Algorithmic Regulation

By Karen Yeung, M. Lodge. 2019

This book presents interdisciplinary perspectives on algorithms which can be observed in contemporary debates. These have been gathered together under the broad notion of ‘algorithmic regulation’. This introductory chapter develops the book’s argument in a number of steps. First, it explores the meaning of ‘algorithmic regulation’. Second, it reflects on how debates regarding algorithmic regulation relate to wider debates about the interaction between technology and regulation. Third, it considers algorithmic systems as potentially novel types of large technical systems. In doing so, it highlights the growing consensus regarding critical challenges for the regulation and governance of algorithmic systems, but also notes the lack of agreement regarding potential solutions. It concludes with an overview of the subsequent chapters.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Adam M. Chekroud
2. Z. Cohen
3. J. Bondar
4. Jaime Delgadillo
5. Gavin Doherty
6. Akash R. Wasil
7. Marjolein Fokkema
8. D. Belgrave

Citations in the corpus

• The promise of machine learning in predicting treatment outcomes in psychiatry (2021, background): “Objective unbiased applications might help reduce discrimination in machine learning 211,212 .”
• Ethics of artificial intelligence and robotics (2020, background): “This general political angle of this discussion has been stressed by (O’Neil 2016) in her influential book Weapons of Math Destruction, and in (Yeung and Lodge 2019).”