AI 中文总结
本文提出LLM驱动的通用工具Apply-<x>Mag，支持多种包容性设计方法，实证显示在7个产品上精确度达90-99%，召回率82-89%，且环境成本低。
AI 中文摘要
在人机交互实践中进行包容性设计可能非常耗费人力，这一高昂成本成为一些公司和HCI从业者不愿或无法克服的障碍。然而，不进行包容性设计同样代价高昂，表现为用户体验障碍，这些障碍不成比例地影响了服务不足的用户群体。为解决这一问题，我们引入了Apply-<x>Mag，一个由LLM驱动的工具，旨在支持HCI从业者面向广泛用户群体进行产品的包容性设计。Apply-<x>Mag具有通用性，支持任何可以表达为<x>Mags（即使用属性范围和启发式规则）的包容性设计方法。它同样有效：研究人员和从业者团队在7个产品上使用两种<x>Mags的不同组合进行的实证结果显示，Apply-<x>Mag的精确度平均为90-99%，召回率平均为82-89%。此外，其环境成本合理，消耗的资源与2-4次普通谷歌搜索相当。
英文摘要
Doing inclusive design in HCI practice can be labor-intensive, a costly barrier that some companies and HCI practitioners may be unwilling or unable to overcome. Yet, not doing inclusive design is costly too, in the form of UX barriers that disproportionately disadvantage under-served user populations. To address this problem, we introduce Apply-<x>Mag, an LLM-powered tool to support HCI practitioners' work to design their products inclusively to wide ranges of users. Apply-<x>Mag is general, supporting any inclusive design method that can be expressed as <x>Mags (i.e., using attribute ranges and heuristics). It is also effective: Empirical results with researcher and practitioner teams using various combinations of two <x>Mags on 7 products showed Apply-<x>Mag precision averaging 90-99% and recall averaging 82-89%. Further, its environmental costs were reasonable, costing about the same resources as 2-4 ordinary Google searches.
