A Contrastive Evaluation Method for Discretion in Administrative Penalty

Abstract

Discretion, namely discretionary power, indicates that administrative agencies could make modifiable decisions under personal judgment when facing situations defined in the law. It plays an essential part in an administrative practice that existing laws and regulations could hardly cover all cases. However, this may also cause the abuse of enforcement power. The rapid development of the Internet of Things (IoT) and databases has provided a powerful tool to measure discretionary power, such as judging if a given administrative punishment is appropriate, and recommending similar cases for a new law-violation record. In this paper, we develop a multi-task framework to extract contrastive patterns from historical records and recommend unprocessed penalties. There is massive ambiguity in collected records, where the limited samples of specific penalties and a large number of whole records make it hard to distinguish factors in individual administrative enforcement actions. We propose an automatic data-labeling method based on data pattern discovery, clustering, and statistical analysis to replace manual labeling under potential personal prejudice. We estimate the distribution of collected penalty records to distinguish deviated and reasonable ones, then produce contrastive samples, which are fed into different network branches. We build a complete IoT platform and collect three-year administrative penalty records nationwide as an empirical evaluation. Experiments show that our proposed methods can learn reasonable discretion by measuring the objectiveness in samples and combining it with a joint training strategy. The final results of penalty amount forecasting and penalty reasonableness judging tasks reach ready-to-use performance.