In AI research and practice, rigor remains largely understood in terms of methodological rigor -- such as whether mathematical, statistical, or computational methods are correctly applied. We argue that this narrow conception of rigor has contributed to the concerns raised by the responsible AI community, including overblown claims about the capabilities of AI systems. Our position is that a broader conception of what rigorous AI research and practice should entail is needed. We believe such a conception -- in addition to a more expansive understanding of (1) methodological rigor -- should include aspects related to (2) what background knowledge informs what to work on (epistemic rigor); (3) how disciplinary, community, or personal norms, standards, or beliefs influence the work (normative rigor); (4) how clearly articulated the theoretical constructs under use are (conceptual rigor); (5) what is reported and how (reporting rigor); and (6) how well-supported the inferences from existing evidence are (interpretative rigor). In doing so, we also provide useful language and a framework for much-needed dialogue about the AI community's work by researchers, policymakers, journalists, and other stakeholders.

翻译：在人工智能研究与实践领域，严谨性在很大程度上仍被理解为方法论的严谨性——例如数学、统计或计算方法是否正确应用。我们认为，这种狭隘的严谨性概念加剧了负责任人工智能社区所提出的担忧，包括对人工智能系统能力的夸大宣称。我们的立场是，需要一种更广泛的关于严谨人工智能研究与实践的构想。我们相信，这种构想——除了对（1）方法论严谨性更广泛的理解外——还应包含以下方面：（2）背景知识如何指导研究方向（认知严谨性）；（3）学科、社区或个人规范、标准或信念如何影响工作（规范严谨性）；（4）所用理论构念的清晰表述程度（概念严谨性）；（5）报告内容与方式（报告严谨性）；（6）从现有证据得出推论的支撑力度（解释严谨性）。通过这一框架，我们为研究人员、政策制定者、记者及其他利益相关者就人工智能社区工作展开亟需的对话提供了实用术语与理论架构。