🤖 AI Model Card Generator

What it means: The fundamental neural network design (e.g., "Transformer", "Convolutional Neural Network", "BERT-base architecture")
Why it's needed: Helps technical users understand computational requirements, expected behaviors, and known limitations of this architecture type. Required for technical transparency.
Example: "Transformer-based encoder-decoder with 12 layers and 768 hidden dimensions"
Who fills this: ML Engineer or Research Scientist

What it means: The category of machine learning task this model performs
Why it's needed: Defines the problem space and expected input/output format. Essential for users to determine if the model fits their use case.
Who fills this: ML Engineer

What it means: The data types the model accepts as input and produces as output
Why it's needed: Clarifies what data formats the model can process. Critical for integration planning.
Examples: "text-to-text" (translation), "image-to-label" (classification), "multimodal: text+image-to-text" (VQA)
Who fills this: ML Engineer

What it means: Natural languages the model was trained on and can process
Why it's needed: Defines language scope and helps identify potential biases. Performance typically varies significantly across languages. Required for NLP models.
Note: Models may perform differently on different languages even when "multilingual"
Who fills this: ML Engineer or Data Scientist

What it means: The pre-trained model you started with before fine-tuning (if applicable)
Why it's needed: Provides transparency about model provenance. Users inherit both capabilities AND biases from the base model. Required for transfer learning transparency.
Example: "google/bert-base-uncased fine-tuned on domain-specific corpus"
Who fills this: ML Engineer

What it means: Total count of trainable weights in the model (e.g., "110 million", "7 billion")
Why it's needed: Indicates model complexity, memory requirements, and inference speed. Helps users assess hardware requirements.
Impact: Larger models generally perform better but require more resources and energy
Who fills this: ML Engineer

What it means: The machine learning paradigm used to train this model
Why it's needed: Indicates what kind of data was needed and how the model learned. Affects expected behaviors and limitations.
Who fills this: ML Engineer or Research Scientist

What it means: How users should use this model directly, without modification
Why it's needed: Sets expectations for appropriate use cases. Required by EU AI Act for high-risk systems to define intended purpose.
Example: "This model is intended for sentiment analysis of English product reviews in e-commerce applications. It classifies reviews as positive, negative, or neutral."
Best practice: Be specific about domains, contexts, and user types
Who fills this: Product Manager + ML Engineer

What it means: How developers can build upon or adapt this model for other applications
Why it's needed: Guides responsible extension of your model. Helps developers understand what adaptations are reasonable vs. risky.
Example: "Can be fine-tuned for related classification tasks in other languages. Suitable as a feature extractor for downstream NLP tasks."
Who fills this: ML Engineer or Research Scientist

What it means: Applications where this model should NOT be used, either because it will perform poorly or cause harm
Why it's needed: CRITICAL FOR SAFETY. Prevents dangerous misuse. Required by responsible AI guidelines and emerging regulations. Establishes legal boundaries.
Example: "NOT for: medical diagnosis, high-stakes financial decisions, criminal justice risk assessment, analyzing languages other than English, or any application where errors could cause harm."
Best practice: Include both technical limitations (poor performance) and ethical boundaries (potential harms)
Who fills this: Sociotechnical Expert + ML Engineer + Ethics Team

What it means: Who should be using this model (developers, businesses, researchers, end-users)
Why it's needed: Clarifies expected technical expertise and appropriate contexts. Helps prevent use by unqualified users.
Example: "Intended for ML engineers with experience in NLP deployment. Requires understanding of model limitations and appropriate monitoring."
Who fills this: Product Manager

What it means: Specific, concrete examples of appropriate applications
Why it's needed: Provides clear guidance through examples. Helps users understand practical applications and boundaries.
Example: "✓ E-commerce review analysis for product insights
✓ Social media sentiment monitoring
✓ Customer feedback categorization"
Who fills this: Product Manager + Domain Expert

What it means: Documented performance differences across demographic groups or data types
Why it's needed: REQUIRED FOR COMPLIANCE. EU AI Act Article 10 requires bias assessment for high-risk systems. NIST AI RMF requires bias documentation. Essential for fairness and preventing discrimination.
Example: "Lower accuracy on non-standard English dialects. May reflect gender biases present in training data. Performance degrades for users over 65."
Must include: Demographic performance differences, data representation issues, known stereotypes
Who fills this: Sociotechnical Expert + Data Scientist + Ethics Team

What it means: Ways this model could cause harm if misused, deployed incorrectly, or even used as intended
Why it's needed: CRITICAL FOR SAFETY. Required by EU AI Act for risk assessment. Enables informed decision-making and risk mitigation.
Consider: Privacy violations, discriminatory outcomes, manipulation, surveillance risks, environmental harm, economic displacement
Example: "Risk of amplifying existing biases in hiring. Could enable privacy-invasive surveillance if misused. False positives could harm individuals."
Who fills this: Ethics Team + Sociotechnical Expert + Risk Manager

What it means: Technical constraints, failure modes, and conditions where the model doesn't work well
Why it's needed: Sets realistic expectations. Helps users avoid deployment in inappropriate conditions. Required for honest transparency.
Include: Data distribution shifts, adversarial vulnerability, computational requirements, latency constraints, edge cases
Example: "Fails on out-of-distribution data. Vulnerable to adversarial attacks. Requires GPU for real-time inference. Poor performance on texts shorter than 10 words."
Who fills this: ML Engineer + QA Team

What it means: Actions you've taken or users should take to reduce identified risks and biases
Why it's needed: Shows responsible development practices. Guides users in safe deployment. Required by EU AI Act for high-risk systems.
Include: Bias mitigation techniques applied, monitoring recommendations, human oversight requirements, fallback procedures
Example: "Implement human review for high-stakes decisions. Monitor performance across demographic groups. Set confidence thresholds appropriate for your use case. Regular retraining recommended every 6 months."
Who fills this: ML Engineer + Risk Manager + Ethics Team

What it means: Broader ethical implications of deploying and using this model
Why it's needed: Demonstrates consideration of societal impact. Required for responsible AI certification and ethical AI frameworks.
Consider: Privacy implications, fairness concerns, transparency issues, accountability questions, societal impacts
Example: "Deployment in hiring contexts raises fairness concerns. Privacy implications for text analysis. Consider impact on affected communities."
Who fills this: Ethics Team + Sociotechnical Expert

What it means: Description of datasets used to train this model, including sources, size, and characteristics
Why it's needed: REQUIRED FOR COMPLIANCE. EU AI Act Article 10 requires training data documentation. NIST AI RMF requires data transparency. Essential for understanding model behavior and biases.
Must include: Data sources, dataset size, data collection period, demographic representation, any known issues
Example: "100M English text samples from public web (2020-2023). Includes books, articles, social media. Demographics: limited representation of non-English speakers and younger populations."
Who fills this: Data Engineer + ML Engineer

What it means: Steps taken to clean, transform, and prepare data before training
Why it's needed: Preprocessing can significantly affect model behavior. Required for reproducibility and understanding model decisions.
Include: Cleaning steps, normalization, filtering, augmentation, deduplication
Example: "Removed HTML tags, normalized Unicode, filtered offensive content using blocklist, deduplicated similar texts, tokenized using SentencePiece."
Who fills this: Data Engineer + ML Engineer

What it means: Technical details about how training was conducted
Why it's needed: Enables reproducibility and helps users understand model characteristics. Required for scientific transparency.
Include: Optimizer, learning rate, batch size, epochs, regularization, early stopping criteria
Example: "AdamW optimizer, learning rate 1e-4, batch size 32, trained for 10 epochs with early stopping on validation loss."
Who fills this: ML Engineer

What it means: How long training took and on what hardware
Why it's needed: Helps users estimate resources needed for retraining or fine-tuning. Informs environmental impact assessment.
Example: "72 hours on 8x NVIDIA A100 GPUs"
Who fills this: ML Engineer

What it means: Estimated carbon emissions from training this model
Why it's needed: Increasingly required for environmental responsibility reporting. EU Green Deal considerations. Part of comprehensive impact assessment.
Tool: Use ML CO2 Impact calculator (https://mlco2.github.io/impact/)
Example: "Estimated 150 kg CO2eq emissions during training (calculated using CodeCarbon)"
Who fills this: ML Engineer + Sustainability Team

What it means: Hardware used for training
Why it's needed: Helps users understand computational requirements and assess feasibility of retraining
Example: "8x NVIDIA A100 (40GB), 512GB RAM, AWS p4d instances"
Who fills this: ML Engineer + DevOps

What it means: Description of data used to evaluate model performance (separate from training data)
Why it's needed: Test data quality determines validity of performance metrics. Required for honest evaluation. Must represent real-world distribution.
Must include: Data source, size, how it differs from training data, any limitations
Example: "10K held-out samples from same distribution as training data. Includes edge cases and adversarial examples. Limited representation of rare classes."
Who fills this: ML Engineer + QA Team

What it means: Quantitative measures used to assess model performance
Why it's needed: Provides objective assessment of model quality. Required for comparison and decision-making. Different metrics matter for different applications.
Common metrics: Accuracy, Precision, Recall, F1, AUC-ROC, Mean Squared Error, BLEU score
Best practice: Include multiple metrics; single metrics can be misleading
Who fills this: ML Engineer + Data Scientist

What it means: Actual performance numbers on your evaluation metrics
Why it's needed: Provides concrete evidence of model quality. Required for informed decision-making about deployment. Enables comparison with other models.
Must include: Overall performance, confidence intervals if available, comparison to baselines
Example: "Accuracy: 92.3% (±0.5%), F1: 0.89, AUC-ROC: 0.95. Outperforms baseline by 5.2 percentage points."
Who fills this: ML Engineer + Data Scientist

What it means: Performance breakdown across different demographic groups or sensitive attributes
Why it's needed: REQUIRED FOR COMPLIANCE. EU AI Act requires fairness testing for high-risk systems. Essential for detecting discriminatory behavior. Required by responsible AI frameworks.
Must include: Performance by gender, race, age, etc. (when applicable and available)
Example: "Performance by gender: Male: 93.1%, Female: 91.8%. Performance by age: <30: 94%, 30-60: 92%, >60: 88%. Disparate impact ratio: 0.98 (meets 0.80 threshold)."
Who fills this: Data Scientist + Sociotechnical Expert + Ethics Team

What it means: Analysis of when and why the model makes mistakes
Why it's needed: Identifies systemic issues and informs deployment decisions. Helps users understand edge cases and failure modes.
Include: Common error patterns, worst-performing subgroups, failure cases
Example: "Higher error rate on sarcastic text. Confuses similar-looking categories. Fails on very short inputs (<5 words)."
Who fills this: ML Engineer + QA Team

What it means: Statistical uncertainty in your performance measurements
Why it's needed: Provides honest assessment of metric reliability. Small test sets can give misleading performance estimates.
Example: "95% CI: [91.8%, 92.8%]. Based on 10K test samples with 5-fold cross-validation."
Who fills this: Data Scientist

What it means: Minimum and recommended hardware specifications for inference
Why it's needed: Helps users determine if they can run the model. Critical for deployment planning and cost estimation.
Include: GPU requirements, RAM, storage, CPU specs
Example: "Minimum: 16GB GPU VRAM, 32GB RAM. Recommended: NVIDIA A100 or equivalent, 64GB RAM, 50GB storage."
Who fills this: ML Engineer + DevOps

What it means: Required software dependencies, libraries, and frameworks
Why it's needed: Ensures users can set up the correct environment. Prevents version conflicts and compatibility issues.
Include: Python version, framework versions (PyTorch, TensorFlow), key library versions
Example: "Python 3.9+, PyTorch 2.0+, transformers 4.30+, CUDA 11.8+"
Who fills this: ML Engineer

What it means: Expected format and structure of input data
Why it's needed: Enables correct model usage. Prevents integration errors.
Example: "Text string, max 512 tokens. Images: 224x224 pixels, RGB, normalized to [0, 1]."
Who fills this: ML Engineer

What it means: Structure and format of model outputs
Why it's needed: Helps users parse and use model predictions correctly
Example: "JSON: {'label': str, 'confidence': float, 'top_5': list}. Confidence scores sum to 1.0."
Who fills this: ML Engineer

What it means: How long the model takes to make a prediction
Why it's needed: Critical for real-time applications. Helps assess deployment feasibility for different use cases.
Example: "15ms per sample on NVIDIA A100, 120ms on CPU (Intel Xeon)"
Who fills this: ML Engineer + Performance Team

What it means: Storage space required for model files
Why it's needed: Affects deployment options, especially for edge devices. Impacts download time and storage costs.
Example: "1.2GB (FP32), 600MB (FP16), 300MB (INT8 quantized)"
Who fills this: ML Engineer

What it means: Link to detailed API documentation for using the model
Why it's needed: Provides implementation guidance. Essential for developer adoption.
Who fills this: Technical Writer + ML Engineer

What it means: Simple code snippet showing how to load and use the model
Why it's needed: Accelerates developer adoption. Reduces integration errors. Shows expected usage pattern.
Best practice: Include imports, initialization, and basic inference example
Who fills this: ML Engineer