Leveraging LLMs for Reflection ��: Approaches to Mitigate Assumptions within the Design Process
Niklas Muhs, Aeneas Stankowski · Proceedings of DRS · 2024
In an increasingly complex landscape, designers grapple with unprecedented uncertainty, often exacerbated by inherent biases and implicit assumptions. Utilizing Large Language Models (LLMs), our formative study introduces "Anticipate," a tool designed to interrogate these hidden presumptions and mitigate uncertainty. A subsequent study demonstrates that LLMs can critically challenge design ideas, elucidate underlying thought patterns, and expose biases, thereby preempting undesirable outcomes. Importantly, we employ specific input framing techniques to minimize the risk of LLM-induced biases and hallucinations in decision-making. Collectively, these methodologies aim to attenuate both designer and algorithmic biases, thereby mitigating the perpetuation of adverse societal trends.