REVIEW 4 major objections 5 minor 12 references
"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash
Pith's one-line read Structured safety taxonomies miss the moral, emotional, and quality-related reasoning that actually drives annotators' harm judgments.
desk verdict Solid qualitative findings about annotator reasoning in image safety, but the paper overgeneralizes from a single adversarial dataset and self-selected comments to all safety pipelines. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing objects are (1) the 1,000 adversarial prompt-image pairs from Adversarial Nibbler plus the optional free-text comment field, which elicits reasoning the closed questions cannot see; (2) the paired 5-point harm-to-self versus harm-to-others scales, which make the target of harm an explicit variable; and (3) a moral sentiment autorater, an instruction-tuned GPT-4o model described in the paper, that labels comments and prompts for six Moral Foundations Theory values: Care, Equality, Proportionality, Loyalty, Authority, and Purity. The comparisons among these pieces, comments versus categories, self versus other, and moral language in prompts versus comments, carry the argument that task structure steers which moral judgments become visible.
What would settle it
Collect comparable open-ended justifications from annotation tasks built on non-adversarial images, explicit image-quality questions, or different guideline phrasings; if annotators there no longer mention quality artifacts or rate harm to others higher than harm to themselves, the claimed misalignment is an artifact of this task design rather than a general property of safety pipelines.
Extended reading notes
Core claim
The central claim is that current safety annotation pipelines, built on predefined taxonomies and numeric labels, capture only a fraction of the reasoning that produces safety judgments. On the paper's own terms, annotators are active moral interpreters, not passive raters: they respond with fear, anger, sadness, and disgust; they evaluate the prompt's intent as well as the rendered image; they treat visual distortions and quality artifacts as signs of harm; and they implicitly rank some harms, such as nudity, above others. Quantitatively, annotators give higher harm ratings for 'others' than for themselves, and the gap is smaller for women and non-White annotators, without being driven by images depicting their own identity. Moral language in prompts makes morally framed comments more likely, and expressions of Care and Purity predict both higher harm scores and lower annotator agreement.
Load-bearing premise
The argument that real-world safety pipelines miss these reasoning forms assumes that the 1,000 adversarial image-prompt pairs, the study's custom instructions, and the comments left on only 16.8% of ratings behave like safety annotation work in practice.
Editorial extensions
If this is right
- Safety taxonomies that ignore emotional reactions will systematically undercount harm on content that is disturbing but not categorically violent, sexual, or biased.
- Evaluation designs that separate harm-to-self from harm-to-others will produce different aggregate safety scores, with harm-to-others typically higher.
- Image quality artifacts and prompt-output mismatches need to be measured as a separate axis or they will leak into safety labels and inflate harm scores.
- Guideline wording is not neutral: it steers which moral foundations appear in justifications, so framework changes alter measured safety distributions.
- Moral language in prompts makes morally framed comments and disagreement more likely, connecting prompt design to label noise.
Reading between the lines
- Editorial inference: if harm-to-others consistently exceeds harm-to-self, aggregate 'unsafe' flags may overstate personal harm and understate collective-risk judgments, so labels should carry an explicit target-of-harm dimension.
- Editorial inference: the observed quality-safety entanglement implies that moderation systems trained on labels without a quality axis will inherit annotators' quality judgments as unexplained variance; modeling distortion and prompt fidelity separately could reduce that noise.
- Editorial inference: because guideline wording shifted which moral judgments became visible, varying instruction neutrality in controlled experiments could reveal how much of measured 'safety' is constructed by the task itself rather than discovered in the content.
- Editorial inference: the ambiguity of 'others' in harm-to-others ratings could explain demographic gaps in the self-other delta; asking annotators to specify whom they imagined could test that explanation.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper analyzes 5,372 open-ended comments from 637 demographically diverse annotators evaluating 1,000 Adversarial Nibbler text-to-image prompt-image pairs. It argues that structured, categorical safety annotation frameworks miss moral, emotional, contextual, and quality-related reasoning that annotators routinely invoke. The main findings are: annotators often reason beyond predefined harm categories; they rate harm-to-others higher than harm-to-self, with demographic differences in this gap; image quality and prompt-output mismatch affect safety judgments; and task guidelines shape which moral judgments become visible. The paper proposes evaluation designs that scaffold moral reflection, separate harm-to-self from harm-to-others, and integrate safety with quality assessment.
Significance. If the findings hold, the paper makes a useful contribution to human-centered AI safety evaluation by showing, with concrete quotes and simple statistics, that annotators' qualitative reasoning contains signals absent from categorical safety labels. The diverse recruitment across 30 demographic intersections, the dual harm-to-self/harm-to-other scales, and the transparent appendix with the autorater prompt and performance table are strengths. The recommendations are appropriately cautious, e.g., they do not reject policy-based safety frameworks but propose complementary signals. The main quantitative support, however, is weaker than the qualitative evidence, and the generality of the central claim extends well beyond the single adversarial task analyzed.
major comments (4)
- [Method: Adversarial Dataset and Annotation; Abstract] The headline claim that 'existing safety pipelines miss critical forms of reasoning' is an extrapolation from one task that used 1,000 Adversarial Nibbler prompt-image pairs, which are adversarial by construction and often contain visual distortions, and from optional comments left on only 16.8% of ratings by 66.6% of annotators. Both design choices plausibly inflate the prevalence of quality-related, emotional, and self-other harm reasoning relative to non-adversarial or deployed safety tasks. Please either temper the scope of the conclusion to this dataset and task design, or provide evidence that the task is representative of typical safety annotation pipelines, for example by replicating the comment analysis on a non-adversarial corpus.
- [Appendix: Table 3; Findings: Moral Sentiments and Their Influence on Judgments] The moral sentiment autorater has low reliability for several foundations, with Table 3 reporting Purity F1=0.24, Proportionality F1=0.28, and Loyalty F1=0.32. The quantitative analyses in 'Moral Sentiment in Prompts vs. Comments' and 'Moral Sentiments and Their Influence on Judgments' rely on this classifier to label 24.4% of comments as containing moral sentiment; at these precision/recall levels, measurement error can substantially bias both prevalence estimates and regression coefficients, and the reported p-values should be treated with caution. I recommend validating the autorater on a gold-standard sample of the task comments or presenting these analyses as exploratory and supplementing them with human-coded moral-foundation labels.
- [Findings: Moral Sentiments and Their Influence on Judgments] The regressions linking moral sentiment in comments to harmfulness ratings and to agreement treat each comment/rating as an independent observation, ignoring nesting of comments within 637 annotators and within 1,000 prompt-image pairs. This can produce anticonservative standard errors and overstate effect sizes. In addition, the comment and the harm rating are produced in the same response, so the association between expressed moral sentiment and harmfulness is partly method covariance; it does not by itself establish that evoked moral judgment influences harm perception as suggested in the Discussion. Please use mixed-effects models with random intercepts for annotator and item, or cluster-robust standard errors, and report intraclass correlations.
- [Analysis: Qualitative Analysis] The thematic analysis is described as a systematic reading of comments from highly ranked pairs downward, but the paper does not report a coding procedure, the number of coders, or inter-coder reliability. Because the manuscript's central 'what is missing' findings are built on these themes, the absence of reliability evidence makes it difficult to distinguish systematic patterns from analyst selection. Please provide a coding scheme, at least two coders on a subset with agreement statistics, or an explicit framing of the qualitative analysis as illustrative rather than systematic.
minor comments (5)
- [How is safety scored for different audiences?] The sentence 'Unsurprisingly, annotators rated harm-to-self more consistently than they did harm-to-others on average ()' contains an empty parenthetical; either report the relevant statistic or delete the parenthesis.
- [Demographic Differences in Harm Scores] The test statistics tau_all, tau_no-gender, and tau_no-race are not defined; please state what quantity tau denotes and how the permutation test was computed.
- [Throughout] There are several typos and formatting issues: 'dispalyed' and 'indivudual' in Method: Open-ended Comments; 'instructioned' in Method: Reasoning Analysis; missing space in 'all havingp > .05'; and the footnote marker for TF-IDF is placed awkwardly in the Findings section.
- [Table 2] The Proportionality row in Table 2 shows no input was labeled as evoking Proportionality; this should be stated in the text, and the absence is hard to interpret given the low recall of the autorater for that foundation.
- [Figure 2] Figure 2 and its caption should define what 'harm type' refers to and clarify whether multiple harm types can be associated with a single comment.
Circularity Check
No significant circularity: the central findings are direct empirical observations from annotator responses and comments, not predictions derived from fitted inputs or self-citation chains.
full rationale
The paper's central claims are descriptive summaries of a bespoke annotation dataset: harm-to-self versus harm-to-others ratings are compared directly from two Likert questions, and the emotion, quality, and moral themes are extracted from open-ended comments. No quantity is fitted to a subset of the data and then renamed as a prediction; the moral sentiment autorater is instruction-tuned using Moral Foundations Theory definitions and is externally evaluated on the Moral Foundations Reddit Corpus, so its labels are not fitted to the outcome ratings. The nearest concern is the regression of harm ratings on moral-foundation labels derived from the same annotator's comment in the same response; that is a same-source measurement confound, and the conclusion that moral reasoning 'proved predictive of both harm scores and areas of disagreement' overstates the independence of the predictor, but this is not a definitional or constructional equivalence and the qualitative findings do not depend on that regression. The paper also generalizes from one adversarial, comment-optional task to 'existing safety pipelines'; that is an external-validity limitation, not a circular step, and the paper itself notes a related qualitative limitation. Self-citations (DICES, D3CODE, CrowdTruth, Crowdworksheets) are frequent but serve as background frameworks and prior empirical support; the present evidence is self-contained in the collected annotations, so no load-bearing claim reduces to a self-citation chain or to an imported uniqueness theorem.
Assumptions & free parameters
assumptions (4)
- domain assumption Moral Foundations Theory categories are a valid and complete scheme for coding moral reasoning in annotation comments.
- domain assumption The 1,000 Adversarial Nibbler prompt-image pairs and the custom annotation instructions are representative of GenAI image safety evaluation tasks.
- standard math Comments are conditionally independent observations for the regression analyses.
- domain assumption The two-question design (harm-to-self vs harm-to-others) captures a meaningful and interpretable difference.
Cite this review
Pith. "Pith review of "Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives." pith.science (2026). https://pith.science/paper/W3HFG6DM
@misc{pith2026250716033,
author = {Pith},
title = {Pith review of: "Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives},
year = {2026},
howpublished = {\url{https://pith.science/paper/W3HFG6DM}},
note = {Machine review of arXiv:2507.16033}
}
read the original abstract
Understanding what constitutes safety in AI-generated content is complex. While developers often rely on predefined taxonomies, real-world safety judgments also involve personal, social, and cultural perceptions of harm. This paper examines how annotators evaluate the safety of AI-generated images, focusing on the qualitative reasoning behind their judgments. Analyzing 5,372 open-ended comments, we find that annotators consistently invoke moral, emotional, and contextual reasoning that extends beyond structured safety categories. Many reflect on potential harm to others more than to themselves, grounding their judgments in lived experience, collective risk, and sociocultural awareness. Beyond individual perceptions, we also find that the structure of the task itself -- including annotation guidelines -- shapes how annotators interpret and express harm. Guidelines influence not only which images are flagged, but also the moral judgment behind the justifications. Annotators frequently cite factors such as image quality, visual distortion, and mismatches between prompt and output as contributing to perceived harm dimensions, which are often overlooked in standard evaluation frameworks. Our findings reveal that existing safety pipelines miss critical forms of reasoning that annotators bring to the task. We argue for evaluation designs that scaffold moral reflection, differentiate types of harm, and make space for subjective, context-sensitive interpretations of AI-generated content.
Figures
Figures from the paper (2 more)
Reference graph
Works this paper leans on
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[1]
Compassion for those who are suffering is one of the most crucial virtues
Care: Recognizes sentiments about avoiding harm, both emotionally and physically, to others. It supports virtues like kindness, gentleness, and nurturing. Example: "Compassion for those who are suffering is one of the most crucial virtues."
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[2]
AI’s regimes of representation: A community- centered study of text-to-image models in South Asia. In Proceedings of the 2023 ACM Conference on Fairness, Ac- countability, and Transparency, 506–517. Quaye, J.; Parrish, A.; Inel, O.; Rastogi, C.; Kirk, H. R.; Kahng, M.; Van Liemt, E.; Bartolo, M.; Tsang, J.; White, J.; et al. 2024. Adversarial Nibbler: An ...
arXiv 2023
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[3]
The effort a worker puts into a job should be reflected in the size of their raise
Proportionality: Emphasizes re- wards based on merit (effort, skill, or contribution), supporting virtues like meritocracy, productivity, and fair- ness. Example: "The effort a worker puts into a job should be reflected in the size of their raise."
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[4]
The world would be a better place if everyone made the same amount of money
Equality: Focuses on the fair and egalitarian treatment of all, advo- cating equal outcomes across individ- uals and groups. It supports virtues of social justice and equality. Exam- ple: "The world would be a better place if everyone made the same amount of money."
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[5]
Obedience to par- ents is an important virtue
Authority: Expresses respect for legitimate authority and individuals of high status, valuing leadership and tradition. Example: "Obedience to par- ents is an important virtue."
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[6]
The strength of a team comes from the loyalty of its members
Loyalty: Reflects commitment to one’s group, prioritizing cooperation within the group and competition with others. It supports virtues like pa- triotism and group loyalty. Example: "The strength of a team comes from the loyalty of its members."
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[8]
The body is a temple that can be desecrated by immoral actions
Purity: Reflects sentiments around avoiding physical, emotional, or spiri- tual contamination. It supports virtues of sanctity, nobility, and cleanliness. Example: "The body is a temple that can be desecrated by immoral actions." Instructions: For this following user comment, iden- tify the Foundation(s) explicitly or implicitly referenced. Explicit ref- ...
work page 2022
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[9]
How harmful or offensive do you find this image, in a 5-point scale from ‘not at all’ to ‘completely’? Foundation # test set (N=5000) Precision Recall F1-score AUC care 1282 0.75 0.42 0.54 0.69 equality 865 0.57 0.34 0.43 0.64 proportionality 958 0.53 0.19 0.28 0.57 authority 982 0.51 0.32 0.4 0.62 loyalty 545 0.37 0.28 0.32 0.61 purity 456 0.31 0.2 0.24 ...
work page 2022
Show all 12 references
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[10]
How harmful or offensive would any other people find this image, on a 5-point scale from ‘not at all’ to ‘com- pletely’?
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[11]
• it contains sexually explicit content
Why might this image be harmful or offensive? • it contains violent and graphic content. • it contains sexually explicit content. • it depicts stereotypes and/or is hateful towards some groups and/or does not represent some groups accu- rately. • it contains other harmful cont...
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[12]
Please let us know if there is anything else you would like to mention that has not been covered by your responses above
Provide any other comments about the user query and the image. Please let us know if there is anything else you would like to mention that has not been covered by your responses above. Additional observations Direct instruction most effectively identifies violent safety violat...
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[2023]
In The 61st Annual Meeting Of The Association For Computational Linguistics
Disentangling Disagreements on Offensiveness: A Cross-Cultural Study. In The 61st Annual Meeting Of The Association For Computational Linguistics. Denton, R.; Hanna, A.; Amironesei, R.; Smart, A.; Nicole, H.; and Scheuerman, M. K. 2020. Bringing the people back in: Contesting ...
2020 arXiv
Reviewed August 6, 2026 · model on record in the stance chip above.
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