1,720,960 research outputs found
Mitigating Human Errors and Cognitive Bias for Human-AI Synergy in Cybersecurity
Full text linkCybersecurity advancements necessitate effective measures to combat rising and sophisticated threats. Artificial Intelligence (AI) and eXplainable AI (XAI) solutions have demonstrated significant capabilities in predicting and responding to cyber threats. Moreover, integrating AI components with Intelligent User Interfaces (IUI) has been explored as a promising approach, emphasizing user experience and interaction policies. Despite these advancements, the primary challenge remains addressing human errors, particularly those induced by cognitive biases. This paper provides an overview of possible recommendations on AI integration with cybersecurity systems and human cognitive bias mitigation solutions
Explaining Through the Right Reasoning Style: Lessons Learnt
No full textCurrent eXplainable Artificial Intelligence (XAI) techniques assist individuals in interpreting AI recommendations. However, research primarily focuses on assessing users’ comprehension of explanations, neglecting important factors influencing decision support, such as whether the explanation uses the correct reasoning style to help the user understand the AI’s advice. In the last two years, our research aimed to fill this gap by examining the effects of factors such as user uncertainty, AI correctness, and the interplay between AI confidence and explanation logic styles in classification tasks. In this paper, we summarise the lesson learnt from this research and discuss its impact on the engineering of AI-based decision support systems
Inspecting Data Using Natural Language Queries
No full textIn this paper, we discuss a simple architecture for supporting the inspection of a generic dataset using natural language queries. We show how to integrate modern Artificial Intelligence libraries in the system and how to derive chart visualization out of the user’s intent. The result is a lightweight architecture for supporting such natural language queries in web-based visualization tools. Finally, we report on the user evaluation of the interface, showing a good acceptance and effectiveness of the proposed approach
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
Going 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
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
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
An interface for explaining the automatic classification of runners' trainings
No full textThis paper discusses an explainable intelligent interface supporting coaches in providing feedback to runners tracking their progress through a mobile application. The interface explicitly shows the confidence on the assigned ratings, it supports the impact analysis of the different recorded metrics and it allows controlling and reinforcing the assessment
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