最近，提出了一种具有混合AI的设计模式的闺房（图形语言），结合了符号和子象征学习和推理。在本文中，我们将这种博语与演员及其互动扩展。本文的主要贡献是：1）分类法延长分布式混合AI系统，与演员和相互作用;2）示出了使用多种子体系统和人剂相互作用相关的一些设计模式的示例。

A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations, which spreads through collected data. When not properly accounted for, machine learning (ML) models learned from data can reinforce the structural biases already present in society. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches show regularly biased behaviors. However, we show that standard mitigation techniques, and our own post-hoc method, can be effective in reducing the level of unfair bias. We provide practical recommendations to develop ML models for depression risk prediction with increased fairness and trust in the real world. No single best ML model for depression prediction provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions.