Mind the semantic gap: semantic efficiency in human computer interfaces

As we become increasingly dependent on technology in our daily lives, the usability of HCIs is a key driver of individual empowerment for us all. A primary focus of AI systems has been to make HCIs easier to use by identifying what users need and agentively taking over some of the cognitive work users would have otherwise performed, as such, they are becoming our delegates. To become effective and reliable delegates, AI agents need to understand all relevant situational semantic context surrounding a user's need and how the tools of the HCI can be leveraged. Current ML systems have fundamental semantic gaps in bespoke human context, real-time world knowledge, and how those relate to HCI tooling. These challenges are difficult to close due factors such as privacy, continual learning, access to real-time context, and how deeply integrated the semantics are with in-context learning. As such, we need to research and explore new ways to safely capture, compactly model, and incrementally evolve semantics in ways that can efficiently integrate into how AI systems act on our behalf. This article presents a thought experiment called the Game of Delegation as a lens to view the effectiveness of delegation and the semantic efficiency with which the delegation was achieved.