Summary: The primary goal of this paper is to define and study the interactive information complexity of functions. Let \(f(x,y)\) be a function, and suppose Alice is given \(x\) and Bob is given \(y\). Informally, the interactive information complexity \(\mathsf{IC}(f)\) of \(f\) is the least amount of information Alice and Bob need to reveal to each other to compute \(f\). Previously, information complexity has been defined with respect to a prior distribution on the input pairs \((x,y)\). Our first goal is to give a definition that is independent of the prior distribution. We show that several possible definitions are essentially equivalent. We establish some basic properties of the interactive information complexity \(\mathsf{IC}(f)\). In particular, we show that \(\mathsf{IC}(f)\) is equal to the amortized (randomized) communication complexity of \(f\). We also show a direct sum theorem for \(\mathsf{IC}(f)\) and give the first general connection between information complexity and (nonamortized) communication complexity. This connection implies that a nontrivial exchange of information is required when solving problems that have nontrivial communication complexity. We explore the information complexity of two specific problems: Equality and Disjointness. We show that only a constant amount of information needs to be exchanged when solving equality with no errors, while solving disjointness with a constant error probability requires the parties to reveal a linear amount of information to each other.