Investigating and Designing for Trust in AI-powered Code Generation Tools
arxiv(2023)
摘要
As AI-powered code generation tools such as GitHub Copilot become popular, it
is crucial to understand software developers' trust in AI tools – a key factor
for tool adoption and responsible usage. However, we know little about how
developers build trust with AI, nor do we understand how to design the
interface of generative AI systems to facilitate their appropriate levels of
trust. In this paper, we describe findings from a two-stage qualitative
investigation. We first interviewed 17 developers to contextualize their
notions of trust and understand their challenges in building appropriate trust
in AI code generation tools. We surfaced three main challenges – including
building appropriate expectations, configuring AI tools, and validating AI
suggestions. To address these challenges, we conducted a design probe study in
the second stage to explore design concepts that support developers'
trust-building process by 1) communicating AI performance to help users set
proper expectations, 2) allowing users to configure AI by setting and adjusting
preferences, and 3) offering indicators of model mechanism to support
evaluation of AI suggestions. We gathered developers' feedback on how these
design concepts can help them build appropriate trust in AI-powered code
generation tools, as well as potential risks in design. These findings inform
our proposed design recommendations on how to design for trust in AI-powered
code generation tools.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要