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This content will become publicly available on April 30, 2024

Title: Tutorial: Causal AI for Web and Health Care.
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.  more » « less
Award ID(s):
2133842 2113350 2110926 2007976
NSF-PAR ID:
10429248
Author(s) / Creator(s):
Date Published:
Journal Name:
Companion Proceedings of the ACM Web Conference
Page Range / eLocation ID:
648 to 658
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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