Category: Trusted Open Source

Systematic evaluation of open-source projects through verified metrics — GitHub activity, community health, and industry impact. Data-driven rankings using reproducible methodology applied to the top repositories of 2026.

The Open Source AI Trust Gap: When Community Projects Do Not Meet Enterprise Standards

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20483567  76stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources64%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI93%✓≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,826✗Minimum 2,000 words for a full research article. Current: 1,826
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20483567
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (93 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprises increasingly rely on artificial intelligence (AI) to gain competitive advantage, yet many hesitate to adopt open source AI solutions despite their technical promise and cost efficiency. This hesitation stems from a growing trust gap—a mismatch between the expectations of corporate stakeholders and the capabilities, governance, and reliability of community‑driven AI projects. Bridgin...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20483567 76stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources64%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI93%✓≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,826✗Minimum 2,000 words for a full research article. Current: 1,826
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20483567
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (93 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Cross-Industry AI Transparency Stacks: Open Source Reference Architectures for XAI

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20422658  38stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]1,508✗Minimum 2,000 words for a full research article. Current: 1,508
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20422658
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

This article presents a comprehensive framework for building cross-industry explainable AI (XAI) transparency stacks, which are modular architectures designed to provide interpretable insights across diverse domains. As regulatory pressures mount for increased AI transparency, organizations require standardized yet adaptable frameworks to deploy XAI solutions that maintain operational efficienc...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20422658 38stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References6 refs○Minimum 10 references required
[w]Words [REQ]1,508✗Minimum 2,000 words for a full research article. Current: 1,508
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20422658
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20417244  61stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI81%✓≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]1,879✗Minimum 2,000 words for a full research article. Current: 1,879
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20417244
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]79%✓≥60% of references from 2025–2026. Current: 79%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Privacy-preserving machine l[REDACTED]g has matured into a diverse ecosystem of algorithms, protocols, and tooling designed to enable collaborative model training without e[REDACTED]sing raw data. Concurrently, explainable artificial intelligence (XAI) has emerged as a critical complement, granting stakeholders insight into model decisions while maintaining data confidentiality. This article su...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20417244 61stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI81%✓≥80% have a Digital Object Identifier
[b]CrossRef5%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]1,879✗Minimum 2,000 words for a full research article. Current: 1,879
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20417244
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]79%✓≥60% of references from 2025–2026. Current: 79%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Bus Factor of XAI: Community Risk in Critical Open Source Explainability Tools

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20410657  61stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]1,105✗Minimum 2,000 words for a full research article. Current: 1,105
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20410657
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]71%✓≥60% of references from 2025–2026. Current: 71%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Explainability in artificial intelligence (AI) systems has become a pivotal concern for researchers, regulators, and practitioners seeking to deploy trustworthy AI solutions. While numerous frameworks and toolkits promise transparent model behavior, the sustainability of these open source initiatives often hinges on the concentration of maintainer resources—a modern manifestation of the classic...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20410657 61stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]1,105✗Minimum 2,000 words for a full research article. Current: 1,105
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20410657
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]71%✓≥60% of references from 2025–2026. Current: 71%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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License Implications for XAI Attribution: Legal Analysis of Open Source Explanation Dependencies

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20404245  59stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted91%✓≥80% from verified, high-quality sources
[a]DOI78%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References23 refs✓Minimum 10 references required
[w]Words [REQ]1,882✗Minimum 2,000 words for a full research article. Current: 1,882
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20404245
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]70%✓≥60% of references from 2025–2026. Current: 70%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (64 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Abstract The rapid expansion of explainable artificial intelligence (XAI) systems raises legal questions about the use of open source components in explanatory modules. This article investigates how open source licenses affect attribution requirements, copyleft obligations, and commercial deployment strategies. We formulate three research questions: (1) Which licenses impose attribution duties ...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20404245 59stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted91%✓≥80% from verified, high-quality sources
[a]DOI78%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References23 refs✓Minimum 10 references required
[w]Words [REQ]1,882✗Minimum 2,000 words for a full research article. Current: 1,882
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20404245
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]70%✓≥60% of references from 2025–2026. Current: 70%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (64 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Open Source AI in Government: Curated Trusted Stack for Public Sector AI

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20374059  71stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI96%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
[w]Words [REQ]2,354✓Minimum 2,000 words for a full research article. Current: 2,354
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20374059
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]96%✓≥60% of references from 2025–2026. Current: 96%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

Government agencies are increasingly looking to artificial intelligence (AI) to modernize procurement workflows, strengthen fraud detection pipelines, and improve the delivery of public services while operating under tight budgetary constraints. Recent surveys reveal that more than 65 % of public‑sector technology officers consider open source AI components essential for achieving cost‑efficien...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20374059 71stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI96%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
[w]Words [REQ]2,354✓Minimum 2,000 words for a full research article. Current: 2,354
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20374059
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]96%✓≥60% of references from 2025–2026. Current: 96%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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The Trusted MLOps Stack: Open Source Tools for Reproducible AI with Explanations

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Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20359688  53stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI73%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed22%○≥80% have metadata indexed
[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access84%✓≥80% are freely accessible
[r]References49 refs✓Minimum 10 references required
[w]Words [REQ]1,029✗Minimum 2,000 words for a full research article. Current: 1,029
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20359688
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]57%✗≥60% of references from 2025–2026. Current: 57%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (64 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Explainability in artificial intelligence remains a critical barrier to adoption in safety‑critical domains such as healthcare, finance, and autonomous systems. While many commercial platforms tout built‑in interpretability, they often lock users into proprietary ecosystems and obscure the underlying model internals. This article presents a fully open source stack that enables reproducible, aud...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20359688 53stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI73%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed22%○≥80% have metadata indexed
[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access84%✓≥80% are freely accessible
[r]References49 refs✓Minimum 10 references required
[w]Words [REQ]1,029✗Minimum 2,000 words for a full research article. Current: 1,029
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20359688
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]57%✗≥60% of references from 2025–2026. Current: 57%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (64 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Reproducibility in XAI Research: Open Source Benchmarks for Explanation Quality

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Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20318088  63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted96%✓≥80% from verified, high-quality sources
[a]DOI89%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]1,257✗Minimum 2,000 words for a full research article. Current: 1,257
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20318088
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]81%✓≥60% of references from 2025–2026. Current: 81%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Accurate and reproducible evaluation of explanation fidelity is essential for advancing XAI research. While several metrics have been proposed, no standardized benchmark framework exists that enables systematic comparison across methods. This article presents an open-source benchmark suite designed to assess explanation quality across multiple XAI techniques. Drawing on recent literature [1], w...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20318088 63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted96%✓≥80% from verified, high-quality sources
[a]DOI89%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]1,257✗Minimum 2,000 words for a full research article. Current: 1,257
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20318088
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]81%✓≥60% of references from 2025–2026. Current: 81%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Supply Chain Security in Open Source AI: Auditing XAI Tool Dependencies

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20274804  57stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
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[t]Trusted100%✓≥80% from verified, high-quality sources
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[b]CrossRef0%○≥80% indexed in CrossRef
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[l]Academic97%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References29 refs✓Minimum 10 references required
[w]Words [REQ]2,769✓Minimum 2,000 words for a full research article. Current: 2,769
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20274804
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The rapid adoption of explainable artificial intelligence (XAI) tools within open sourceMachine L[REDACTED]g (ML) ecosystems has amplified concerns regarding supply chain security. While XAI techniques enhance model transparency, their integration often relies on third‑party libraries, data pipelines, and inference services that introduce hidden vulnerabilities. This article investigates the se...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20274804 57stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI3%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic97%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References29 refs✓Minimum 10 references required
[w]Words [REQ]2,769✓Minimum 2,000 words for a full research article. Current: 2,769
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20274804
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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Community Governance Models for Open Source AI Projects: What Makes XAI Projects Trustworthy

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Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20277714  51stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]1,036✗Minimum 2,000 words for a full research article. Current: 1,036
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20277714
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Open source artificial intelligence (AI) projects are increasingly shaping technological trajectories, yet their governance structures often remain opaque, undermining trustworthiness assessments. This article investigates how community-driven governance models affect the perceived trustworthiness of explainable AI (XAI) initiatives. We pose three research questions: (1) What governance models ...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20277714 51stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic67%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]1,036✗Minimum 2,000 words for a full research article. Current: 1,036
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20277714
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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