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.
翻译:AI技术继续从数字助理推动到协助决策。然而,设计AI仍然是一个挑战,因为其结果和用途未知。扩大AI设计的方法之一是在设计过程中将利益攸关方集中起来。我们与工作表现工人共同设计会议,探索以其驾驶模式、决定和个人背景为根据的以工作表现为核心的工具的设计。我们利用工人自己的数据以及城市一级的数据,创建探索器 -- -- 交互式数据视觉 -- -- 参与者探索如何展示影响其工作战略的福利和定位。我们描述了参与者的洞察力和相应的AI设计考虑。我们从数据调查中了解到:(1) 工人的福祉交易和定位限制;(2) 影响数据探测之外的福利的因素;(3) 不公平的算法管理案例。我们讨论了设计数据探测器和使用数据提升以工人为中心的AI设计以及工人宣传的影响。</s>