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Author Prabjot Kandola & Chris Baber |
| Abstract We used a theoretical framework of human-centred explainable artificial intelligence (XAI) as the basis for design of a recommender system. We evaluated the recommender through a user trial. Our primary measures were the degree to which users agreed with the recommendations and the degree to which user decisions changed following the interaction. We demonstrate that, interacting with the recommender system, resulted in users having a clearer understanding of the features that contribute to their decision (even if they did not always agree with the recommender system’s decision or change the decision). We argue that the design illustrates the XAI framework and supports the proposal that explanation involves a two-stage dialogue. |
