The method of independent bounded differences (McDiarmid, 1989) gives largedeviation concentration bounds for multivariate functions in terms of the maximum effect that changing one coordinate of the input can have on the output. This method has been widely used in combinatorial applications, and in learning theory. In some recent applications to the theory of algorithmic stability (Kutin and Niyogi, 2002), we need to consider the case where changing one coordinate of the input usually leads to a small change in the output, but not always. We prove two extensions to McDiarmid’s inequality. The first applies when, for most inputs, any small change leads to a small change in the output. The second applies when, for a randomly selected input and a random one-coordinate change, the change in the output is usually small. 1