NYC School of Data 2020

In this workshop we will use the metaphorical figure of the monster to help participants question potentially dangerous assumptions about the provenance and validity of data, and identify areas of potential future concern caused by uncertainty and ambiguity. This questioning helps demystify the under reported work that underlies data (such as data gathering and cleaning), and encourages participants to think and act critically with regard to technology. The session is informative, instructive, and interactive. It lasts 1 hour and is divided into three stages:

1) Why monsters? (15 minutes)
To start the workshop we reintroduce participants to the figure of the monster to show its original purpose was to act as a warning, particularly with regard to the unknown. We use familiar examples from popular culture, and historic examples from across the world, to show how the monster can be more complex and ambiguous than is typically understood today. We use the example of Dr. Frankenstein, and the creature he creates, to highlight the need for designers and developers of new technology to proceed intentionally and with care for their creations.
2) How to find monsters: (15 minutes)
In the second stage, participants are introduced to the 5WsH technique for creatively probing a topic, and to the specially designed hexagonal worksheets we use for identifying places where monsters might lurk. The 5WsH technique teaches participants to pose Who, What, Why, Where, When, and How questions about a topic. The hexagonal worksheet allows us to approach these questions in any order, without privileging any particular one. Where there is uncertainty, or the answer to a question is unknown, we mark a potential monster hiding place.
3) Practical monster hunt: (30 minutes)
In the final stage of the workshop we ask participants to hunt for monsters themselves. Using an example data set, for example NYC 311 open data, we ask participants to:
a. Come up with a set of 5WsH questions that will probe the provenance and validity of the data, e.g. by considering How, by Who, and for What purpose the data were gathered.
b. Pose the questions they come up with to the data set
c. Identify the warning signs of uncertainty and ambiguity at the places where assumptions are being made, and so from where monsters might emerge
d. Consider how the monster might be domesticated, problems mitigated, and uncertainty reduced.