算法黑箱下污染预测责任主体与追溯困境研究

Research on the Subject of Pollution Prediction Liability and the Dilemma of Traceability under the Algorithm Black Box

摘要: 本文聚焦算法黑箱导致的污染预测责任主体界定与损害追溯难题，从技术、法律与实践三维度展开分析。技术层面，深度学习模型的决策逻辑不可解释性与环境数据的动态复杂性，导致预测偏差的具体成因难以追溯；法律层面，现行环境法律对算法开发者、数据提供者、模型使用者等多元主体的责任划分模糊，传统因果关系证明规则难以适应多主体交织的责任链条；实践层面，污染损害的累积性、滞后性与跨区域特征，进一步加剧了责任追溯的现实困境。研究指出，破解上述难题需构建技术透明化、法律框架完善与协同治理相结合的多元规制路径，通过可解释AI技术创新、算法审计制度、分级责任制度及跨学科监管机制的协同作用。

Abstract: This paper delves into the challenges of identifying pollution prediction liability subjects and tracing damages in algorithmic black boxes and analyzes across technology, law, and practice. Technologically, deep learning models’ non-interpretable decision-making and environmental data’s dynamic complexity obscure prediction bias causes. Legally, current environmental laws lack clear liability demarcations for algorithm developers, data providers, and model users; traditional causality rules are ill-suited to multi-subject liability chains. Practically, pollution damages’ accumulative, lagging, and cross-regional traits compound liability-tracing difficulties. The study proposes a multi-regulatory approach integrating technological transparency, legal enhancements, and collaborative governance, via explainable AI, algorithm audits, hierarchical liability, and interdisciplinary oversight.