90904 - Prediction And The Future Of Public Policy

Learning outcomes

The aim of the course is to provide an overview of the evolution of forms of prediction in the public sphere. At the end of the course the student will be able to: • distinguish probabilistic and algorithmic forms of prediction • identify assumptions and consequences of different forms of prediction • analyze and interpret concrete cases of planning in the field of public policy.

Course contents

The algorithmic turn of prediction, connected with big data and machine learning, introduces a radical technical-computational change from probabilistic forms of uncertainty management, whose effects extend throughout society. The course will focus on the social effects of this transition, promising innovative solutions and yet producing disruptive problems. The first part of the course will deal with the general theory of prediction, it social preconditions and its historical development. In the second part of the course we will discuss the consequences of changing forms of prediction in different field of society, and specifically in the field of policing.

Against the backdrop of the topic of prediction, course participants will familiarize with the issues of the social consequences of digitization in contemporary society. Within the framework of the general theory of society, they will acquire the skills required to observe and critically comment on the diffusion of technological innovations such as machine learning algorithms and big data. The focus on predictive policing will allow to concretely examine issues such as social and technological bias, pre-emption, relationship between technologies and public policies, impact of digitization on organizations.

Readings/Bibliography

A detailed syllabus will be provided at the beginning of the course.

Teaching methods

The course is organized according to the model of the Structured Seminar. Ten hours will be taught online weekly in five 2-hour classes that introduce the basic issues of algorithmic prediction. The remaining classes will be organized as seminars held in presence in ten meetings (3 hours each).

Participants are expected to read the materials in advance and actively contribute to the discussion. They must prepare two questions for each meeting, that can be presented and debated during the sessions. After each session, they will deliver a short memo (e.g. several bullet points) summarizing their understanding of the outcome of the meeting in terms of the issues they find most relevant. At the end of the course, they will write a paper commenting and discussing one of the topic discussed during the meetings.

Assessment methods

Class attendance: 35%

Oral presentation: 25%

Written memos and paper: 40%

Office hours

See the website of Elena Esposito