title: AIGov
emoji: 🧭
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colorTo: indigo
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short_description: Practical AI governance infrastructure
AIGov
AIGov builds infrastructure for transparent, auditable, and accountable AI systems.
We focus on practical AI governance engineering across model evaluation, agent reliability, traceability, lifecycle controls, and regulatory readiness.
What we work on
- AI governance and compliance infrastructure
- evaluation and monitoring of AI systems
- agent reliability and execution-trace analysis
- transparency and auditability
- lifecycle governance
- model and system documentation
- responsible AI engineering
Current Hugging Face work
AIGovDev is used to publish selected public demos, datasets, models, and curated resources related to AI governance and responsible AI.
Public work will include:
- interactive governance demonstrations
- evaluation datasets
- responsible AI benchmarks
- agent reliability tooling
- governance-oriented ML experiments
Approach
We treat AI governance as an engineering discipline.
The goal is not only to document AI systems, but to make governance requirements observable, testable, reproducible, and integrated into technical workflows.
Focus areas
Evaluation
Methods and tooling for assessing model and system behaviour, including reliability, failure modes, and response-level evaluation.
Agent governance
Observable execution traces, tool-use behaviour, verification, failure handling, and reliability signals in agentic systems.
Traceability
Technical mechanisms for reconstructing how AI systems behaved, what evidence was used, and how decisions were produced.
Responsible AI
Practical implementation of transparency, accountability, oversight, monitoring, and risk-management principles.
Public resources
This organization will progressively publish:
- Spaces
- datasets
- models
- collections
- research-oriented prototypes
AIGov
AIGov is developed by AIMLGov, s.r.o.
Website: https://govbase.dev
GitHub: https://github.com/AIGovDev