Wikipedia ORES Explorer: Visualizing Trade-offs For Designing Applications With Machine Learning API

Wikipedia ORES Explorer: Visualizing Trade-offs For Designing Applications With Machine Learning API
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维基百科 ORES Explorer:使用机器学习 API 可视化设计应用程序的权衡

DOI:
10.1145/3461778.3462099
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发表时间:
2021
期刊:
DIS '21: Designing Interactive Systems Conference 2021
影响因子:
--
通讯作者:
Zhu, Haiyi
Zhu, Haiyi
中科院分区:
--
文献类型:
--
作者:
Ye, Zining;Yuan, Xinran;Gaur, Shaurya;Halfaker, Aaron;Forlizzi, Jodi;Zhu, Haiyi

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随着人工智能(AI)系统的行业应用越来越多,预先训练的模型和API已经出现,并大大降低了构建AI驱动产品的门槛。然而,新手AI应用程序设计人员在做出明智的设计决策之前,往往很难认识到固有的算法权衡并评估模型的公平性。在这项研究中,我们研究了客观修订评估系统(ORES),这是维基百科中的一个机器学习(ML)API,社区使用它来构建反破坏工具。我们设计了一个交互式的可视化系统,沟通模型阈值的权衡和公平的ORES。我们通过与潜在的ORES应用程序设计人员进行10次深入访谈来评估我们的系统。我们发现,我们的系统可以帮助ML背景有限的应用程序设计人员了解上下文ML知识,认识到固有的价值权衡,并做出与其目标一致的设计决策。通过在现实世界的领域中展示我们的系统,本文提出了一种新的可视化方法,以促进更大的可访问性和人工智能应用程序设计中的代理。
With the growing industry applications of Artificial Intelligence (AI) systems, pre-trained models and APIs have emerged and greatly lowered the barrier of building AI-powered products. However, novice AI application designers often struggle to recognize the inherent algorithmic trade-offs and evaluate model fairness before making informed design decisions. In this study, we examined the Objective Revision Evaluation System (ORES), a machine learning (ML) API in Wikipedia used by the community to build anti-vandalism tools. We designed an interactive visualization system to communicate model threshold trade-offs and fairness in ORES. We evaluated our system by conducting 10 in-depth interviews with potential ORES application designers. We found that our system helped application designers who have limited ML backgrounds learn about in-context ML knowledge, recognize inherent value trade-offs, and make design decisions that aligned with their goals. By demonstrating our system in a real-world domain, this paper presents a novel visualization approach to facilitate greater accessibility and human agency in AI application design.
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