Fair and Effective Policing for Neighborhood Safety: Understanding and Overcoming Selection Biases

Abstract

An accurate crime prediction and risk estimation can help improve the efficiency and effectiveness of policing activities. However, reports have revealed that biases like racial prejudice could exist in policing enforcement, and trained predictors may inherit them. In this work, we study the possible reasons and countermeasures to this problem, using records from the New York frisk and search program (NYCSF) as the dataset. Concretely, we provide analysis on the possible origin of this phenomenon from the perspective of risk discrepancy, and study it with the scope of selection bias. Motivated by theories in causal inference, we propose a re-weighting approach based on propensity score to balance the data distribution, with respect to the identified treatment: search action. Naively applying existing re-weighting approaches in causal inference is not suitable as the weight is passively estimated from observational data. Inspired by adversarial learning techniques, we formulate the predictor training and re-weighting as a min-max game, so that the re-weighting scale can be automatically learned. Specifically, the proposed approach aims to train a model that: 1) able to balance the data distribution in the searched and un-searched groups; 2) remain discriminative between treatment interventions. Extensive evaluations on real-world dataset are conducted, and results validate the effectiveness of the proposed framework.

T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data

“…Addressing these challenges, we propose an incremental model selection approach in the form of a dynamic network to handle sequentially shifting data. In this paper, we introduce T-SaS, a novel Bayesian-based approach that combines dynamic network selection with a shift points estimation scheme for sequentially evolving data [12]. This approach enables the modeling of distinct distributions by evolving the network structure [30] and facilitates positive knowledge transfer across complex dynamic regimes.…”