The Trusted MLOps Stack: Open Source Tools for Reproducible AI with Explanations

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20359688  70stabilfr·wuedophcgmx
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[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed22%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access84%✓≥80% are freely accessible
[r]References51 refs✓Minimum 10 references required
[w]Words [REQ]1,840✓Minimum 1,000 words of article body. Current: 1,840
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]46✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 46
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20359688
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness55%○≥60% of references from 2025–2026. Current: 55%
[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 (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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 70stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources4%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed22%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access84%✓≥80% are freely accessible
[r]References51 refs✓Minimum 10 references required
[w]Words [REQ]1,840✓Minimum 1,000 words of article body. Current: 1,840
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]46✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 46
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20359688
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness55%○≥60% of references from 2025–2026. Current: 55%
[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 (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 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  63stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]2,710✓Minimum 1,000 words of article body. Current: 2,710
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]26✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 26
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20274804
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness93%✓≥60% of references from 2025–2026. Current: 93%
[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 (48 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

The rapid adoption of explainable artificial intelligence (XAI) tools within open sourceMachine Learning (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 securi...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20274804 63stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]2,710✓Minimum 1,000 words of article body. Current: 2,710
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]26✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 26
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20274804
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness93%✓≥60% of references from 2025–2026. Current: 93%
[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 (48 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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The Legal Industry AI Transformation: From Research to Courtroom

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20168865  62stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic58%○≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,263✓Minimum 1,000 words of article body. Current: 2,263
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20168865
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness27%○≥60% of references from 2025–2026. Current: 27%
[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 (48 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

The legal services sector is undergoing a profound transformation driven by artificial intelligence technologies that reshape economics and workflows across core domains. This article systematically investigates AI’s impact on e-discovery, contract analysis, legal writing, and courtroom preparation, addressing three critical research questions: (RQ1) How has AI altered cost structures and effic...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.20168865 62stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic58%○≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,263✓Minimum 1,000 words of article body. Current: 2,263
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20168865
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness27%○≥60% of references from 2025–2026. Current: 27%
[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 (48 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Trusted Open Source AI in Healthcare: Curated Stack for Clinical AI

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23478153  88stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources56%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI89%✓≥80% have a Digital Object Identifier
[b]CrossRef67%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,626✓Minimum 1,000 words of article body. Current: 1,626
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]6✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 6
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23478153
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness88%✓≥60% of references from 2025–2026. Current: 88%
[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 (90 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

Building trusted open source AI infrastructure for healthcare requires a curated stack that ensures transparency, reliability, and safety from imaging to clinical decision support. This article addresses three research questions: (RQ1) What are the key components of a trusted open source AI stack for healthcare clinical decision support? (RQ2) How can open source AI tools be integrated to ensur...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.23478153 88stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources56%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI89%✓≥80% have a Digital Object Identifier
[b]CrossRef67%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,626✓Minimum 1,000 words of article body. Current: 1,626
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]6✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 6
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23478153
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness88%✓≥60% of references from 2025–2026. Current: 88%
[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 (90 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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The Open Source XAI Ecosystem: Gaps, Opportunities, and Trusted Projects to Watch

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20115253  71stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI76%○≥80% have a Digital Object Identifier
[b]CrossRef3%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References38 refs✓Minimum 10 references required
[w]Words [REQ]2,796✓Minimum 1,000 words of article body. Current: 2,796
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]31✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 31
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20115253
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness67%✓≥60% of references from 2025–2026. Current: 67%
[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 (62 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

Explainable Artificial Intelligence (XAI) has moved from niche academic curiosity to a cornerstone of responsible AI deployment in enterprises worldwide. Recent industry surveys indicate that 68% of Fortune 500 companies now require interpretability mechanisms for any production model, yet only 24% of open-source AI libraries provide robust, production-grade explanation tools (see [1], [2], [3]...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.20115253 71stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI76%○≥80% have a Digital Object Identifier
[b]CrossRef3%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic79%○≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References38 refs✓Minimum 10 references required
[w]Words [REQ]2,796✓Minimum 1,000 words of article body. Current: 2,796
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]31✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 31
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20115253
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness67%✓≥60% of references from 2025–2026. Current: 67%
[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 (62 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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War and Shadow Economy — How Armed Conflict Reshapes Informal Markets in Ukraine

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.23454463  90stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources67%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI92%✓≥80% have a Digital Object Identifier
[b]CrossRef75%○≥80% indexed in CrossRef
[i]Indexed75%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,567✓Minimum 1,000 words of article body. Current: 1,567
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23454463
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness100%✓≥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]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (95 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Armed conflict profoundly reshapes economic landscapes, particularly through the transformation of informal market structures. This study examines how armed conflict in Ukraine has altered informal market dynamics, focusing on the shadow economy's adaptation to wartime conditions. By synthesizing recent empirical evidence from diverse conflict-affected regions, we model the mechanisms through w...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.23454463 90stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources67%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI92%✓≥80% have a Digital Object Identifier
[b]CrossRef75%○≥80% indexed in CrossRef
[i]Indexed75%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,567✓Minimum 1,000 words of article body. Current: 1,567
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23454463
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness100%✓≥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]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (95 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Blockchain-Based Tax Compliance — Smart Contracts for Automated VAT Collection

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Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.20039119  74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI87%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]4,067✓Minimum 1,000 words of article body. Current: 4,067
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20039119
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness77%✓≥60% of references from 2025–2026. Current: 77%
[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 (67 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

Blockchain technology presents a transformative opportunity for tax administration, particularly in automating Value-Added Tax (VAT) collection through programmable smart contracts.[1] This article systematically investigates architectures for smart contract-enabled VAT compliance, addressing the critical need for reliable, transparent, and efficient tax reporting mechanisms in decentralized fi...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.20039119 74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI87%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]4,067✓Minimum 1,000 words of article body. Current: 4,067
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]25✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 25
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20039119
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness77%✓≥60% of references from 2025–2026. Current: 77%
[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 (67 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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Formal Methods for XAI Verification: Proving That Explanations Are Correct

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Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23462264  88stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources43%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
[i]Indexed71%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]3,948✓Minimum 1,000 words of article body. Current: 3,948
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23462264
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness100%✓≥60% of references from 2025–2026. Current: 100%
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[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 (90 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

This article presents 150 key facts about Using formal verification techniques to validate that explanations accurately reflect model behavior.. These facts cover various aspects of the topic, providing a comprehensive overview for researchers and practitioners. The compilation draws upon existing scholarship, including foundational work [1].

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.23462264 88stabilfr·wuedophcgmx
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[s]Reviewed Sources43%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef71%○≥80% indexed in CrossRef
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]3,948✓Minimum 1,000 words of article body. Current: 3,948
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23462264
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness100%✓≥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]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (90 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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The EU AI Act Explanability Requirements: Technical Specification Analysis

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19993955  83stabilfr·wuedophcgmx
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[s]Reviewed Sources61%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI72%○≥80% have a Digital Object Identifier
[b]CrossRef61%○≥80% indexed in CrossRef
[i]Indexed61%○≥80% have metadata indexed
[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,081✓Minimum 1,000 words of article body. Current: 2,081
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19993955
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (78 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)

The rapid deployment of artificial intelligence systems across high‑risk domains has prompted regulators to demand greater transparency and accountability. The European Union’s Artificial Intelligence Act (EU AI Act) introduces a comprehensive framework for trustworthy AI, with particular emphasis on explicability obligations for high‑risk AI systems. This article dissects the technical specifi...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19993955 83stabilfr·wuedophcgmx
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[s]Reviewed Sources61%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI72%○≥80% have a Digital Object Identifier
[b]CrossRef61%○≥80% indexed in CrossRef
[i]Indexed61%○≥80% have metadata indexed
[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,081✓Minimum 1,000 words of article body. Current: 2,081
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]12✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19993955
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness80%✓≥60% of references from 2025–2026. Current: 80%
[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 (78 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)
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Interpretable Models vs Post-Hoc Explanations: True Cost Comparison for Enterprise AI

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23467627  83stabilfr·wuedophcgmx
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[s]Reviewed Sources42%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef42%○≥80% indexed in CrossRef
[i]Indexed42%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,217✓Minimum 1,000 words of article body. Current: 2,217
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23467627
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (81 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

This article presents a comprehensive economic analysis comparing inherently interpretable models versus post-hoc explanation techniques for complex black-box models in enterprise AI deployments. We evaluate five distinct scenarios spanning low to high data complexity, real-time inference constraints, and regulated environments requiring full audit trails. Our methodology incorporates total cos...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.23467627 83stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources42%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
[b]CrossRef42%○≥80% indexed in CrossRef
[i]Indexed42%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,217✓Minimum 1,000 words of article body. Current: 2,217
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23467627
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness64%✓≥60% of references from 2025–2026. Current: 64%
[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 (81 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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