Tutorial: Causal AI for Web and Health Care.
Tutorial: Causal AI for Web and Health Care.
复制标题
教程：网络和医疗保健的因果人工智能。
DOI：
10.1145/3543873.3587713
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Usha Lokala, Kaushik Roy
中科院分区：
文献类型：
--
作者：
Usha Lokala, Kaushik Roy
改善ML算法的性能和解释是人类在真实的世界中采用的优先事项。在医疗保健等关键领域，这种技术具有巨大的潜力，可以通过提供大规模的高质量帮助来减轻人类的负担，并大大减少人工评估。在当今数据驱动的世界中，人工智能（AI）系统仍然面临着偏见、可解释性以及类人推理和可解释性的问题。因果人工智能是一种可以进行推理并做出类似人类选择的技术，它可以超越狭隘的基于机器学习的技术，并可以集成到人类决策中。它还提供了内在的可解释性，新的领域适应性，无偏差预测，并与各种规模的数据集一起工作。在本教程中，我们详细介绍了如何使用基于知识图（KG）的方法在AI系统中更丰富地表示因果关系，以进行干预和反事实推理（图1），我们如何获得基于模型和领域的可解释性，因果表示如何帮助网络和医疗保健。
Improving the performance and explanations of ML algorithms is a priority for adoption by humans in the real world. In critical domains such as healthcare, such technology has significant potential to reduce the burden on humans and considerably reduce manual assessments by providing quality assistance at scale. In today’s data-driven world, artificial intelligence (AI) systems are still experiencing issues with bias, explainability, and human-like reasoning and interpretability. Causal AI is the technique that can reason and make human-like choices making it possible to go beyond narrow Machine learning-based techniques and can be integrated into human decision-making. It also offers intrinsic explainability, new domain adaptability, bias free predictions, and works with datasets of all sizes. In this tutorial of type lecture style, we detail how a richer representation of causality in AI systems using a knowledge graph (KG) based approach is needed for intervention and counterfactual reasoning (Figure 1), how do we get to model-based and domain explainability, how causal representations helps in web and health care.