expertise needed
domain expertise
Requires deep understanding of the problem domain
32 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Demographic Parity Assessment | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Demographic Parity Assessment evaluates whether a model produces equal positive prediction rates across different... | ||
| Sensitivity Analysis for Fairness | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Sensitivity Analysis for Fairness systematically evaluates how model predictions change when sensitive attributes or... | ||
| Simulation-Based Synthetic Data Generation | Algorithmic | Requirements/model Agnostic Paradigm/generative +1 | Tabular Time-series | Generates synthetic datasets through computational simulation of underlying data-generating processes, encompassing... | ||
| Safety Envelope Testing | Testing | Architecture/model Agnostic Requirements/black Box | Any | Safety envelope testing systematically evaluates AI system performance at the boundaries of its intended operational... | ||
| Internal Review Boards | Process | Architecture/model Agnostic Requirements/black Box | Any | Internal Review Boards (IRBs) provide independent, systematic evaluation of AI/ML projects throughout their lifecycle to... | ||
| Human-in-the-Loop Safeguards | Process | Architecture/model Agnostic Requirements/black Box | Any | Human-in-the-loop safeguards establish systematic checkpoints where human experts review, validate, or override AI/ML... | ||
| Datasheets for Datasets | Documentation | Architecture/model Agnostic Requirements/black Box | Any | Datasheets for datasets establish comprehensive documentation standards for datasets, systematically recording creation... | ||
| Monotonicity Constraints | Algorithmic | Architecture/neural Networks Architecture/probabilistic/gaussian Processes +3 | Tabular | Monotonicity constraints enforce consistent directional relationships between input features and model predictions,... | ||
| Concept Activation Vectors | Algorithmic | Architecture/neural Networks Requirements/gradient Access +2 | Any | Concept Activation Vectors (CAVs), also known as Testing with Concept Activation Vectors (TCAV), identify mathematical... | ||
| Disparate Impact Remover | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Tabular | Disparate Impact Remover is a preprocessing technique that transforms feature values in a dataset to reduce statistical... | ||
| 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... | ||
| Fair Transfer Learning | Algorithmic | Architecture/neural Networks Paradigm/parametric +4 | Any | An in-processing fairness technique that adapts pre-trained models from one domain to another whilst explicitly... | ||
| Adversarial Training Evaluation | Testing | Architecture/model Agnostic Architecture/neural Networks +1 | Any | Adversarial training evaluation assesses whether models trained with adversarial examples have genuinely improved... | ||
| Agent Goal Misalignment Testing | Testing | Architecture/model Agnostic Architecture/neural Networks/transformer +3 | Any | Agent goal misalignment testing identifies scenarios where AI agents pursue objectives in unintended ways or develop... | ||
| API Usage Pattern Monitoring | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | API usage pattern monitoring analyses AI model API usage to detect anomalies and generate evidence of secure operation.... | ||
| Chain-of-Thought Faithfulness Evaluation | Algorithmic | Architecture/neural Networks/transformer/llm Paradigm/generative +1 | Text | Chain-of-thought faithfulness evaluation assesses the quality and faithfulness of step-by-step reasoning produced by... | ||
| 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... | ||
| Few-Shot Fairness Evaluation | Testing | Architecture/neural Networks/transformer/llm Paradigm/generative +1 | Text | Few-shot fairness evaluation assesses whether in-context learning with few-shot examples introduces or amplifies biases... |
Rows per page
Page 1 of 2