Algebraic Machine Learning (AML) has recently been proposed as a new learning paradigm that builds upon abstract algebra and model theory. Unlike deep learning and other popular learning algorithms, AML is not a statistical method. Instead, it produces generalizing models from semantic embeddings of data into discrete algebraic structures. The result is a machine learning paradigm that:

1. is far less sensitive to the statistical characteristics of the training data and does not fit (or even use) parameters;
2. has the potential to flexibly and seamlessly combine unstructured and complex information contained in training data with a formal specification of human knowledge including constraints and task goals;
3. has higher mathematical transparency than deep networks and other optimization-based statistical methods and uses sets and graphs as internal representations of data which are ideal for generating human-understandable descriptions of what, why and how has been learned; and
4. can be implemented in a distributed way that avoids centralized, privacy-invasive collections of large datasets in favor of a collaboration of many local learners.

The aim of the project is to leverage the above properties of AML for a new generation of interactive, human-centric machine learning systems. Interactive means that the system should allow human users and intelligent machines to jointly learn and reason. AML (see section 1.3.1 for an introduction) is ideal to enable human users to not only reflect upon the learning process but also to actively drive it, simultaneously enhancing
their own cognitive powers through the interaction with the AI. Reflecting the vision of Human-Centric AI, we will:

• reduce bias and prevent discrimination by benefiting from the reduced dependence on statistical properties of training data (property 1), integrating formalized human knowledge with regular training data (property 2), and exploring the how and why of the learning process (property 3);
• facilitate trust and reliability by respecting ‘hard’ human-defined constraints in the learning process (property 2) and by enhancing explainability of the learned models (property 3);
• integrate complex ethical constraints into Human-AI systems by going beyond basic bias and discrimination prevention (property 2) to interactively shape between humans and the machine the ethics related to the learning process (property 3); and
• facilitate a new distributed, incremental collaborative learning method by going beyond the dominant centralized and off-line data processing approach (property 4).