Using Computational Creativity to Guide Data-intensive Scientific Discovery

The generation of plausible hypotheses from observations is a creative process.  Scientists looking to explain phenomena must invent hypothetical relationships between their dependent and independent variables and then design methods to verify or falsify them. Data-driven science is expanding both the role of artificial intelligence in this process and the scale of the observations from which hypotheses must be abduced. We adopt methods from the field of computational creativity -- which seeks to model and understand creative behaviour -- to the generation of scientific hypotheses.  We argue that the generation of new insights from data is a creative process, and that a search for new hypotheses can be guided by evaluating those insights as creative artefacts. We present a framework for data-driven hypothesis discovery that is based on a computational model of creativity evaluation.