Public Engagement with Data Science and AI

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The Alan Turing Institute is pleased to announce that registration is now open now open for the online training course, Public Engagement with Data Science and AI (25 - 29 April 2022). The course is organised and run by Dr Christopher Burr (Ethics Fellow, Public Policy Programme) as part of the Turing Commons series of online courses. 

The course will take place across 5 days, and each day will comprise a series of lectures, hands-on sessions, and group discussions. A guest lecture will also take place on one of the days (TBC). All times are in GMT. 

The course will be delivered online using Zoom, and to ensure effective group discussion will be limited to 30 participants. Application is, therefore, required (see further details below).

Following the live workshops, recordings of the taught components will be made available to all (along with relevant materials) for asynchronous self-study.

This course is designed to help you understand the practical and ethical value of public engagement with data science and AI. The course begins with an introduction to different forms of public engagement, while critically examining the different methods and approaches. Then, through a series of structured seminars and workshops, you will consider the impact of public engagement upon both practices of research and innovation as well as society more broadly.

Following this general introduction, the course pivots to introduce and discuss practical methods of public engagement, including deliberative activities that help build consensus among stakeholder; transparent and explainable methods of data governance to support project activities; methods of data visualisation to support the communication of science and technology; and an awareness of social and psychological biases, which can negatively affect the goals of responsible public engagement.

Introduction: an overview of the course and its objectives

The course begins with a welcome from the course leader, a series of introductions from participants and ice-breaker activities, and an overview to help set expectations about the course objectives.

In the second half of day 1 we will begin with a seminar that introduces and critically examines the meaning of public engagement. This theoretical foundation will help provide important context for the more practical parts of the course.

Continuing on from the discussion in day 1, participants will explore the different ethical, social and practical values of public engagement through a series of case studies. These case studies will be built upon as the course progresses, providing an anchor for subsequent discussions and activities.

Using the case studies introduced at the start of the day, the participants will begin to identify some of the ethical and social challenges associated with their group’s respective case study—each of which will centre upon a different data science or AI project. Participants will be introduced to the ethical framework of responsible research and innovation in data science and AI to help structure this discussion.

When should you engage?

Participants will start considering practical questions related to responsible project governance, which have a bearing on important questions related to project funding, planning, data collection, analysis, and the communication of science and innovation. Participants will be introduced to a model of a typical data science or AI project lifecycle to help structure these activities.

How should you engage?

Once participants have a high-level understanding of where public engagement and communication fits into the project lifecycle, we will start exploring methods for engagement that are linked to key project activities (e.g., problem formulation and data collection).

A core part of public engagement and an increasingly important skill for scientists is the ability to effectively communicate using data and statistics. Participants will explore key examples from the public communication of statistics and also examine the benefits and challenges of using novel software tools for data visualisation.

Having a sound awareness and understanding of social, statistical, and cognitive biases is another vital skill for scientists involved in public engagement. Using the model of the project lifecycle, introduced in day 3, participants will use a tool designed to help identify and mitigate key biases that can hinder the goals of responsible public engagement.

Day 5 is centred upon a keystone activity that is designed to support the participants with the development of their communicative skills. Using the case studies that will have been developed throughout the course activities, each group will plan a hypothetical public engagement and communication event that integrates what they have learned over the previous days.

The course ends with a discussion about the principles of public trust and assurance in science and technology. Participants will return to some of the questions raised in days 1 and 2, in order to reflect upon their own roles and responsibilities—both as researchers, scientists, or developers, on the one hand, and as key members of society on the other.

Applications are open to graduate students and early career researchers across academia and the public sector who have an active research interest in public engagement of data science/AI.

There are no technical prerequisites for this course. However, the course will be taught in English and delivered during normal working hours in the UK.