith tools

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