expertise needed
domain expertise
Requires deep understanding of the problem domain
57 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Demographic Parity Assessment | Metric | Architecture/model Agnostic Paradigm/supervised +1 | Any | Demographic Parity Assessment checks whether a model gives a favourable outcome, such as a loan approval or an interview... | ||
| Counterfactual Fairness Assessment | Testing | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | Counterfactual Fairness Assessment tests whether a model would have made the same predictions for a person had their... | ||
| Causal Discrimination Testing | Testing | Architecture/model Agnostic Paradigm/supervised +1 | Tabular Text | Causal discrimination testing checks whether a system gives different outputs to otherwise identical inputs because of a... | ||
| Sequential CART Synthesis | Algorithmic | Architecture/model Agnostic Requirements/black Box | Tabular | Sequential CART synthesis creates an artificial copy of a confidential microdata set (records about individual people or... | ||
| Safety Envelope Testing | Testing | Architecture/model Agnostic Requirements/black Box | Any | Safety envelope testing checks how an AI system performs at and beyond the edges of the conditions it was designed for,... | ||
| Algorithm Review Boards | Process | Architecture/model Agnostic Requirements/black Box | Any | An algorithm review board, also called an AI ethics board or AI review board, is a standing group from different... | ||
| Human-in-the-Loop Safeguards | Process | Architecture/model Agnostic Requirements/black Box | Any | Human-in-the-loop safeguards hold an AI system's output at a checkpoint until a trained person has reviewed it.... | ||
| Confidence Thresholding | Algorithmic | Architecture/model Agnostic Requirements/black Box +1 | Any | Confidence thresholding uses a model score to decide whether a prediction should proceed automatically or receive... | ||
| Datasheets for Datasets | Documentation | Architecture/model Agnostic Requirements/black Box | Any | A datasheet for a dataset is a short document that accompanies the dataset and answers a set of questions about it, in... | ||
| Monotonicity Constraints | Algorithmic | Architecture/neural Networks Architecture/probabilistic/gaussian Processes +3 | Tabular | Monotonicity constraints require a model's prediction to move only one way as a chosen input rises, with the other... | ||
| Prompt Robustness Testing | Testing | Architecture/neural Networks/transformer/llm Paradigm/generative +1 | Text | Prompt robustness testing measures how much a language model's answers, or its performance on a task, change when the... | ||
| Concept Activation Vectors | Algorithmic | Architecture/neural Networks Requirements/gradient Access +2 | Any | A concept activation vector (CAV) is a direction inside a neural network that stands for a human idea such as 'stripes'.... | ||
| Reweighing | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Reweighing is a fairness method, presented by Kamiran and Calders (2012), that adjusts the training data before any... | ||
| Disparate Impact Remover | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | Disparate Impact Remover is a pre-processing method that edits the numerical features of a dataset so that they reveal... | ||
| Relabelling | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Relabelling, also called data massaging, is a pre-processing method proposed by Kamiran and Calders. It changes some of... | ||
| Preferential Sampling | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Preferential Sampling is a pre-processing method, proposed by Kamiran and Calders, that rebalances a training set so... | ||
| Attribute Removal (Fairness Through Unawareness) | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | Attribute removal, also called fairness through unawareness, trains and runs a model without the protected attributes,... | ||
| Equal Opportunity Difference | Metric | Architecture/model Agnostic Paradigm/supervised +1 | Any | Equal Opportunity Difference checks whether a model is equally likely to give a favourable prediction to the people... | ||
| Average Odds Difference | Metric | Architecture/model Agnostic Paradigm/supervised +1 | Any | Average Odds Difference summarises how differently a model treats two demographic groups, using two error rates. The... | ||
| Path-Specific Counterfactual Fairness Assessment | Metric | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | Path-specific counterfactual fairness assessment tests whether a protected attribute, such as sex or race, influences a... |
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