1,721,016 research outputs found
Formal arguments, preferences, and natural language interfaces to humans: An empirical evaluation
No full textIt has been claimed that computational models of argumentation provide support for complex decision making activities in part due to the close alignment between their semantics and human intuition. In this paper we assess this claim by means of an experiment: people's evaluation of formal arguments - presented in plain English - is compared to the conclusions obtained from argumentation semantics. Our results show a correspondence between the acceptability of arguments by human subjects and the justification status prescribed by the formal theory in the majority of the cases. However, post-hoc analyses show that there are some significant deviations, which appear to arise from implicit knowledge regarding the domains in which evaluation took place. We argue that in order to create argumentation systems, designers must take implicit domain specific knowledge into account
Considerations for applying logical reasoning to explain neural network outputs
Full text linkWe discuss the impact of presenting explanations to people for Artificial Intelligence (AI) decisions powered by Neural Networks, according to three types of logical reasoning (inductive, deductive, and abductive). We start from examples in the existing literature on explaining artificial neural networks. We see that abductive reasoning is (unintentionally) the most commonly used as default in user testing for comparing the quality of explanation techniques. We discuss whether this may be because this reasoning type balances the technical challenges of generating the explanations, and the effectiveness of the explanations. Also, by illustrating how the original (abductive) explanation can be converted into the remaining two reasoning types we are able to identify considerations needed to support these kinds of transformations
MovieTweeters: An Interactive Interface to Improve Recommendation Novelty
No full textThis paper introduces and evaluates a novel interface, MovieTweeters. It is a movie recommendation system which incorporates social information with a traditional recommendation algorithm to generate recommendations for users. Few previous studies have investigated the influence of using social information in interactiveinterfaces to improve the novelty of recommendations. To address this gap, we investigate whether social information can be incorporated effectively into an interactive interface to improve recommendation novelty and user satisfaction. Our initial results suggest that such an interactive interface does indeed help users discovermore novel items. Also, we observed users who perceived that they discovered more novel and diverse items reported increased levelsof user satisfaction. Surprisingly, we observed that even though we successfully were able to increase the system diversity of the recommendations, it had a negative correlation with users perception of novelty and diversity of the items highlighting the importance of improved user-centered approaches.Accepted author manuscriptWeb Information System
Supporting High-Uncertainty Decisions through AI and Logic-Style Explanations
Full text linkA common criteria for Explainable AI (XAI) is to support users in establishing appropriate trust in the AI - rejecting advice when it is incorrect, and accepting advice when it is correct. Previous findings suggest that explanations can cause an over-reliance on AI (overly accepting advice). Explanations that evoke appropriate trust are even more challenging for decision-making tasks that are difficult for humans and AI. For this reason, we study decision-making by non-experts in the high-uncertainty domain of stock trading. We compare the effectiveness of three different explanation styles (influenced by inductive, abductive, and deductive reasoning) and the role of AI confidence in terms of a) the users' reliance on the XAI interface elements (charts with indicators, AI prediction, explanation), b) the correctness of the decision (task performance), and c) the agreement with the AI's prediction. In contrast to previous work, we look at interactions between different aspects of decision-making, including AI correctness, and the combined effects of AI confidence and explanations styles. Our results show that specific explanation styles (abductive and deductive) improve the user's task performance in the case of high AI confidence compared to inductive explanations. In other words, these styles of explanations were able to invoke correct decisions (for both positive and negative decisions) when the system was certain. In such a condition, the agreement between the user's decision and the AI prediction confirms this finding, highlighting a significant agreement increase when the AI is correct. This suggests that both explanation styles are suitable for evoking appropriate trust in a confident AI. Our findings further indicate a need to consider AI confidence as a criterion for including or excluding explanations from AI interfaces. In addition, this paper highlights the importance of carefully selecting an explanation style according to the characteristics of the task and data
Report on NORMalize: The First Workshop on the Normative Design and Evaluation of Recommender Systems
Full text linkReport on NORMalize: The First Workshop on the Normative Design and Evaluation of Recommender Systems
Full text linkGoing Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Report on NORMalize: The First Workshop on the Normative Design and Evaluation of Recommender Systems
No full textIUI 2020: Proceedings of the 25th international conference on intelligent user interfaces
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