Knowledge Elicitation: Data Collection and Knowledge Modeling for Human-Aware Systems

Abstract

Knowledge elicitation systems are important for utilizing expert domain knowledge in human-machine systems. These systems acknowledge the role that expert knowledge plays in structuring and utilizing information effectively. And yet, there remains the issue of how to “captur[e] the important information that sufficiently represents the real-world domain.” Central to this issue is the characterization of knowledge beyond the systems of data collection and knowledge modeling. We turn to feminist epistemology to approach the notion of knowledge from a critical perspective that challenges normative representational ways of knowing. Feminist theories support generative methods that influence the futuring of knowledge-driven AI systems. In this workshop, we put forward a speculative feminist knowledge elicitation system to challenge normative assumptions about knowledge. Through feminist epistemology, we consider knowledge from a feminist lens, adding a new, context-sensitive perspective to knowledge elicitation systems: a re-formulation of content, context, and ethics.

*Note. Both researchers contributed equally to this workshop.

Citation

@article | inproceedings | phdthesis | book{ 2023_wasp_knowledge,
  author = akbaba and Irene Kaklopoulou,
  title = "Knowledge Elicitation: Data Collection and Knowledge Modeling for Human-Aware Systems",
  year = 2023,
  booktitle = "AI for Humanity and Society",



}