Procurement AI Paradox: Enterprise Buying Cycles vs Model Deprecation Velocity

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Gap Research
Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21150688  56stabilfr·wdophcgmx
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[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI60%○≥80% have a Digital Object Identifier
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[i]Indexed0%○≥80% have metadata indexed
[l]Academic87%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]992✗Minimum 2,000 words for a full research article. Current: 992
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21150688
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]86%✓≥60% of references from 2025–2026. Current: 86%
[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 (59 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise adoption of artificial intelligence suffers from a structural mismatch between procurement timelines and model release frequencies. This article quantifies the misalignment between 18‑24 month enterprise buying cycles and 6‑month AI model deprecation cycles, identifies established mitigation strategies, and evaluates their effectiveness using empirical metrics. We formulate three res...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.21150688 56stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI60%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic87%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]992✗Minimum 2,000 words for a full research article. Current: 992
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21150688
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]86%✓≥60% of references from 2025–2026. Current: 86%
[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 (59 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Citation Hallucination Rates in LLM-Generated Research: A 2025 Benchmark Across 10 Models

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21131031  50stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI50%○≥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]References2 refs○Minimum 10 references required
[w]Words [REQ]1,651✗Minimum 2,000 words for a full research article. Current: 1,651
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21131031
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Citation hallucination — the generation of fabricated bibliographic references by large language models (LLMs) — poses a critical reproducibility risk for AI‑driven scholarly output. This article benchmarks citation hallucination rates across ten leading LLMs released between 2023 and 2025, measuring the prevalence of fabricated citations in response to standardized research‑question prompts. W...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.21131031 50stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI50%○≥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]References2 refs○Minimum 10 references required
[w]Words [REQ]1,651✗Minimum 2,000 words for a full research article. Current: 1,651
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21131031
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Financial AI Observability: Explaining Credit and Trading Decisions in Real-Time

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21120102  63stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted96%✓≥80% from verified, high-quality sources
[a]DOI91%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic96%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References23 refs✓Minimum 10 references required
[w]Words [REQ]1,536✗Minimum 2,000 words for a full research article. Current: 1,536
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21120102
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]83%✓≥60% of references from 2025–2026. Current: 83%
[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%)

This article investigates the regulatory landscape surrounding explanation quality monitoring for financial artificial intelligence systems deployed in credit scoring and algorithmic trading environments. Recent regulatory initiatives have begun to mandate transparent explanatory mechanisms for AI-driven financial decisions, aiming to enhance consumer protection and systemic risk mitigation. Ho...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.21120102 63stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted96%✓≥80% from verified, high-quality sources
[a]DOI91%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic96%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References23 refs✓Minimum 10 references required
[w]Words [REQ]1,536✗Minimum 2,000 words for a full research article. Current: 1,536
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21120102
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]83%✓≥60% of references from 2025–2026. Current: 83%
[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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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
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[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
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[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
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[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
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[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
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[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 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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AI Transparency as Competitive Moat: Why Explainability Creates Sustainable Advantage

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20401398  70stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources50%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI79%○≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]954✗Minimum 2,000 words for a full research article. Current: 954
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20401398
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]85%✓≥60% of references from 2025–2026. Current: 85%
[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 (82 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

AI transparency has emerged as a critical strategic asset for enterprises seeking sustainable competitive advantage in the rapidly evolving artificial intelligence market. This article presents a strategic analysis of how explainability and transparency in AI systems translate into tangible economic benefits, including premium pricing, enhanced trust, compliance savings, and innovation accelera...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.20401398 70stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources50%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI79%○≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic86%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]954✗Minimum 2,000 words for a full research article. Current: 954
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20401398
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]85%✓≥60% of references from 2025–2026. Current: 85%
[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 (82 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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