Designing Al Systems That Make Organizational Knowledge Actionable

摘要

Knowledge management describes processes involved in creating, sharing, using, and maintaining an organization's knowledge. In computer science, knowledge management is traditionally associated with artificial intelligence (AI), as researchers developed algorithms to manage large datasets [1]. Although not a new AI technique, machine learning (ML) offers interesting opportunities for designing knowledge-management systems because it allows people to create systems based on sample data rather than explicit descriptions of computational procedures in code. This makes it possible to design interfaces that depend on difficult-to-code and dynamic user behavior, such as the utilization of knowledge. It also enables end users to contribute to ML models by providing sample data. In this article, we begin by presenting one view on organizational knowledge and how it relates to meaning, context, and action. We then move on to present three tensions that need to be addressed when designing AI systems to make organizational knowledge actionable.