# Topic9：XAI in line with human cognitive

## Motivation：
* The current deep learning model is essentially black box due to its technical complexity , which leads to the opacity and inexplicability of AI services and further restricts the commercial application and promotion of AI services. Existing explainable AI technology mainly focuses on how to provide limited engineering auxiliary information to the model, but ignores the understanding of AI models from the perspective of human cognition.
* Humans usually understand things through analogies, metaphors, induction and other cognitive methods, and have a certain process of mental cognition construction. Thus, in this project, we expect to be able to explore more systematic and explainable AI methods that conform to human cognition, including interactive interfaces, explainable methods, measurement methods, and so on.


## Target：
A complete set of explainable AI methods and strategies in line with human cognition, providing necessary interactive cognitive interface design solutions for different scenarios and different cognitions, and a case study for typical scenarios.

## Method：
​We expect the applicant can conduct XAI research based on MindSpore, and hope to get your valuable suggestions to MindSpore in the process. We will do our best to improve the capabilities of the MindSpore framework and  provide you with the most powerful technical support.

## How To Join：
* Submit an issue/PR based on community discussion for consultation or claim on related topics
* Submit your proposal to us by email roc.wangyunpeng@huawei.com