Something Borrowed: Exploring the Influence of AI-Generated Explanation Text on the Composition of Human Explanations

Abstract

Recent advances in Human-AI interaction have highlighted the possibility of employing AI in collaborative decision-making contexts, particularly in cases where the decision is subjective, without one ground truth. In these contexts, researchers argue that AI could be used not just to provide a final decision recommendation, but to surface new perspectives, rationales, and insights. In this late-breaking work, we describe the initial findings from an empirical study investigating how complementary AI input influences humans' rationale in ambiguous decision-making. We use subtle sexism as an example of this context, and GPT-3 to create explanation-like text. We find that participants change the language, level of detail, and even the argumentative stance of their explanations after seeing the AI explanation text. They often borrow language directly from this complementary text. We discuss the implications for collaborative decision-making and the next steps in this research agenda.