Junior Research Group Correctable Hybrid Artificial Intelligence (chAI)

Motivation, Background, and Problem Statement

Data-driven artificial intelligence (AI) methods, especially deep neural networks (DNNs), hold great potential for automating processes in safety-critical applications. Examples include environmental perception, such as camera-based perception in robotics or autonomous driving. However, to ensure the safe application of these methods, their verifiability and correctability must be guaranteed. This also applies to symbolic knowledge, i.e., knowledge describable in natural or formal language, such as traffic rules ("If the light is red, stop") or known object relationships ("A head belongs to a person"). Unfortunately, current DNNs fail to meet these requirements: these purely statistical models are large and opaque, offering insufficient guarantees for verifying learned knowledge or integrating and correcting symbolic knowledge effectively.

Objective and Approach

The junior research group Correctable Hybrid Artificial Intelligence (chAI) aims to develop methods that allow symbolic knowledge to be (1) verified, (2) specifically corrected within the DNN (→ correctable), and (3) incorporated during training and design (→ hybrid). This will involve leveraging methods

• from the field of explainable AI to uncover and track information within DNNs, in particular introspection techniques like concept activation vectors;
• for formal solvers to obtain guarantees;
• the field knowledge representations.