Adapting to the addressee is crucial for successful explanations, yet poses significant challenges for dialogsystems. We adopt the approach of treating explanation generation as a non-stationary decision process, where the optimal strategy varies according to changing beliefs about the explainee and the interaction context. In this paper we address the questions of (1) how to track the interaction context and the relevant listener features in a formally defined computational partner model, and (2) how to utilize this model in the dynamically adjusted, rational decision process that determines the currently best explanation strategy. We propose a Bayesian inference-based approach to continuously update the partner model based on user feedback, and a non-stationary Markov Decision Process to adjust decision-making based on the partner model values. We evaluate an implementation of this framework with five simulated interlocutors, demonstrating its effectiveness in adapting to different partners with constant and even changing feedback behavior. The results show high adaptivity with distinct explanation strategies emerging for different partners, highlighting the potential of our approach to improve explainable AI systems and dialogsystems in general.

Recently, the proliferation of omni-channel platforms has attracted interest in customer journeys, particularly regarding their role in developing marketing strategies. However, few efforts have been taken to quantitatively study or comprehensively analyze them owing to the sequential nature of their data and the complexity involved in analysis. In this study, we propose a novel approach comprising three steps for analyzing customer journeys. First, the distance between sequential data is defined and used to identify and visualize representative sequences. Second, the likelihood of purchase is predicted based on this distance. Third, if a sequence suggests no purchase, counterfactual sequences are recommended to increase the probability of a purchase using a proposed method, which extracts counterfactual explanations for sequential data. A survey was conducted, and the data were analyzed; the results revealed that typical sequences could be extracted, and the parts of those sequences important for purchase could be detected. We believe that the proposed approach can support improvements in various marketing activities.

Argumentation is a central subarea of Artificial Intelligence (AI) for modeling and reasoning about arguments. The semantics of abstract argumentation frameworks (AFs) is given by sets of arguments (extensions) and conditions on the relationship between them, such as stable or admissible. Today's solvers implement tasks such as finding extensions, deciding credulous or skeptical acceptance, counting, or enumerating extensions. While these tasks are well charted, the area between decision, counting/enumeration and fine-grained reasoning requires expensive reasoning so far. We introduce a novel concept (facets) for reasoning between decision and enumeration. Facets are arguments that belong to some extensions (credulous) but not to all extensions (skeptical). They are most natural when a user aims to navigate, filter, or comprehend the significance of specific arguments, according to their needs. We study the complexity and show that tasks involving facets are much easier than counting extensions. Finally, we provide an implementation, and conduct experiments to demonstrate feasibility.

As the field of healthcare increasingly adopts artificial intelligence, it becomes important to understand which types of explanations increase transparency and empower users to develop confidence and trust in the predictions made by machine learning (ML) systems. In shared decision-making scenarios where doctors cooperate with ML systems to reach an appropriate decision, establishing mutual trust is crucial. In this paper, we explore different approaches to generating explanations in eXplainable AI (XAI) and make their underlying arguments explicit so that they can be evaluated by medical experts. In particular, we present the findings of a user study conducted with physicians to investigate their perceptions of various types of AI-generated explanations in the context of diagnostic decision support. The study aims to identify the most effective and useful explanations that enhance the diagnostic process. In the study, medical doctors filled out a survey to assess different types of explanations. Further, an interview was carried out post-survey to gain qualitative insights on the requirements of explanations incorporated in diagnostic decision support. Overall, the insights gained from this study contribute to understanding the types of explanations that are most effective.

Machine Learning as a Service (MLaaS) has gained important attraction as a means for deploying powerful predictive models, offering ease of use that enables organizations to leverage advanced analytics without substantial investments in specialized infrastructure or expertise. However, MLaaS platforms must be safeguarded against security and privacy attacks, such as model extraction (MEA) attacks. The increasing integration of explainable AI (XAI) within MLaaS has introduced an additional privacy challenge, as attackers can exploit model explanations particularly counterfactual explanations (CFs) to facilitate MEA. In this paper, we investigate the trade offs among model performance, privacy, and explainability when employing Differential Privacy (DP), a promising technique for mitigating CF facilitated MEA. We evaluate two distinct DP strategies: implemented during the classification model training and at the explainer during CF generation.

In recent years, Artificial Intelligence technology has excelled in various applications across all domains and fields. However, the various algorithms in neural networks make it difficult to understand the reasons behind decisions. For this reason, trustworthy AI techniques have started gaining popularity. The concept of trustworthiness is cross-disciplinary; it must meet societal standards and principles, and technology is used to fulfill these requirements. In this paper, we first surveyed developments from various countries and regions on the ethical elements that make AI algorithms trustworthy; and then focused our survey on the state of the art research into the interpretability of AI. We have conducted an intensive survey on technologies and techniques used in making AI explainable. Finally, we identified new trends in achieving explainable AI. In particular, we elaborate on the strong link between the explainability of AI and the meta-reasoning of autonomous systems. The concept of meta-reasoning is 'reason the reasoning', which coincides with the intention and goal of explainable Al. The integration of the approaches could pave the way for future interpretable AI systems.

There is a growing demand for the use of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, particularly as clinical decision support systems to assist medical professionals. However, the complexity of many of these models, often referred to as black box models, raises concerns about their safe integration into clinical settings as it is difficult to understand how they arrived at their predictions. This paper discusses insights and recommendations derived from an expert working group convened by the UK Medicine and Healthcare products Regulatory Agency (MHRA). The group consisted of healthcare professionals, regulators, and data scientists, with a primary focus on evaluating the outputs from different AI algorithms in clinical decision-making contexts. Additionally, the group evaluated findings from a pilot study investigating clinicians' behaviour and interaction with AI methods during clinical diagnosis. Incorporating AI methods is crucial for ensuring the safety and trustworthiness of medical AI devices in clinical settings. Adequate training for stakeholders is essential to address potential issues, and further insights and recommendations for safely adopting AI systems in healthcare settings are provided.

Improving end-users' understanding of decisions made by autonomous vehicles (AVs) driven by artificial intelligence (AI) can improve utilization and acceptance of AVs. However, current explanation mechanisms primarily help AI researchers and engineers in debugging and monitoring their AI systems, and may not address the specific questions of end-users, such as passengers, about AVs in various scenarios. In this paper, we conducted two user studies to investigate questions that potential AV passengers might pose while riding in an AV and evaluate how well answers to those questions improve their understanding of AI-driven AV decisions. Our initial formative study identified a range of questions about AI in autonomous driving that existing explanation mechanisms do not readily address. Our second study demonstrated that interactive text-based explanations effectively improved participants' comprehension of AV decisions compared to simply observing AV decisions. These findings inform the design of interactions that motivate end-users to engage with and inquire about the reasoning behind AI-driven AV decisions.

The need to explain predictive models is well-established in modern machine learning. However, beyond model interpretability, understanding pre-processing methods is equally essential. Understanding how data modifications impact model performance improvements and potential biases and promoting a reliable pipeline is mandatory for developing robust machine learning solutions. Isolation Forest (iForest) is a widely used technique for outlier detection that performs well. Its effectiveness increases with the number of tree-based learners. However, this also complicates the explanation of outlier selection and the decision boundaries for inliers. This research introduces a novel Explainable AI (XAI) method, tackling the problem of global explainability. In detail, it aims to offer a global explanation for outlier detection to address its opaque nature. Our approach is based on the Decision Predicate Graph (DPG), which clarifies the logic of ensemble methods and provides both insights and a graph-based metric to explain how samples are identified as outliers using the proposed Inlier-Outlier Propagation Score (IOP-Score). Our proposal enhances iForest's explainability and provides a comprehensive view of the decision-making process, detailing which features contribute to outlier identification and how the model utilizes them. This method advances the state-of-the-art by providing insights into decision boundaries and a comprehensive view of holistic feature usage in outlier identification. -- thus promoting a fully explainable machine learning pipeline.

Public deliberation, as in open discussion of issues of public concern, often suffers from scattered and shallow discourse, poor sensemaking, and a disconnect from actionable policy outcomes. This paper introduces BCause, a discussion system leveraging generative AI and human-machine collaboration to transform unstructured dialogue around public issues (such as urban living, policy changes, and current socio-economic transformations) into structured, actionable democratic processes. We present three innovations: (i) importing and transforming unstructured transcripts into argumentative discussions, (ii) geo-deliberated problem-sensing via a Telegram bot for local issue reporting, and (iii) smart reporting with customizable widgets (e.g., summaries, topic modelling, policy recommendations, clustered arguments). The system's human-AI partnership preserves critical human participation to ensure ethical oversight, contextual relevance, and creative synthesis.