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Evaluating and evolving explanatory content of a computational creative system

Primary supervisor

Maria Teresa Llano


  • Camilo Cruz

Explainable AI (XAI) is a subfield of AI that has grown in recent years with the goal of making black box systems more transparent and accountable through models of explanation that communicate the way decisions have been reached. Research into the role of XAI in creative systems is in its infancy, but is rapidly gaining attention in how we approach the development of co-creative AI systems. In this project, you will study a framework for the evaluation and evolution of explanatory content of Computational Creative (CC) systems. 

In the project you will be tasked with the development of a prototype of an evolutionary system, capable of producing explanatory content of a CC system. The process will involve the exploration and implementation of evaluation methods tailored to assess the attributes that make explanatory content useful to human users of the system. You will also be likely to help conduct user studies that will analyse the effects of these explanations when co-creating an artefact alongside the CC system.


Required knowledge

Background on evolutionary algorithms is desirable but not essential.