Dialogical guidelines aided by knowledge acquisition: Enhancing the design of explainable interfaces and algorithmic accuracy

Understanding expert domain knowledge may inform the design of explainable interfaces that convey comprehensible information by “mirroring” the explanation practice of domain experts. Likewise, scrutinizing expert domain knowledge is pivotal to guarantee data quality and enhance algorithmic accuracy, by zooming in on the types of data and information that constitute relevant and reliable representations in a given domain. Against this backdrop, the paper revitalizes the field of knowledge acquisition and presents easily applicable user-centered and value-oriented dialogical guidelines to unravel domain knowledge with the aim of enhancing the design of explainable interfaces and algorithmic accuracy. While it might seem counter-intuitive to revisit the field of knowledge acquisition in the era of machine learning and deep learning, there are plenty of cases in which AI systems, trained on biased data, have led to epistemological deficiencies with morally harmful consequences. In order to improve the data preparation and modelling stage in the development of ML models, this paper suggests that AI developers could benefit from the pragmatic application of manageable dialogical guidelines aided by knowledge acquisition to cultivate shared understanding between AI developers and domain expert end users.