These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Scope
SUBMIT A METRIC
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Submit This page includes technical metrics and methodologies for measuring and evaluating AI trustworthiness and AI risks. These metrics are often represented through mathematical formulas that assess the technical requirements for achieving trustworthy AI in a particular context. They can help to ensure that a system is fair, accurate, explainable, transparent, robust, safe, or secure.
Data Shapley
In a cooperative game, there are n players D = {1,...,n} and a score function v : 2[n] → R assigns a reward to each of 2 n subsets of players: v(S) is the reward if the players in subset S ⊆ D cooperate. We view the supervised machine learning problem as a coo...
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Beta Shapley
Beta Shapley is a unified data valuation framework that naturally arises from Data Shapley by relaxing the efficiency axiom. The Beta(α, β)-Shapley value considers the pair of hyperparameters (α, β) which decides the weight distribution on [n]. Beta(1,1)-Shapl...
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