Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting

Avinash Agarwal; Manisha J Nene

REVIEW 3 major objections 5 minor 33 references

Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Nine gaps block AI incident reporting from preventing harm, the paper argues.

desk verdict 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. read the letter →

arxiv 2501.14778 v1 pith:PHS2CEMG submitted 2025-01-01 cs.CY cs.AIcs.HC

Avinash Agarwal , Manisha J Nene This is my paper · ORCID

classification cs.CYcs.AIcs.HC

keywords AIincidentreportingdatabaseharmstandardizationtaxonomysustainabledevelopmenttrustworthygapanalysis

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

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.

What carries the argument

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.

What would settle it

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.

Watch

Extended reading notes

Core claim

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.

Load-bearing premise

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.

Editorial extensions

If this is right

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.

Reading between the lines

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

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.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the 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.

Referee Report

3 major / 5 minor

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.

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 (3)

[§3, methodology step 4 and §4] 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.
[§5.3 and Table 3] 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.
[§3 and §5] 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.

minor comments (5)

[Table 5] 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.
[Table 3] 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.'
[§4.7] 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.
[References] 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.
[Figure 1] 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.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the nine gaps and recommendations rest on direct database observations, and the two self-citations are background support only.

full rationale

The paper's central derivation is observational rather than formal: Section 4 presents direct comparisons of AIID and AIAAIC (incident counts, reporting and review policies, data fields, submitter and source distributions, sector and country coverage, and data-sharing formats), and Section 5 derives each of the nine gaps from those tables and pairs each gap with a recommendation. There are no fitted parameters, predictive equations, or imported uniqueness theorems whose conclusions equal their inputs by construction. The only self-citations are reference [10], cited for the general need for standards and assessment throughout the AI lifecycle, and reference [27], cited in Section 2.3 for the absence of federally operated databases and mandatory legal disclosure. Neither is load-bearing: the gap analysis depends on Tables 1-9 of the present paper, not on those prior works. The unreported test submission in Methodology step 4 and the apparent discrepancy between Section 5.3's 'only six fields are compatible' and Table 3's seven shared fields are auditability and accuracy concerns, but they do not show that any claimed result was equivalent to its input by construction. Accordingly, no circularity is found.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new entities or fitted parameters. It relies on domain assumptions about the value of incident reporting and a representativeness assumption about the two selected databases.

assumptions (3)

domain assumption The OECD definition of an AI incident is the appropriate standard for evaluating what counts as an incident.
Used throughout Section 2.1 and Section 5.1 to judge whether certain AIAAIC entries qualify as AI incidents.
domain assumption Systematic incident reporting, analogous to aviation and cybersecurity, leads to reduced future harm and improved safety.
Invoked in the introduction and Section 2.2 to motivate the entire study, drawing on the aviation analogy.
ad hoc to paper The two shortlisted open-access databases (AIID and AIAAIC) are sufficiently representative of AI incident reporting practices to support general claims.
Section 3, Methodology step 2, selects these two while excluding AVID, AILD, and the OECD AIM, but the paper generalizes to 'current AI incident reporting practices' in the abstract.

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Cite this review

Pith. "Pith review of Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting." pith.science (2026). https://pith.science/paper/PHS2CEMG

@misc{pith2026250114778,
  author       = {Pith},
  title        = {Pith review of: Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PHS2CEMG}},
  note         = {Machine review of arXiv:2501.14778}
}

read the original abstract

The increasing use of AI technologies has led to increasing AI incidents, posing risks and causing harm to individuals, organizations, and society. This study recognizes and addresses the lack of standardized protocols for reliably and comprehensively gathering such incident data crucial for preventing future incidents and developing mitigating strategies. Specifically, this study analyses existing open-access AI-incident databases through a systematic methodology and identifies nine gaps in current AI incident reporting practices. Further, it proposes nine actionable recommendations to enhance standardization efforts to address these gaps. Ensuring the trustworthiness of enabling technologies such as AI is necessary for sustainable digital transformation. Our research promotes the development of standards to prevent future AI incidents and promote trustworthy AI, thus facilitating achieving the UN sustainable development goals. Through international cooperation, stakeholders can unlock the transformative potential of AI, enabling a sustainable and inclusive future for all.

Discussion (0). Continue with ORCID to comment.

Reference graph

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Reviewed August 10, 2026 · model on record in the stance chip above.

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