Knowledge Affordances for Hybrid Human-AI Information Seeking

TL;DR

This paper introduces the concept of knowledge affordance (KA) to help hybrid human-AI systems identify meaningful information sources based on their capabilities and context, aiming for more transparent and adaptable information seeking.

Contribution

It proposes KAs as declarative, semantically grounded descriptions of knowledge sources, connecting affordances, semantic web, and knowledge engineering for improved system navigation.

Findings

Conceptual framework connecting affordances and knowledge sources.

Sketches of research directions for KA-aware systems.

Relational nature of KAs emerging from task and context.

Abstract

As information ecosystems grow more heterogeneous, both humans and artificial agents increasingly face a simple yet unresolved question: when seeking knowledge, whom should we ask, and why? Inspired by how people intuitively "read a room", this paper introduces the concept of knowledge affordance (KA) to systematize how agents identify meaningful opportunities for information seeking in hybrid human-AI environments. Rather than introducing a fully formed framework, we propose KAs as declarative, semantically grounded descriptions of what a knowledge source can offer, for which kinds of questions, and with which contextual properties. Additionally, we suggest that KAs are relational, possibly emerging from the interplay between the agent's task, preferences and situational factors. Our contribution is thus a conceptual proposal that connects different research streams, including affordances, semantic web services, knowledge engineering and querying, and mutual intelligibility. We sketch possible research directions to build KA-aware systems that navigate information spaces with greater transparency, adaptability and shared understanding.