In this course, you will learn about the institutional structures — such as laws, regulations, policies, the workforce, and the relationship between the public and private sectors — through which, and because of which, data and artificial-intelligence systems operate. The course addresses how designers and planners can understand and incorporate into their work this “human side” of data and AI: the institutions that determine whether data is created, how data is used, who is involved in the data lifecycle, and, ultimately, how data and AI can best aid practitioners, and when it cannot. This “human side,” just as much as the technology itself, is an integral component of data and AI systems; “data-driven decisions” are not possible without “decision-driven data”: humans exercising agency to determine whether, when, or how to employ data.

The goal is to go beyond simple, uncritical acceptance of available data sources to interrogating such sources, giving practitioners the tools they need to function effectively not merely as consumers or users of existing data, but as creators of data and tools, and as shapers of the broader human systems that give rise to data.

By the end of this course, you will be able to:
• Evaluate the appropriateness, relevance, and trustworthiness of data sources
• Identify “human” constraints and problems with data at the organizational level, and propose plausible project-based and/or institutional strategies to address them
• Curate and assemble data sources to apply to planning and design problems
• Create “data lifecycle” maps, particularly demonstrating the linkage between “data-driven decisions” and “decision-driven data,” and differentiating between roles as data users/consumers, data producers, and institution-shapers