After the Interface: Relocating Human Agency in the Age of Conversational AI

TL;DR

This paper argues that human agency in AI interactions has not diminished but shifted from interfaces to the interaction process itself, emphasizing the importance of understanding agency's redistribution in conversational AI.

Contribution

It introduces a framework to understand human agency as relocated from interface control to goal articulation and evaluation in conversational AI systems.

Findings

Agency shifts from interface to interaction process in AI systems.

Control can be distinguished into process and outcome control.

Agency's redistribution is visible through control mapping.

Abstract

As AI systems take on greater autonomy, a quiet anxiety has settled over the HCI community: human agency is eroding. Users no longer control execution, interfaces recede, and machines decide. We argue that this anxiety, while understandable, reflects a framing problem rather than an empirical finding. Agency has not diminished but has relocated. As interaction has shifted from command- and feature-based paradigms toward conversational, generative, and agentic AI, human agency migrates from interface affordances to interaction itself: articulating goals, evaluating outputs, and negotiating outcomes. To make this relocation visible, we revisit control as a diagnostic lens, distinguish process control and outcome control, and map different systems across this space to show that what looks like agency's disappearance is actually its redistribution. We take seriously the objection that outcome-based agency may be illusory in systems that produce plausible but unverifiable outputs, and argue that this concern reveals what agency in human-AI interaction truly requires. This paper invites the CUI community to reconsider what agency means, where it lives, and what it demands, including who gets to have it and who holds responsibility when it fails, before the consequences become impossible to overlook.