{"id":"03761e65-df2e-42b3-bf60-867f8ef43f83","arxiv_id":"2507.16033","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Annotators' open-ended comments on AI image safety reveal moral, emotional, and quality-based reasoning that standard safety taxonomies fail to capture.","lead":"This paper analyzed 5,372 open-ended comments from 637 annotators rating AI-generated images, and found that safety judgments routinely go beyond predefined categories to include emotions, harm to others, and image quality. The authors argue that current AI safety evaluation pipelines miss these reasoning forms and propose annotation designs that separate self-harm from other-harm and scaffold subjective interpretation.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The central claim generalizes from a single adversarial, self-selected-comment task to all safety pipelines; a replication on representative non-adversarial outputs is needed before that conclusion can stand.","rationale":"The reader's weakest assumption—that the specific Nibbler corpus, custom instructions, and optional comment format may not represent safety annotation tasks in practice—is exactly the concern I find most load-bearing. The paper is internally coherent and its qualitative observations are often vivid and well-quoted, but the jump from 'these annotators, on these images, under this protocol, expressed these considerations' to 'existing safety pipelines miss critical forms of reasoning' is not secured. The task was deliberately constructed with a subjectivity note and with separate self/other harm questions, so the study demonstrates what is possible when such features are present, not what is missing from typical pipelines. The low comment rate (16.8%) and concentration of comments in a subset of annotators means the thematic analysis is based on a self-selected sample; this does not invalidate the themes, but it weakens prevalence claims such as 'annotators consistently invoke' moral or emotional reasoning. The moral sentiment autorater's low F1 scores (0.24–0.54) further weaken the quantitative moral-reasoning regressions, though the central qualitative claim does not depend on those regressions. Given these points, the appropriate disposition remains conditional acceptance with a request for a replication on representative data and, ideally, release of the de-identified comments and analysis code. I therefore recommend no change to the reader's verdict.","tokens_in":19153,"tokens_out":4929,"duration_ms":55879,"concrete_test":"Run the identical annotation protocol on a stratified random sample of 1,000 non-adversarial text-to-image outputs from ordinary, naturally occurring prompts, using the same annotator pool, instructions, and optional comment format. If the prevalence of emotional, moral, and quality-related commentary and the harm-to-others versus harm-to-self gap drop substantially or disappear, the paper's claims about 'existing safety pipelines' are artifacts of the adversarial corpus and task design; if the phenomena persist at comparable rates, the generalization is supported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's headline conclusion—that existing structured safety pipelines systematically miss moral, emotional, contextual, and quality-related reasoning—rests entirely on one bespoke annotation task. That task used only 1,000 Adversarial Nibbler prompt-image pairs, which are adversarial by construction and often visually distorted; it also included an explicit subjectivity note, dual harm-to-self and harm-to-others scales, and optional free-text comments. Each of these design choices plausibly amplifies the reported phenomena: adversarial images invite quality and emotional commentary, optional comments (16.8% of ratings, from a self-selected 66.6% of annotators) overrepresent images that provoke reactions, and asking about 'other people' invites a third-person effect that may explain the self/other asymmetry. The paper never measures a deployed pipeline or a non-adversarial corpus, so the universal phrasing 'existing safety pipelines miss critical forms of reasoning' is an extrapolation rather than a demonstrated result. This is an external-validity gap, not an internal contradiction, but it is load-bearing because the paper's title and abstract make the general claim, not merely a claim about Adversarial Nibbler.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":19354,"tokens_out":4725,"duration_ms":52525,"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":[{"comment":"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.","section":"Method: Adversarial Dataset and Annotation; Abstract"},{"comment":"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.","section":"Appendix: Table 3; Findings: Moral Sentiments and Their Influence on Judgments"},{"comment":"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.","section":"Findings: Moral Sentiments and Their Influence on Judgments"},{"comment":"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.","section":"Analysis: Qualitative Analysis"}],"minor_comments":[{"comment":"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.","section":"How is safety scored for different audiences?"},{"comment":"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.","section":"Demographic Differences in Harm Scores"},{"comment":"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.","section":"Throughout"},{"comment":"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.","section":"Table 2"},{"comment":"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.","section":"Figure 2"}],"recommendation":"major_revision","confidential_remarks":"I see no citation or novelty concerns; the manuscript is candid about its limitations. My main reservation is the gap between the general title and abstract claims and the single adversarial, self-selected-comment task. If the authors narrow the claims or add a representative replication, the paper could become publishable; as it stands, the external-validity gap is load-bearing and should be addressed."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Read the arXiv paper on annotator perspectives in GenAI image safety. The empirical core is genuinely new: the self/other harm gap in text-to-image annotation, the way visual distortions get read as harm, and the dual evaluation of prompt intent and generated image haven't been documented before. The qualitative analysis is careful, and the direct quotes are convincing. The demographic differences in the self/other gap are interesting, and the finding that women's rating patterns didn't change when gender-related images were removed is a genuinely non-obvious result.\n\nThe main weakness is the gap between what the data show and what the title/abstract claim. The study uses 1,000 Adversarial Nibbler pairs, which are adversarial by construction and often visually distorted. The task included an explicit note on subjectivity, separate harm-to-self and harm-to-others scales, and optional free-text comments. Each of those choices plausibly amplifies the reported phenomena. Comments came from only 16.8% of ratings, left by self-selected annotators, so the distribution of reasoning styles is probably skewed toward images that provoke reactions. The claim that 'existing safety pipelines miss critical forms of reasoning' is an extrapolation from one bespoke task. It's a reasonable hypothesis, but it is not demonstrated.\n\nThe moral-sentiment quantitative analysis is the weakest part. The autorater's F1 scores are low (Purity 0.24, Proportionality 0.28), the regressions linking comment sentiment to harm ratings are partly circular (comment and rating come from the same response), and the nested structure of annotators and items is ignored. The thematic analysis has no inter-coder reliability, and no data or code are released, which limits independent checking. These issues are fixable, but they need real work.\n\nNone of that is fatal. The qualitative findings stand on their own, and the discussion is appropriately cautious. The paper would be stronger with a title and abstract that say 'in an adversarial image annotation task' rather than 'in existing pipelines,' and with a replication on non-adversarial outputs.\n\nThis is for people designing safety annotation tasks and researchers working on perspectivist evaluation. It deserves a serious referee: send it to peer review, expect major revisions on external validity and quantitative rigor, but the core contribution is real.","headline":"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.","tokens_in":19915,"tokens_out":2927,"would_cite":true,"duration_ms":29727,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Structured safety taxonomies miss the moral, emotional, and quality-related reasoning that actually drives annotators' harm judgments.","keywords":["AI safety evaluation","annotator reasoning","text-to-image generation","moral foundations theory","harm-to-self versus harm-to-others","qualitative analysis","annotation task design","content moderation"],"falsifier":"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.","tokens_in":18977,"feed_emoji":"🖼️","tokens_out":4497,"duration_ms":45455,"temperature":0.7,"pith_summary":"This paper sets out to show that structured, categorical safety annotation frameworks are fundamentally misaligned with how annotators actually reason about AI-generated images. Analyzing 5,372 open-ended comments attached to 1,000 adversarial text-to-image prompt-output pairs, the authors find that annotators bring moral, emotional, and contextual considerations that do not fit the predefined harm categories. They report that annotators consistently rate harm to others higher than harm to themselves, that demographic groups differ in the size of this gap, and that image quality and prompt-output mismatches shape perceived harm. The authors conclude that safety pipelines miss critical reasoning and that evaluation designs should scaffold moral reflection, differentiate types of harm, and leave room for subjective interpretation.","feed_headline":"Safety labels miss why annotators flag AI images","feed_subtitle":"A study of 5,372 annotator comments shows harm judgments hinge on emotion, image quality, and whom the harm affects.","key_machinery":"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.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"supporting_citations":[{"why":"Supplies the 1,000 adversarial prompt-image pairs and their harm categories that define the study's annotation material.","marker":"Quaye et al. 2024"},{"why":"Provides prior evidence that annotator social differences shape safety judgments, which this study extends to image tasks.","marker":"Aroyo et al. 2023"},{"why":"Supplies the Moral Foundations Theory framework the paper uses to code moral reasoning in comments and prompts.","marker":"Graham, Haidt, and Nosek 2009"},{"why":"Provides the Moral Foundations Reddit Corpus used to evaluate the moral sentiment autorater's performance.","marker":"Trager et al. 2022"},{"why":"Links moral foundations to annotator disagreement, the relationship this paper tests for image safety judgments.","marker":"Davani et al. 2023"},{"why":"Offers the sociotechnical harms taxonomy that the paper contrasts with annotators' quality-harm entanglement.","marker":"Shelby et al. 2023"},{"why":"Describes a red-teaming protocol where annotators provide written justifications, the exception this paper's findings generalize from.","marker":"Weidinger et al. 2024"},{"why":"Provides prior work on identity-driven annotation behavior used to interpret demographic differences in harm scores.","marker":"Díaz et al. 2022b"}],"fun_headline_variants":["AI safety labels miss annotators' moral reasoning","Why annotators flag AI images: emotion, quality, context","Safety ratings ignore how annotators think about harm","Annotators judge AI image safety beyond set categories","Study: Harm judgments hinge on morality and image quality"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["AI safety labels miss annotators' moral reasoning","Why annotators flag AI images: emotion, quality, context","Safety ratings ignore how annotators think about harm","Annotators judge AI image safety beyond set categories","Study: Harm judgments hinge on morality and image quality"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000183,"raw_usage":{"total_tokens":1313,"prompt_tokens":946,"completion_tokens":367,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":562,"completion_tokens_details":{"reasoning_tokens":291}},"tokens_in":562,"tokens_out":367,"duration_ms":4728,"temperature":1.0,"reasoning_tokens":291,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T15:19:48.902504+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[],"review_version":1}