Composable Building Blocks for Controllable and Transparent Interactive AI Systems

TL;DR

This paper introduces a modular approach to designing interactive AI systems using structural building blocks and visual explanations, enhancing transparency and interpretability for humans and automated agents.

Contribution

It proposes a novel architecture that represents interactive AI systems as sequences of explainable building blocks, improving system transparency and communication.

Findings

Developed a flow-based architecture for interactive AI systems.

Created a prototype demonstrating the approach.

Enhanced interpretability for both humans and AI agents.

Abstract

While the increased integration of AI technologies into interactive systems enables them to solve an equally increasing number of tasks, the black box problem of AI models continues to spread throughout the interactive system as a whole. Explainable AI (XAI) techniques can make AI models more accessible by employing post-hoc methods or transitioning to inherently interpretable models. While this makes individual AI models clearer, the overarching system architecture remains opaque. To this end, we propose an approach to represent interactive systems as sequences of structural building blocks, such as AI models and control mechanisms grounded in the literature. These can then be explained through accompanying visual building blocks, such as XAI techniques. The flow and APIs of the structural building blocks form an explicit overview of the system. This serves as a communication basis for both humans and automated agents like LLMs, aligning human and machine interpretability of AI models. We discuss a selection of building blocks and concretize our flow-based approach in an architecture and accompanying prototype interactive system.