Model Card Hub

About Model Card Hub

Model Card Hub is a reference site for ML model documentation and evaluation methodology. It provides templates, guides, and references for practitioners building transparent and responsible AI systems.

Mission

Machine learning models are increasingly deployed in high-stakes domains including healthcare, criminal justice, lending, and hiring. Without standardized documentation, users cannot assess whether a model is appropriate for their use case, what biases it may carry, or how it was evaluated. Model Card Hub exists to make model documentation accessible and actionable.

What We Cover

  • Model Cards -- Origin, structure, and templates based on the Mitchell et al. (2019) framework for standardized model reporting.
  • Evaluation Methodology -- Data splitting strategies, cross-validation techniques, and metric selection guidance for classification, regression, and generation tasks.
  • Bias and Fairness Testing -- Frameworks and metrics for detecting and measuring algorithmic bias across demographic groups.
  • AI Red Teaming -- Adversarial testing techniques for language models including prompt injection, jailbreak categorization, and safety benchmarks.
  • Benchmarks -- Reference tables for major ML evaluation benchmarks including MMLU, HumanEval, GSM8K, and others.
  • Frameworks -- Comparison of ML experiment tracking and evaluation platforms.

Intended Audience

ML engineers, data scientists, AI product managers, compliance officers, and researchers who need to document, evaluate, or audit machine learning models. The content assumes familiarity with basic ML concepts.

Sources and References

Content is based on published research, established frameworks, and industry standards. Key sources include the original model cards paper (Mitchell et al., 2019), the AI Fairness 360 toolkit, NIST AI Risk Management Framework, and the OWASP LLM Top 10.