FAIR AI: A Conceptual Framework for Democratisation of 21st Century AI
Saman Kumara Halgamuge · 2021
Popular models of AI have two significant deficiencies: 1) they are mostly manually designed using the experience of AI-experts 2) they lack human interpretability, i.e., users cannot make sense of the functionality of neural network architectures either semantically/linguistically or mathematically. This lack of interpretability is a main inhibitor of broader use of 21st century AI, e.g., Deep Neural Networks (DNN). The dependence on AI experts to create AI hinders the democratisation of AI and therefore the accessibility to AI. Addressing these deficiencies would provide answers to some of the valid questions about traceability, accountability and the ability to integrate existing knowledge (scientific or linguistically articulated human experience) into the model. This keynote abstract addresses these two significant deficiencies that inhibit the democratisation of AI by developing new methods that can automatically create interpretable neural network models without the help of AI-experts. The proposed cross-fertilised innovation will have a profound impact on the society through the increased accessibility and trustworthiness of AI beneficial to almost all areas of sciences, engineering, and humanities.