Conceptual Engineering

Conceptual engineering aims to improve the clarity and utility of concepts, often by refining their definitions or representations. Current research focuses on leveraging large language models (LLMs) and machine learning techniques, such as variational autoencoders and sparse coding, to analyze and manipulate conceptual spaces, addressing challenges like bias mitigation in LLMs and improving the efficiency of design processes. This work has implications for various fields, enhancing both the precision of scientific discourse and the effectiveness of engineering design through data-driven approaches to concept refinement and exploration.