Algorithmic Analysis of Social Behavior for Profiling, Ranking, and Assessment

Nizan Geslevich Packin, Yafit Lev-Aretz

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


In this chapter, we look at the global development of “people-scoring” and its implications. Unlike traditional credit scoring, which is used to evaluate individuals’ financial trustworthiness, social scoring seeks to comprehensively rank individuals based on social, reputational, and behavioral attributes. The implications of widespread social scoring are far-reaching and troubling. Bias and error, discrimination, manipulation, privacy violations, excessive market power, and social segregation are only some of the concerns we have discussed and elaborated on in previous works.1 In this chapter, we describe the global shift from financial scores to social credit, and show how, notwithstanding constitutional, statutory, and regulatory safeguards, the United States and other Western democracies are not as far from social credit as we seem to believe.
Original languageEnglish
Title of host publicationThe Cambridge Handbook of the Law of Algorithms
EditorsWoodrow Barfield
Place of PublicationCambridge
PublisherCambridge University Press
Number of pages22
ISBN (Electronic)9781108680844
StatePublished - 2020
Externally publishedYes


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