Funding
AIClarum Raises $4M Seed Round Led by First Round Capital
Austin-based AI transparency startup secures $4M in seed funding to scale its compliance automation platform across regulated industries.
Blog
AI Transparency Insights
Deep dives into AI explainability, compliance automation, bias monitoring, and the evolving regulatory landscape.
Compliance
A Practical Guide to EU AI Act High-Risk System Classification
Which AI systems qualify as high-risk under the EU AI Act? How do the Annexes determine your compliance obligations?
Engineering
Running SHAP Explanations in Production: Latency and Cost Optimization
How to compute SHAP values at scale without sacrificing prediction latency — caching strategies, sampling, and tree SHAP optimizations.
Monitoring
Detecting Bias Drift in Production AI Models
Why fairness metrics degrade over time, how to monitor them continuously, and what triggers should prompt immediate model review.
Standards
ISO 42001 Explained: Building an AI Management System That Scales
ISO 42001 is the world's first AI management system standard. Here is what it requires and how to implement it without drowning in documentation.
Compliance
NIST AI RMF in Practice: The Govern-Map-Measure-Manage Cycle
How to operationalize the NIST AI Risk Management Framework across your organization — from policy to production monitoring.
Healthcare
Healthcare AI Compliance: Navigating FDA Guidance and the EU AI Act
Clinical decision support tools face dual regulatory pressure from FDA and the EU AI Act. Here is how to satisfy both with a unified compliance strategy.
Explainability
From SHAP Values to Plain Language: Making AI Decisions Human-Readable
Technical feature attributions are meaningless to loan applicants and patients. How to translate model outputs into language that informs and empowers.
Finance
AI in Credit Scoring: Compliance Obligations Under ECOA and the EU AI Act
Credit scoring algorithms face some of the strictest AI regulation globally. Here is how lenders can stay compliant without sacrificing model performance.
Governance
Building an AI Audit Trail That Survives Regulatory Scrutiny
What regulators actually look for in an AI audit trail — and how to build one that is both technically sound and legally defensible.
HR Tech
NYC Local Law 144 and AI Hiring Tools: What Every Employer Needs to Know
New York City's Local Law 144 requires bias audits for automated employment decision tools. What it covers, what it requires, and how to comply.
Engineering
LIME vs SHAP: Choosing the Right Explainability Method for Your Use Case
LIME and SHAP are both powerful explainability tools — but they work differently and have different strengths. Here is how to choose.