论文标题
机器学习犯罪累犯模型的准确性，公平性和解释性
Accuracy, Fairness, and Interpretability of Machine Learning Criminal Recidivism Models
论文作者
论文摘要
犯罪累犯模型是在美国各地通过假释委员会广泛采用的工具，以协助假释决定。这些模型收集了有关个人的大量数据，然后预测个人是否会在假释上释放犯罪。尽管这样的模型不是做出最终假释决定的唯一或主要因素，但已经提出了有关其准确性，公平性和解释性的问题。在本文中，基于美国佐治亚州的现实世界假释决策数据集创建了各种基于机器的犯罪累犯模型。累犯模型的准确性，公平性和解释性相对评估。据发现，准确性，公平性和天生可解释的差异和权衡存在差异和权衡。因此，选择最佳模型取决于准确性，公平性和解释性之间的所需平衡，因为没有模型是完美的或始终是不同标准的最佳模型。
Criminal recidivism models are tools that have gained widespread adoption by parole boards across the United States to assist with parole decisions. These models take in large amounts of data about an individual and then predict whether an individual would commit a crime if released on parole. Although such models are not the only or primary factor in making the final parole decision, questions have been raised about their accuracy, fairness, and interpretability. In this paper, various machine learning-based criminal recidivism models are created based on a real-world parole decision dataset from the state of Georgia in the United States. The recidivism models are comparatively evaluated for their accuracy, fairness, and interpretability. It is found that there are noted differences and trade-offs between accuracy, fairness, and being inherently interpretable. Therefore, choosing the best model depends on the desired balance between accuracy, fairness, and interpretability, as no model is perfect or consistently the best across different criteria.
