{"id":"112cc594-202b-4012-b0a0-ada856b32100","arxiv_id":"2501.14778","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":3.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"An analysis of AIID and AIAAIC identifies nine reporting gaps and proposes nine remedial recommendations, mostly echoing existing calls for standardization.","lead":"This paper examines two public AI incident databases and lists nine gaps in how AI incidents are reported, along with nine recommendations for standardization. It argues that standardized incident reporting can make AI safer and support UN sustainability goals.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The 'systematic methodology' includes a test submission to AIID and AIAAIC, but the paper never reports what happened; this unverifiable step weakens the nine-gap claim.","rationale":"I considered the reader's representativeness concern: the shortlist excludes AILD, AVID, and OECD AIM with stated reasons, so external validity is a real but secondary issue; a paper could still make valid claims about the two analyzed repositories if it carefully scoped them. The more immediately falsifiable problem is internal: the authors list step 4 as part of a systematic methodology, yet the results section never reports the outcome of those submissions. This is the kind of missing support the review guidelines ask to flag. It is load-bearing because the abstract and introduction claim the nine gaps are the product of a systematic methodology; one of the methodology's explicit data-collection actions has no observable result. A single contradiction also exists in Section 5.3, which says only six fields are compatible while Table 3 lists seven common fields, suggesting a possible counting error. However, I do not make that the headline because correcting it does not overturn the gap analysis; the missing submission results affect the evidentiary basis of the whole method. Agreement with the reader is partial: we both flag methodology, but the reader emphasized sample representativeness (external) whereas I emphasize an unexecuted and unreported internal step. Verdict remains UNCHANGED as CONDITIONAL, because this concern reinforces the existing conditional disposition rather than requiring a different one.","tokens_in":9070,"tokens_out":4946,"duration_ms":46624,"concrete_test":"Request the authors' records for the test submissions made to AIID and AIAAIC under Section 3, step 4: the incident text submitted, date, submission mechanism, and any review or disposition response. If records do not exist, re-run the step by submitting one standardized test incident to each database and documenting the full lifecycle. Then verify whether Table 1's claims about review and voluntary reporting are consistent with the observed outcomes. If the submission outcomes are not recoverable and not reproducible, the central claim should be downgraded to a gap analysis of two public database dumps, not of reporting practices as experienced by reporters.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 3, Methodology step 4 states: 'Submitted an incident to each database to discern their reporting protocols and procedural intricacies.' This is a promised data-collection step, yet Section 4 (Results) contains no report of those submissions: no incident description, no submission date, no review outcome, no response time, and no procedural observation. Without this, Table 1's entries such as 'Submissions reviewed before publishing? Yes' and 'Nature of reporting: Voluntary' cannot be traced to an executed test, and the 'systematic' derivation of gaps—particularly Gap 2 (bias, inconsistencies, misclassification) and Gap 4 (inadequate motive to report)—rests on an unobservable step. The omission is not merely cosmetic: if a submitted incident was rejected, delayed, miscategorized, or never acknowledged, that would be direct evidence bearing on the gaps, and its absence prevents independent verification of the core claim that nine gaps were identified through the stated methodology. This is an internal completeness problem, distinct from the external generalizability question of whether AIID and AIAAIC represent all incident repositories. Additionally, Section 5.3 says 'only six fields are compatible' between the two databases, but Table 3 lists seven fields under 'Fields available in both AIID and AIAAIC', a counting inconsistency that further suggests the comparative analysis needs auditing.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript analyzes two open-access AI incident repositories, AIID and AIAAIC, to assess current AI incident reporting practices. It compares their reporting processes, data fields, contributor and source distributions, sector and geographic coverage, and data-sharing formats, then identifies nine gaps—ranging from missing standardized definitions and taxonomies to demographic underrepresentation and lack of awareness—and proposes nine corresponding recommendations for standardization. The paper frames these contributions as supporting trustworthy AI and the UN Sustainable Development Goals.","tokens_in":9283,"tokens_out":3261,"duration_ms":29920,"significance":"If the nine gaps are accepted as reliable observations, the paper offers a useful checklist for database maintainers, policymakers, and standards bodies such as ITU. Its main strengths are the transparent tabulation of publicly available data with retrieval dates, the direct mapping from each observation to a gap and recommendation, and the explicit acknowledgment of database limitations such as the absence of APIs and restricted access to certain fields. However, the analysis is based on only two repositories, and the recommendations are high-level and largely qualitative. The value of the central claim therefore depends on the completeness and accuracy of the comparative methodology and on how convincingly the two selected databases are shown to be representative of AI incident reporting practices more generally.","major_comments":[{"comment":"Section 3 lists as step 4: 'Submitted an incident to each database to discern their reporting protocols and procedural intricacies,' but Section 4 reports no results from these submissions—no incident description, submission date, acknowledgment, review outcome, or response time. This makes Table 1 entries such as 'Submissions reviewed before publishing? Yes' and 'Nature of reporting: Voluntary' untraceable to the stated methodology, and it weakens the evidence for Gap 2 (bias, inconsistencies, and misclassification) and Gap 4 (inadequate motive to report), both of which would plausibly be informed by the submission experience. The authors should either report the outcome of the test submissions in Section 4 or remove this step from the methodology.","section":"§3, methodology step 4 and §4"},{"comment":"Section 5.3 states that 'only six fields are compatible' between the two databases, but Table 3 lists seven fields under 'Fields available in both AIID and AIAAIC': Incident ID; Title/Headline; Description; Occurrence date; System deployer; System developer; and Alleged harmed or nearly harmed parties. This numerical inconsistency is load-bearing for Gap 3 (insufficient and incompatible data fields) and must be resolved by defining 'compatible' precisely and recounting the fields.","section":"§5.3 and Table 3"},{"comment":"The methodology shortlists AIID and AIAAIC from four repositories and excludes AILD and AVID because of their legal and vulnerability-focused scopes, but Sections 4 and 5 repeatedly generalize to 'existing AI incident reporting practices' and 'the AI-incident databases.' The paper should either justify why these two repositories are sufficiently representative to support the nine-gap claim or explicitly scope the conclusions to AIID and AIAAIC. This is not a demand for additional databases, but the inference from two cases to the field requires either evidence of representativeness or a clearly stated limitation.","section":"§3 and §5"}],"minor_comments":[{"comment":"Table 5 is titled 'Top seven source-domains of the reports in AIID' but lists eight domains; the count should be corrected or the table retitled.","section":"Table 5"},{"comment":"In Table 3, the shared field 'Alleged harmed or nearly or nearly harmed parties' contains a duplicated 'or nearly'; it should read 'Alleged harmed or nearly harmed parties.'","section":"Table 3"},{"comment":"The sentence in Section 4.7 that references Table 7 as evidence that AIID lacks country fields is confusing, since Table 7 lists deployers rather than geographic data; the cross-reference should be clarified.","section":"§4.7"},{"comment":"References [10] and [27] are self-citations used to support background claims about fairness assessment and incident reporting formalization; independent sources would strengthen the literature review, though this does not affect the central analysis.","section":"References"},{"comment":"Figure 1, the conceptualized AI lifecycle, is neither referenced nor explained in the text; a brief discussion of how the lifecycle stages follow from the identified gaps would improve readability.","section":"Figure 1"}],"recommendation":"major_revision","confidential_remarks":"The missing report of the test submissions is the most serious issue: the methodology explicitly promises a data-collection step that never appears in the results, and the paper's systematic-methodology claim rests on it. The field-count inconsistency in Section 5.3 versus Table 3 is small but symptomatic of the need for a careful audit of the comparative tables. I would not insist on expanding the database sample beyond AIID and AIAAIC, but the authors must either provide evidence of representativeness or explicitly scope their conclusions. The SDG framing is mostly motivational and does not interfere with the core analysis."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"You should know this paper is a modest policy analysis, not a research breakthrough. It compares two open-access AI incident databases, AIID and AIAAIC, and derives nine gaps and nine recommendations. The empirical snapshot is the best part: clean tables showing submission counts, contributor concentration, source domains, sector and country skew, and data-sharing formats. That is genuinely useful reference material for anyone working on AI governance or incident reporting standards. The identified gaps—definitional inconsistency, voluntary reporting, narrow reporter base, incompatible fields, geographic skew—are real and consistent with what Turri and Dzombak and Lupo already said. The recommendations are pragmatic and not overwrought. The soft spots are real but not fatal. The stress-test note is correct and I think it matters: Section 3 promises a test submission to each database, but Section 4 never reports what happened. That is not just cosmetic. Gap 2, about bias and misclassification, could have been directly supported by observing whether a submitted incident was rejected, miscategorized, or ignored. The absence makes the claimed 'systematic methodology' unverifiable at a load-bearing point. There is also a minor counting error: Section 5.3 says only six fields are compatible, but Table 3 lists seven under 'Fields available in both AIID and AIAAIC.' That is sloppy and needs correcting. The paper also generalizes from two databases to 'current AI incident reporting practices' without justifying that AIID and AIAAIC are representative. The authors should either narrow their claims or broaden their sample. The self-citations are not a problem; they are background and not load-bearing. Overall: the central observation—that AI incident reporting is under-standardized—holds up, and the paper's evidence would convince most readers. But the unexecuted-looking test submission and the field-count inconsistency undercut the paper's internal quality. It is a paper for policymakers and AI governance practitioners, not for methodology researchers. I would send it to peer review rather than desk-reject, because the topic is timely and the empirical tables are worth publishing. But I would require revision: report the test submission outcomes or drop that step from the methodology, fix the field count, and explicitly limit the conclusions to the two databases analyzed. With those changes, it would be an acceptable contribution to the governance literature.","headline":"A readable, moderately useful gap analysis of two AI incident databases, but it overstates its systematic methodology and needs the test-submission step reported and a field-count error fixed before I'd trust its details.","tokens_in":719,"tokens_out":723,"would_cite":false,"duration_ms":20975,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Nine gaps block AI incident reporting from preventing harm, the paper argues.","keywords":["AI incident reporting","AI incident database","AI harm","standardization","taxonomy","sustainable development","trustworthy AI","gap analysis"],"falsifier":"Apply the same methodology to another open repository, such as AVID or the OECD AI Incidents Monitor: if one of them already has a standard taxonomy, interoperable data fields, APIs, and adequate sector and country coverage, then the nine gaps are not general features of current AI incident reporting practices.","tokens_in":8827,"feed_emoji":"🚨","tokens_out":3969,"duration_ms":35552,"temperature":0.7,"pith_summary":"The paper sets out to show that current open-access AI incident reporting practices are not standardised enough to support learning from past failures. It identifies nine specific gaps by comparing two public incident databases, AIID and AIAAIC, and proposes nine recommendations to close them. If true, this means the growing collection of AI incident reports cannot yet be pooled, compared, or relied upon to prevent future harm, which matters for any effort to make AI trustworthy and to align AI with the UN Sustainable Development Goals.","feed_headline":"Nine gaps block AI incident reporting from preventing harm","feed_subtitle":"A close look at two open databases shows why incident data can't yet be pooled, trusted, or fairly compared.","key_machinery":"The argument is carried by a gap-analysis framework: the paper tabulates observations from the two databases (reporting basics, data fields, top submitters, source domains, sectors, countries, and download formats) and converts each observed deficiency into an inference about a systemic gap and then into a specific standardisation recommendation. The framework treats the OECD, AIID, and AIAAIC definitions of an AI incident as a reference point to expose definitional inconsistency, and it uses the structural comparison of the two repositories to expose interoperability and coverage gaps.","core_discovery":"The central claim is that existing open-access AI incident reporting, as represented by AIID and AIAAIC, has nine identifiable standardization gaps: lack of definitions and taxonomies, bias and misclassification, insufficient and incompatible data fields, inadequate reporting incentives, a narrow reporter base, inadequate data-sharing protocols, sectoral underrepresentation, demographic underrepresentation, and lack of awareness. The paper grounds this claim in a systematic comparison of the two databases' reporting procedures, data structures, contributor and source profiles, sector and country coverage, and data-sharing formats, then maps each gap to a corresponding recommendation for standardisation.","pith_inferences":["Because the nine gaps were derived from only two repositories, they are likely a lower bound; applying the same method to AVID, AILD, or OECD AIM would probably reveal additional or overlapping gaps rather than fewer.","The recommendation for ITU-led coordination presumes that international standardisation bodies can act faster than voluntary database maintainers; an alternative, possibly faster path would be a shared open schema adopted directly by existing repositories.","The observation that AIAAIC restricts harm data to premium members implies an access inequity that may itself bias which researchers and stakeholders can study incidents, an equity dimension the paper does not explicitly name.","The bias and misclassification gap is directly testable: have multiple reviewers independently classify the same sample of incidents and measure inter-rater agreement to quantify how inconsistent current manual classification is."],"forward_implications":["Adopting standard taxonomies and database structures would allow incident data from multiple repositories to be merged, compared, and analysed across sectors and jurisdictions.","Mandatory or incentivised reporting, supported by regulatory frameworks, would reduce the current dependence on a small number of volunteer submitters and on English-language media coverage.","Standards for automated incident reporting would let AI systems surface incidents directly, widening the reporter base beyond human volunteers.","Sector-specific databases would bring critical infrastructure areas such as telecom and electricity into the incident record, which are now underrepresented.","Integrating incident reporting into the AI lifecycle would make data collection a routine part of system development rather than an afterthought."],"supporting_citations":[{"why":"Supplies the OECD definition of an AI incident that the paper uses as a baseline for showing definitional inconsistency.","marker":"[15]"},{"why":"AIID is one of the two open-access databases analysed; its incident records, fields, and reporting process are the paper's primary evidence.","marker":"[16]"},{"why":"AIAAIC is the second analysed database; its incident records, fields, and reporting process provide the comparative evidence for the gaps.","marker":"[17]"},{"why":"Prior documentation of the state of AI incident documentation practices, which this study extends with its own gap analysis.","marker":"[21]"},{"why":"Describes the AI Incident Database and its cataloguing purpose, grounding the paper's account of AIID's role.","marker":"[22]"},{"why":"Argues that incidents play a role in AI regulation and that current repositories lack robust technical input, supporting the need for standardisation.","marker":"[18]"},{"why":"Supports the value of sharing incidents for verifiable claims and external scrutiny, motivating the paper's call for better reporting.","marker":"[19]"}],"fun_headline_variants":["Nine gaps stall AI incident reporting standards","AI incident databases show nine key gaps","Study finds nine gaps in AI risk reporting","Nine reporting gaps undermine AI trustworthiness","AI incident data gaps: nine fixes proposed"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The paper assumes that AIID and AIAAIC are sufficiently representative of current AI incident reporting practices that the gaps found in them apply to the whole field of AI incident reporting.","fun_headline_variants_meta":{"raw":{"variants":["Nine gaps stall AI incident reporting standards","AI incident databases show nine key gaps","Study finds nine gaps in AI risk reporting","Nine reporting gaps undermine AI trustworthiness","AI incident data gaps: nine fixes proposed"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000138,"raw_usage":{"total_tokens":1078,"prompt_tokens":796,"completion_tokens":282,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":412,"completion_tokens_details":{"reasoning_tokens":219}},"tokens_in":412,"tokens_out":282,"duration_ms":3464,"temperature":1.0,"reasoning_tokens":219,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T22:38:52.193553+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Apply the same methodology to another open repository, such as AVID or the OECD AI Incidents Monitor: if one of them already has a standard taxonomy, interoperable data fields, APIs, and adequate sector and country coverage, then the nine gaps are not general features of current AI incident reporting practices.","supporting_citations":[{"cited_title":"Stocktaking for the development of an AI incident definition","cited_arxiv_id":null,"evidence_quote":"Supplies the OECD definition of an AI incident that the paper uses as a baseline for showing definitional inconsistency."},{"cited_title":"AI Incident Database","cited_arxiv_id":null,"evidence_quote":"AIID is one of the two open-access databases analysed; its incident records, fields, and reporting process are the paper's primary evidence."},{"cited_title":"AIAAIC Repository","cited_arxiv_id":null,"evidence_quote":"AIAAIC is the second analysed database; its incident records, fields, and reporting process provide the comparative evidence for the gaps."},{"cited_title":"Why We Need to Know More: Exploring the State of AI Incident Documentation Practices","cited_arxiv_id":null,"evidence_quote":"Prior documentation of the state of AI incident documentation practices, which this study extends with its own gap analysis."},{"cited_title":"Preventing repeated real world AI failures by cataloging incidents: The AI incident database","cited_arxiv_id":null,"evidence_quote":"Describes the AI Incident Database and its cataloguing purpose, grounding the paper's account of AIID's role."},{"cited_title":"Risky artificial intelligence: The role of incidents in the path to AI regulation.Law, Technology and Humans, 5(1):133–152, 2023","cited_arxiv_id":null,"evidence_quote":"Argues that incidents play a role in AI regulation and that current repositories lack robust technical input, supporting the need for standardisation."}],"review_version":1}