  In their book Uncertainty and Quality of Science for Policy (1990), Funtowicz
and Ravetz argued the need to extend traditional methods and techniques of
quality assurance in policy-related science. Since then, these ideas have been
operationalized further and applied. Particularly relevant have been the
recourse to extended peer review, to be intended as internal, across
disciplines, as well as external, between practitioners and stakeholders, and
the use of a new approach to qualify quantities: NUSAP (Numeral, Unit, Spread,
Assessment, Pedigree). Here we describe how sensitivity analysis, mandated by
existing guidelines as a good practice to use in conjunction to mathematical
modelling, needs to be transformed and adapted to ensure quality in the
treatment of uncertainty of science for policy. We thus provide seven rules to
extend the use of sensitivity analysis (or how to apportion uncertainty in
model based inference among input factors) in a process of sensitivity auditing
of models used in a policy context. Each rule will be illustrated by examples.
