What the Machine Did—and Did Not Do

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A restrained account of AI assistance in translating Works of Love

Series: A Corrigible Kierkegaard, Essay 6
Status: public scholarly working paper; open for correction
Version: 0.3, 20 August 2026

Abstract

This essay describes the use of OpenAI Codex in a human-instigated translation project devoted to Søren Kierkegaard’s Kjerlighedens Gjerninger. It does not present artificial intelligence as an author, Danish scholar, or autonomous translator. Codex has assisted with source organisation, drafting, recurrent-term searches, comparison, bibliography, and the maintenance of a revisable apparatus. The project editor established the purpose, commissioned the work, required a modest and source-led method, and retains responsibility for publication and editorial decisions. The essay also identifies limits that procedural care cannot remove: a language model does not possess scholarly accountability, cannot certify the accuracy of its own fluent prose, and cannot establish the contents of its pretraining. Earlier English translations may expose questions during post-draft critical comparison, but Kierkegaard’s Danish remains the governing source. Professional concerns about consent, remuneration, literary voice, cultural standardisation, and translators’ livelihoods are not answered merely by producing a competent passage. The project’s appropriate claim is therefore limited: AI assistance can support an inspectable editorial process, but the resulting translation must still be judged passage by passage and remain open to correction.

Keywords: artificial intelligence; literary translation; Kierkegaard; human responsibility; disclosure; translation ethics

1. Why a method statement is necessary

“AI-assisted translation” can name very different activities. It may refer to an unreviewed prompt and response, machine translation followed by light post-editing, a human draft checked by computational tools, or a more elaborate process of source alignment, multiple drafts, concordance, commentary, and revision. Without a description, the label tells a reader little about how the English was formed or who is answerable for it.

The present project therefore needs a method statement. The statement should neither conceal the assistance nor inflate its sophistication. A long workflow cannot confer judgement upon a machine, and a disclosure cannot convert an error into a responsible choice. Its purpose is more practical: to allow a reader to know what kinds of work were delegated, where human decisions enter, and what claims the project refuses to make.

The series was instigated by the project editor, not by the software. After reading criticism of established Works of Love translations, the editor asked how the present text would appear under the same scrutiny and proposed a set of public essays on method, style, contested vocabulary, AI assistance, and correction. The editor also required a modest tone and accepted that the essays should record weaknesses rather than function as promotional defences.

The editor set the project’s aims and authorised publication, but those decisions do not guarantee the correctness of every generated sentence.

2. What Codex has done

Codex has assisted in six broad areas.

2.1 Source organisation

The project uses Søren Kierkegaards Skrifter 9 as its controlling Danish text.¹ Codex has helped separate source text from website controls and apparatus, organise numbered source units, preserve Danish openings and closings, and align those units with English. It has also helped maintain source ledgers and chapter status records.

This is clerical and analytical work with interpretive consequences. A misplaced boundary can change syntax; an omitted opening can alter an argument. The source units therefore require checking against the visible SKS reading rather than acceptance because a file has been generated.

2.2 Drafting close and readable English

Codex has proposed both close and readable translations. The close layer is intended to control completeness, lexical recurrence, syntax, and imagery. The readable layer may recast word order or sentence division while remaining responsible to the Danish.

The language model’s fluency is useful but dangerous here. It can produce an English paragraph that appears finished even when it has compressed or omitted source material. The project’s early review found exactly that problem. The response was not to trust a later draft more strongly; it was to strengthen source-unit alignment and distinguish complete close translation from synopsis.²

2.3 Recurrence and corpus work

Codex has searched the local Danish corpus for recurring word families and compared their contexts. This assisted the review of Kjerlighed, Elskov, Forkjerlighed, and erotisk. The resulting Essay 4 recommends suspending a blanket equation of Elskov with “erotic love.”³

Counts can expose a weak generalisation, but they do not translate a word. A frequency table does not decide whether Elskov in one sentence is best rendered “romantic love,” “preferential love,” “love between lovers,” or another phrase. Context, syntax, argument, historical usage, and English implication remain necessary.

2.4 Post-draft critical comparison

Earlier English translations have been consulted after drafting as comparators and critics, not as the governing source or as templates for ready-made sentences. Their differences can reveal a missed distinction, an alternative syntactic attachment, or a stylistic possibility. Any proposed change must then be justified from Kierkegaard’s Danish rather than from the prestige or elegance of the predecessor.

The current Chapter I underwent a source-led stylistic review after such comparison. In its present form the project has not identified an uncomfortable convergence with the Hongs. This is a limited judgement about the resulting English text. It does not prove historical independence, and it says nothing certain about language that may have entered model pretraining.

2.5 Research discovery and bibliography

Codex has located relevant scholarship, dictionary entries, reviews, professional statements, and public discussion. It has helped check titles, dates, page ranges, and links. The sources include work on translating Kierkegaard, debates about Elskov and Kjerlighed, literary-translation ethics, authorship disclosure, and the reception of AI translation.

Discovery is not authority. A generated citation can be wrong, a search result can be misread, and an abstract cannot support claims made only in the full article. The project therefore cites the underlying source and should distinguish works actually inspected from items merely located for further reading.

2.6 Drafting the apparatus and essays

Codex has helped draft glossary entries, landmark registers, decision records, chapter essays, these methodological papers, and a proposed public correction protocol. This makes the assistance substantive rather than incidental. It should be disclosed.

The model is nevertheless not listed as an author. Authorship entails responsibility for claims, responses to criticism, competing interests, permissions, and the final text. Codex cannot undertake those obligations. Guidance from Cambridge University Press and the Modern Language Association likewise places responsibility upon human authors and calls for material AI use to be described.⁴

3. What Codex has not done

The limits are as important as the tasks.

3.1 It has not become a Danish scholar

A language model can analyse Danish and retrieve patterns. That performance is not a scholarly qualification, a history of sustained reading, or membership in the interpretive conversation surrounding Kierkegaard. The present work has not been reviewed by a commissioned Danish specialist. Public availability creates an opportunity for correction; it does not fill that absence.

3.2 It has not certified its own accuracy

Generated explanations may sound proportionate and still be wrong. Confidence of tone is not evidence. The project’s omitted paragraphs and over-broad Elskov default demonstrate that fluent production and elaborate apparatus can coexist with substantive error.

No self-review prompt solves this problem. Independent source checking, disagreement between drafts, dictionaries, published scholarship, biblical context, and public criticism can increase the chance of discovery. They cannot yield a guarantee.

3.3 It has not supplied human literary accountability

The machine does not experience Kierkegaard’s prose, bear a reputation, enter a vocation, lose income, consent to use of its work, or answer a scholar who objects. It cannot decide what should be published under a human name. Those decisions remain with the project editor and any eventual human signatory.

3.4 It has not established freedom from pretraining influence

The project can control what materials are supplied within a particular working context. It cannot reconstruct the model’s training data or prove that no earlier translation affected the model’s linguistic tendencies. The series will not claim “translation from Danish alone” in an absolute historical sense. Its narrower claim is procedural: the Danish governs drafting and revision, while earlier English versions are used only for post-draft criticism.

3.5 It has not made ethical objections disappear

Even a passage judged accurate may have been produced within practices that translators reasonably contest. Textual quality does not settle questions about permission, compensation, attribution, cultural labour, environmental cost, concentration of power, or the future conditions of literary translation.

4. Human direction in this project

The phrase “human-directed” should not be decorative. In this project it refers to identifiable interventions by the project editor:

• initiating the translation and defining it as a working companion rather than a definitive edition;
• approving the Translation Method Charter and its values;
• asking for close and readable layers and a living apparatus;
• requiring source verification and preserved revision history;
• commissioning critical essays rather than promotional copy;
• asking how known scholarly objections apply to the project’s own text;
• accepting the suspension of the project’s Elskov default when the evidence warranted it;
• requiring a modest tone;
• deciding whether, where, and under whose name anything will be published.

The descriptive role “project editor and series instigator” is therefore appropriate. It makes no claim to an institutional affiliation or to expertise not asserted by the editor. Before publication, a byline and contribution statement should identify the human person who has actually read and approved the final paper.

Human direction does not mean that the editor manually composed every line. Nor should “human in the loop” be treated as a quality mark without evidence of what the human did. The contribution statement must remain specific.

5. A translation workflow open to inspection

The public workflow can be described without presenting it as a recipe that ensures success:

1. Establish the source unit. Record the Danish text and SKS location.
2. Analyse before smoothing. Identify syntax, recurrent terms, images, biblical echoes, rhetorical address, and unresolved ambiguity.
3. Draft the close layer. Account for the complete unit.
4. Draft the readable layer. Make necessary English changes while retaining conceptual and rhetorical pressure.
5. Check completeness and recurrence. Compare the opening, closing, and internal content of every source unit; test glossary decisions across occurrences.
6. Consult relevant sources and comparators. Use dictionaries, scholarship, and earlier translations to expose problems after the draft exists.
7. Return to the Danish. Accept a change only when the source and argument support it.
8. Record consequential decisions. Mark them adopted, provisional, or open.
9. Preserve versions. Keep superseded text and the reason for revision.
10. Invite correction. Publish bounded claims and respond to objections by evidence.

The workflow’s strongest feature is not automation. It is the attempt to leave a trail from source unit to decision. Its weakness is that much of the trail is itself generated or maintained with AI assistance and therefore requires human checking.

6. What current scholarship can and cannot tell us

Scholarship on AI and translation is developing rapidly, and general conclusions should be cautious.

Bo Li examines ethical issues raised for literary translation in the era of artificial intelligence.⁵ Lawrence Venuti, while insisting upon translation as interpretation, allows that computational tools may be incorporated within a human interpretive act.⁶ These approaches help describe a project in which tools assist source handling and drafting but do not acquire authorship.

Kristiina Taivalkoski-Shilov identifies ethical risks in machine-assisted literary translation, including the treatment of voice, multivocality, process, and the conditions of professional work.⁷ Her analysis is particularly relevant to Kierkegaard, whose changing pseudonymous and rhetorical voices cannot safely be reduced to fluent semantic transfer.

One recent Arabic–English study reported higher fluency scores for ChatGPT output but higher accuracy scores for human literary translation.⁸ The sample was small and language-direction specific. It does not establish a universal ranking, but it illustrates why surface fluency should not be treated as accuracy.

An emerging experimental study of English–Chinese literary translation suggests that disclosure of AI authorship can change some readers’ retrospective ethical interpretation even where blind-rated empathy or quality does not significantly differ.⁹ Its design and language pair limit generalisation. It nevertheless supports a practical point: disclosure is not extraneous to reception. Readers may reasonably care about process as well as prose.

None of these sources validates the present translation. They help specify the relevant questions.

7. Professional objections deserve their own answer

The European Council of Literary Translators’ Associations has warned about authorisation, remuneration, attribution, standardisation, and cultural impoverishment in the use of AI.¹⁰ The UK Translators Association has likewise urged publishers not to replace human literary translators with AI.¹¹ Such statements arise from professional experience and material interests that an AI-assisted project should not caricature as fear of technology.

The present project cannot answer these objections by saying that its output has footnotes or that a machine sometimes gets a Danish sentence right. At most it can act responsibly within its scope:

• disclose substantive AI assistance;
• avoid representing Codex as an author or scholar;
• cite and engage human translators and researchers;
• avoid claiming that literary translators are unnecessary;
• respect lawful access and avoid reproducing protected translations;
• invite correction without assuming unpaid labour from specialists;
• accept that some readers will object to the project on principled grounds.

The absence of resources to commission Danish scholars should be stated, not turned into a request that scholars supply free certification. Public engagement is voluntary.

8. Reddit and other public discussion

Reddit discussions in r/kierkegaard and r/TranslationStudies show recurring anxieties about which Kierkegaard translation to choose, whether literalness is being confused with fidelity, whether AI output flattens voice, and whether post-editing transfers labour while reducing recognition or pay.¹² These discussions are useful evidence of reception and professional concern. They are not linguistic evidence for a reading of Kjerlighedens Gjerninger.

A precise grammatical observation posted anonymously can be tested against the Danish and may prove correct. A frequently repeated claim does not become correct by popularity. The project’s evidence register should label Reddit material “informal reception” unless its substantive claims are independently supported.

9. A suitable disclosure

A concise public disclosure for the translation might read:

This is a human-instigated, AI-assisted working translation. The project editor established the method, commissioned and reviewed the work, and retains responsibility for publication decisions. OpenAI Codex assisted with source organisation, drafting, recurrent-term analysis, research discovery, comparison, and documentation. Kierkegaard’s Danish in SKS 9 is the governing source. The work has not been peer reviewed or approved by a Danish specialist and remains open to correction.

Individual essays should add the tasks specific to them. If a human author has not yet reviewed and accepted responsibility for a paper, it should remain an internal or unsigned draft rather than being represented as authored scholarship.

This disclosure is not a warning label intended to settle the reader’s judgement. It supplies information needed to form that judgement.

10. Claims the project should avoid

The following formulations would exceed the evidence:

• “AI has translated Kierkegaard as well as or better than scholars.”
• “The workflow guarantees accuracy.”
• “The text is independent of all previous English translations.”
• “The translation has been academically validated.”
• “Two English layers eliminate the trade-off between style and accuracy.”
• “Open publication is equivalent to peer review.”
• “Criticism of AI translation is merely hostility to innovation.”

More defensible language is smaller: a passage preserves a particular distinction; a corpus finding prompted revision; a source unit has been checked for completeness; a decision remains open; a public objection has been answered or accepted.

Limitations

This account is written from within the project it describes. It may therefore understate problems or overestimate the effectiveness of its safeguards. No Danish specialist, literary translator, AI ethicist, or publishing lawyer has reviewed it. The legal and commercial implications of eventual publication are outside this essay’s competence and would require separate advice appropriate to the intended venue and jurisdiction.

The cited empirical research concerns different language pairs, models, genres, and reader populations. It provides context, not a performance evaluation of this translation.

Conclusion

The machine did substantial work. It organised sources, drafted English, searched recurrences, found research, compared alternatives, and helped construct the apparatus and essays. That assistance should not be minimised.

It did not become an author, acquire Danish scholarly standing, certify its own accuracy, reconstruct its pretraining, assume professional responsibility, or resolve the ethical objections of literary translators. Those limits should not be softened.

The project’s modest case is that AI assistance can be placed within a human-directed, source-led, inspectable process. Whether the resulting English deserves use remains a passage-by-passage judgement. The purpose of this series is to make that judgement easier to contest.

Contribution and AI-use statement

The project editor and series instigator conceived the inquiry, established its methodological and ethical direction, required a modest account of AI assistance, and retains final editorial authority. OpenAI Codex assisted with source analysis, research discovery, bibliographic checking, outlining, and drafting this essay, as well as with the translation activities described above. Codex is not an author. A human signatory must review, approve, and accept responsibility for any published version.

Bibliography

Al Rousan, Rafat, Raghad Jaradat, and Mona Malkawi. “ChatGPT Translation vs. Human Translation: An Examination of a Literary Text.” Cogent Social Sciences 11, no. 1 (2025): 2472916. https://doi.org/10.1080/23311886.2025.2472916.

European Council of Literary Translators’ Associations. “Statement on Artificial Intelligence.” 2023. PDF.

Li, Bo. “Ethical Issues for Literary Translation in the Era of Artificial Intelligence.” Babel 69, no. 4 (2023): 529–545. https://doi.org/10.1075/babel.00334.li.

Modern Language Association. “Beyond Citation: Describing AI Use in Your Work.” MLA Style Center. https://style.mla.org/describing-ai-use/.

Taivalkoski-Shilov, Kristiina. “Ethical Issues Regarding Machine(-Assisted) Translation of Literary Texts.” Perspectives 27, no. 5 (2019): 689–703. https://doi.org/10.1080/0907676X.2018.1520907.

Translators Association. “Don’t Trade Human Translators for AI.” Society of Authors. Statement.

Venuti, Lawrence. “The Human Translator versus the Machine: Algorithms of Interpretation.” Critical Inquiry 52, no. 3 (2026). https://doi.org/10.1086/739756.

Zhang, Wenkang, Rui Xie, and Jiaxing Hu. “Reading without Knowing: How Authorship Disclosure Shapes Ethical Engagement with AI-Translated Literature.” Ethics & Behavior, advance online publication, 6 July 2026. https://doi.org/10.1080/10508422.2026.2696829.

Notes

Correction history

• 0.3 — 20 August 2026: refreshed the Essay 4 cross-reference after the final reception-language review.
• 0.2 — 19 August 2026: corrected the Li, Al Rousan–Jaradat–Malkawi, and Zhang–Xie–Hu bibliographic records against publisher and institutional records; narrowed the description of Li’s argument; updated the Essay 4 link.
• 0.1 — 19 August 2026: first complete public-working-paper draft.

1. Søren Kierkegaard, Kjerlighedens Gjerninger, in Søren Kierkegaards Skrifter, vol. 9 (Copenhagen: Gads Forlag, 2004), volume information. ↩
2. Works of Love — Methodological Review after Chapters 1–3 (project record), “Close and Readable Translation.” ↩
3. “What Kind of Love Is Elskov?”. ↩
4. Cambridge University Press, “Cambridge Launches AI Research Ethics Policy”; Modern Language Association, “Beyond Citation”. ↩
5. Bo Li, “Ethical Issues for Literary Translation in the Era of Artificial Intelligence,” Babel 69, no. 4 (2023): 529–545, https://doi.org/10.1075/babel.00334.li. ↩
6. Lawrence Venuti, “The Human Translator versus the Machine: Algorithms of Interpretation,” Critical Inquiry 52, no. 3 (2026), https://doi.org/10.1086/739756. ↩
7. Kristiina Taivalkoski-Shilov, “Ethical Issues Regarding Machine(-Assisted) Translation of Literary Texts,” Perspectives 27, no. 5 (2019): 689–703, https://doi.org/10.1080/0907676X.2018.1520907. ↩
8. Rafat Al Rousan, Raghad Jaradat, and Mona Malkawi, “ChatGPT Translation vs. Human Translation: An Examination of a Literary Text,” Cogent Social Sciences 11, no. 1 (2025): 2472916, https://doi.org/10.1080/23311886.2025.2472916. ↩
9. “Reading without Knowing: How Authorship Disclosure Shapes Ethical Engagement with AI-Translated Literature,” Ethics & Behavior, advance online publication, 6 July 2026, https://doi.org/10.1080/10508422.2026.2696829. ↩
10. European Council of Literary Translators’ Associations, “Statement on Artificial Intelligence”. ↩
11. Translators Association, “Don’t Trade Human Translators for AI”. ↩
12. Illustrative public discussions: r/kierkegaard, translation discussion; r/kierkegaard, edition discussion; r/TranslationStudies, AI literary-translation discussion; r/TranslationStudies, professional concerns. These links are reception evidence only. ↩

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Editorial responsibility. The project editor and series instigator initiated the translation and this essay series, established its methodological and ethical direction, commissioned the research and drafting, and retains authority over revisions and publication. OpenAI Codex assisted with the work described in each essay but is not an author. No Danish specialist or Kierkegaard scholar is represented as a reviewer, participant, or endorser.

Invitation to engagement. Interested readers are welcome to discuss, criticise, or correct these papers publicly elsewhere. Such engagement does not imply participation in or endorsement of the project.