Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Clear Sanctions, Vague Rewards: How China's Social Credit System Currently Defines "Good" and "Bad" Behavior

: Engelmann, S.; Chen, M.; Fischer, F.; Kao, C.Y.; Grossklags, J.


Association for Computing Machinery -ACM-:
FAT* 2019, Conference on Fairness, Accountability, and Transparency. Proceedings : January 29-31, 2019, Atlanta, GA, USA
New York: ACM, 2019
ISBN: 978-1-4503-6125-5
Conference on Fairness, Accountability, and Transparency (FAT*) <2019, Atlanta/Ga.>
Fraunhofer AISEC ()

China's Social Credit System (SCS, 社会信用体系 or shehui xinyong tixi) is expected to become the first digitally-implemented nationwide scoring system with the purpose to rate the behavior of citizens, companies, and other entities. Thereby, in the SCS, "good" behavior can result in material rewards and reputational gain while "bad" behavior can lead to exclusion from material resources and reputational loss. Crucially, for the implementation of the SCS, society must be able to distinguish between behaviors that result in reward and those that lead to sanction. In this paper, we conduct the first transparency analysis of two central administrative information platforms of the SCS to understand how the SCS currently defines "good" and "bad" behavior. We analyze 194,829 behavioral records and 942 reports on citizens' behaviors published on the official Beijing SCS website and the national SCS platform "Credit China", respectively. By applying a mixed-method approach, we demonstrate that there is a considerable asymmetry between information provided by the so-called Redlist (information on "good" behavior) and the Blacklist (information on "bad" behavior). At the current stage of the SCS implementation, the majority of explanations on blacklisted behaviors includes a detailed description of the causal relation between inadequate behavior and its sanction. On the other hand, explanations on redlisted behavior, which comprise positive norms fostering value internalization and integration, are less transparent. Finally, this first SCS transparency analysis suggests that socio-technical systems applying a scoring mechanism might use different degrees of transparency to achieve particular behavioral engineering goals.