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Facilitating Public Engagement for Data Science and AI

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Introduction

In the previous chapters we discussed the following topics:

Summary

  1. What are the goals and values of public engagement?
  2. What does it mean to conduct responsible public engagement for data science and AI?

With the conceptual foundations laid, we now turn towards the more practical side of public engagement.

This chapter begins by reviewing two frameworks that can help us locate activities and methods for public engagement within a broader project lifecycle for data science and AI. The objective is to use these models to support informed answers to the following questions:

  • When should you engage?
  • How should you engage?

Chapter Outline

Learning Objectives

By the end of this chapter you should have a strong comprehension of a) a stakeholder analysis process, b) a model of a typical data science or AI project lifecycle, and c) a variety of methods for public engagement. Collectively, these will enable you to scaffold your own project planning activities to help answer

  • How should you engage?
  • When should you engage?