Algorithmic Decision Systems: Automation and Machine Learning in the Public Administration
The Cambridge Handbook of the Law of Algorithms (2020)
28 Pages Posted: 1 Feb 2022
Date Written: November 30, 2020
Our society in the twenty-first century is being shaped evermore by sets of instructions running at data centers spread around the world, commonly known as “algorithms.” Algorithmic decision systems (ADS) are deployed for purposes as disparate as pricing in online marketplaces, flying planes, generating credit scores, and predicting demand for electricity. Advanced ADS are characterized by two key features. First, they rely on the analysis of large amounts of data to make predictive inferences, such as the likelihood of a default for a potential borrower or an increase in demand for electricity consumption. Second, they automate in whole or in part the execution of decisions, such as refusing a loan to a high-risk borrower or increasing energy prices during peak hours, respectively. ADS may also refer to less advanced systems implementing only one of these features. Although ADS generally have proven to be beneficial in improving the efficiency of making decisions, the underlying algorithms remain controversial, among other issues, because they are susceptible to discrimination, bias, and a loss of privacy – with the potential to even be used to manipulate the democratic processes and structures underpinning our society – alongside lacking effective means of control and accountability. Drawing on multiple case studies in the United States, France and the United Kingdom, the chapter analyses four specific features of ADS deployed in the public administration: (1) the formalization of legal rules and transcription into computer code; (2) the generation of datasets; (3) data processing through machine-learning techniques; and (4) automated execution of decisions. It concludes by showing how these features are triggering at least three transformations in legal decision-making with significant implications for the rule of law: (1) the hybridisation of legal rules, (2) the emergence of data driven rules, and (3) the conflation of the life cycle of law.
Keywords: Algorithms, law and society, public administration, accountability, machine learning, law
JEL Classification: K10, K23, K40
Suggested Citation: Suggested Citation