Counterfactual Testing
An experimental method that infers causal relationships by systematically varying a single variable. In AI safety research, it is used to analyze the causes of misaligned model behavior, helping translate individual observations into generalizable knowledge.
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Generated legal reasons cannot serve as sufficient evidence for compliance auditing; verification mechanisms independent of the model's self-report are needed, such as counterfactual tests or internal representation probing.
Counterfactual testing is an experimental method that infers causal relationships by systematically changing a single variable, used in AI alignment research to explore whether different training data or reward signals would lead to the same misalignment path.
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Generated legal reasons cannot serve as sufficient evidence for compliance auditing; verification mechanisms independent of the model's self-report are needed, such as counterfactual tests or internal representation probing.
50%UnverifiedCounterfactual testing is an experimental method that infers causal relationships by systematically changing a single variable, used in AI alignment research to explore whether different training data or reward signals would lead to the same misalignment path.
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