World Congress 2026 Europe - Virtual Stage Jul 2, 2026 Session details

arc42 Meets AI: From Black Box to Blueprint

Nikita Golovko

Responsible AI isn't just an ethics issue—it’s an architectural problem. Discover how ARC AI 42 transforms untrackable machine learning models into unified, transparent blueprints.

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#1 about 3 min

Extending traditional software architecture frameworks for artificial intelligence

Standardizing architectural documentation addresses the unpredictable nature of machine learning models.

#2 about 2 min

Identifying architectural failures through regulatory audit use cases

High-risk automated decisions require traceable documentation for compliance and explainability.

#3 about 4 min

Addressing seven common documentation gaps in production systems

Resolving issues like hidden data dependencies and semantic drift ensures continuous model reliability.

#4 about 4 min

Differentiating artificial intelligence systems from classical deterministic software

Managing probabilistic outputs and continuous life cycles requires a coherent system view beyond standalone tools.

#5 about 2 min

Splitting architectural specifications from operational evidence tracking

Separating system requirements from technical implementation proofs maintains clear accountability.

#6 about 5 min

Defining five target architecture views for machine learning

Structuring documentation across data, explainability, operations, and risk creates a comprehensive system map.

#7 about 6 min

Applying practical frameworks for operational machine learning maturity

Adopting phased governance and dynamic continuous integration pipelines prevents theoretical frameworks from failing in practice.

#8 about 3 min

Integrating accountability layers into existing software architecture documentation

Embedding machine learning requirements into established templates transforms responsible artificial intelligence from an ethical concept into an architectural standard.

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Implementing an architecture blueprint for continuous validation

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Designing complex software architecture in the era of AI

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Navigating accountability and architecture in AI generated code

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The emergence of architectural risks in AI workflows

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Understanding the landscape of AI capabilities and risks

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