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Algorithmic governance

Objectives

Algorithmic governance is broadly defined as governance by algorithmic means in public administration, where some order and coordination among actors is achieved through the use of computable procedures referred to as (machine learning) algorithms. The rule by algorithms, or algorithmic governance, is proposed as an alternative to bureaucracy (legal/rationality-driven) and market (price-driven) systems. As the number of decisions made by algorithms grows, there is an increasing enthusiasm about their potential to bring positive changes, raising concerns about the negative implications of algorithmic decision-making. This course enables students to understand the fundamental principles of governance, as it evolved over the years, the algorithms, and how the governance is impacted by algorithmic decision-making. It will provide students with the understanding of both positive and negative implications of algorithmic governance, the practices in different governments.


General characterization

Code

2621

Credits

3.5

Responsible teacher

Leid Zejnilovic

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites

n/a


Bibliography

Freely accessible online A governance framework for algorithmic accountability and transparency https://www.europarl.europa.eu/RegData/etudes/STUD/2019/624262/EPRS_STU(2019)624262_EN.pdf

Ebers, M., Gamito, M. C., (2021). Algorithmic Governance and Governance of Algorithms Legal and Ethical Challenges. SpringerLink 

 

Teaching method

Lectures will be the predominant form of presentation and will be complemented by a variety of teaching approaches including discussion of case studies and in-class exercises. A full interchange between the instructor and the participant is expected. Preparation before class is of crucial importance.

The course will also benefit from guest lecturers who will visit the class to share their experience in algorithmic governance. 


Evaluation method

1. Class participation (10%) 

2. In-class quizzes (20%) 

3. Exam (70%)

 

Subject matter

This course is divided into two components: 

Component 1: Introduction to Governance, and Machine Learning In the first three weeks, the student will review the evolution of the theory and practice of governance, and get to know the fundamentals of machine learning, in the context of governance.

Component 2: Governance through human-computer decision-making, In the last three weeks, we will focus on the decision-making along the human-only to fully automated spectrum, and inherent ethical considerations. Finally, the student will have an opportunity to contextualize the knowledge and reflect upon the possible algorithmic governance applications, by reviewing the practices in different countries and thinking of new ways to challenge the challenge governance.