data requirements
access to training data
Requires the original training dataset
17 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Influence Functions | Algorithmic | Architecture/linear Models Architecture/neural Networks +6 | Any | Influence functions quantify how much each training example influenced a model's predictions by computing the change in... | ||
| Bootstrapping | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Bootstrapping estimates uncertainty by repeatedly resampling the original dataset with replacement to create many new... | ||
| Jackknife Resampling | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Jackknife resampling (also called leave-one-out resampling) assesses model stability and uncertainty by systematically... | ||
| Model Cards | Documentation | Architecture/model Agnostic Requirements/black Box | Any | Model cards are standardised documentation frameworks that systematically document machine learning models through... | ||
| Model Distillation | Algorithmic | Architecture/neural Networks Paradigm/parametric +3 | Any | Model distillation transfers knowledge from a large, complex model (teacher) to a smaller, more efficient model... | ||
| Reweighing | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Reweighing is a pre-processing technique that mitigates bias by assigning different weights to training examples based... | ||
| Relabelling | Procedural | Architecture/model Agnostic Paradigm/supervised +2 | Any | A preprocessing fairness technique that modifies class labels in training data to achieve equal positive outcome rates... | ||
| Constitutional AI Evaluation | Testing | Architecture/neural Networks/transformer/llm Requirements/white Box +1 | Text | Constitutional AI evaluation assesses models trained to follow explicit behavioural principles or 'constitutions' that... | ||
| Continual Learning Stability Testing | Testing | Architecture/model Agnostic Architecture/neural Networks +1 | Any | Continual learning stability testing evaluates whether models that learn from streaming data maintain performance on... | ||
| Data Poisoning Detection | Algorithmic | Architecture/model Agnostic Requirements/white Box +1 | Any | Data poisoning detection identifies malicious training data designed to compromise model behaviour. This technique... | ||
| Embedding Bias Analysis | Algorithmic | Architecture/neural Networks Architecture/neural Networks/transformer +3 | Text Image | Embedding bias analysis examines learned representations to identify biases, spurious correlations, and problematic... | ||
| Hallucination Detection | Testing | Architecture/neural Networks/transformer Architecture/neural Networks/transformer/llm +2 | Text | Hallucination detection identifies when generative models produce factually incorrect, fabricated, or ungrounded... | ||
| Machine Unlearning | Algorithmic | Architecture/model Agnostic Architecture/neural Networks +2 | Any | Machine unlearning enables removal of specific training data's influence from trained models without complete... | ||
| Model Extraction Defence Testing | Testing | Architecture/model Agnostic Requirements/black Box | Any | Model extraction defence testing evaluates protections against attackers who attempt to steal model functionality by... | ||
| Membership Inference Attack Testing | Testing | Architecture/model Agnostic Requirements/black Box | Any | Membership inference attack testing evaluates whether adversaries can determine if specific data points were included in... | ||
| Out-of-Domain Detection | Algorithmic | Architecture/model Agnostic Architecture/neural Networks/transformer/llm +2 | Text | Out-of-domain (OOD) detection identifies user inputs that fall outside an AI system's intended domain or capabilities,... | ||
| Synthetic Data Evaluation | Algorithmic | Architecture/model Agnostic Requirements/black Box | Tabular Any | Synthetic data evaluation assesses whether synthetic datasets protect individual privacy while maintaining statistical... |
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