Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes
Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes
复制标题
DOI：
10.1145/3544548.3581354
复制
发表时间：
2023-03
期刊：
影响因子：
--
通讯作者：
Angie Zhang;Alexander Boltz;Jonathan Lynn;Chun-Wei Wang;Min Kyung Lee
中科院分区：
文献类型：
--
作者：
Angie Zhang;Alexander Boltz;Jonathan Lynn;Chun-Wei Wang;Min Kyung Lee
人工智能技术不断从数字助理发展到辅助决策。然而，鉴于其未知的结果和用途，设计人工智能仍然是一个挑战。扩展人工智能设计的一种方法是将利益相关者集中在设计过程中。我们与零工工人举行联合设计会议，根据他们的驾驶模式、决策和个人背景，探索以零工工人为中心的工具的设计。使用工人自己的数据以及城市层面的数据，我们创建了探针（交互式数据视觉效果），参与者可以探索这些探针，以揭示影响其工作策略的福祉和地位。我们描述了数据调查中出现的参与者见解和相应的人工智能设计考虑因素：1）工人的福祉权衡和职位限制，2）影响数据调查中的福祉的因素，以及3）不公平的算法管理实例。我们讨论了设计数据探针并使用它们来提升以工人为中心的人工智能设计以及工人倡导的影响。
AI technologies continue to advance from digital assistants to assisted decision-making. However, designing AI remains a challenge given its unknown outcomes and uses. One way to expand AI design is by centering stakeholders in the design process. We conduct co-design sessions with gig workers to explore the design of gig worker-centered tools as informed by their driving patterns, decisions, and personal contexts. Using workers’ own data as well as city-level data, we create probes—interactive data visuals—that participants explore to surface the well-being and positionalities that shape their work strategies. We describe participant insights and corresponding AI design considerations surfaced from data probes about: 1) workers’ well-being trade-offs and positionality constraints, 2) factors that impact well-being beyond those in the data probes, and 3) instances of unfair algorithmic management. We discuss the implications for designing data probes and using them to elevate worker-centered AI design as well as for worker advocacy.