Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles - Muschalik, Maximilian; Fumagalli, Fabian; Hammer, Barbara; Huellermeier, Eyke (Proceedings of the AAAI Conference on Artificial Intelligence)
While shallow decision trees may be interpretable, larger ensemble models like gradient-boosted trees, which often set the state of the art in machine learning problems involving tabular data, still remain black box models. As a remedy, the Shapley value (SV) is a well-known concept in explainable artificial intelligence (XAI) research for quantifying additive feature attributions of predictions. The model-specific TreeSHAP methodology solves the exponential complexity for retrieving exact SVs from tree-based models. Expanding beyond individual feature attribution, Shapley interactions reveal the impact of intricate feature interactions of any order. In this work, we present TreeSHAP-IQ, an efficient method to compute any-order additive Shapley interactions for predictions of tree-based models. TreeSHAP-IQ is supported by a mathematical framework that exploits polynomial arithmetic to compute the interaction scores in a single recursive traversal of the tree, akin to Linear TreeSHAP. We apply TreeSHAP-IQ on state-of-the-art tree ensembles and explore interactions on well-established benchmark datasets.
Keywords: Explainable Artificial Intelligence

An Integrated Fuzzy Analytic Network Process and Fuzzy Regression Method for Bitcoin Price Prediction - Amiri, Arman; Tavana, Madjid; Arman, Hosein (Internet of Things)
Keywords: Management of Technology and Innovation; Artificial Intelligence; Computer Science Applications; Hardware and Architecture; Engineering (miscellaneous); Information Systems; Computer Science (miscellaneous); Software

Mining legal arguments in court decisions - Habernal, Ivan; Faber, Daniel; Recchia, Nicola; Bretthauer, Sebastian; Gurevych, Iryna; Spiecker genannt Döhmann, Indra; Burchard, Christoph (Artificial Intelligence and Law)
Abstract Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field. However, there has been a major discrepancy between the way natural language processing (NLP) researchers model and annotate arguments in court decisions and the way legal experts understand and analyze legal argumentation. While computational approaches typically simplify arguments into generic premises and claims, arguments in legal research usually exhibit a rich typology that is important for gaining insights into the particular case and applications of law in general. We address this problem and make several substantial contributions to move the field forward. First, we design a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights (ECHR) that is deeply rooted in the theory and practice of legal argumentation research. Second, we compile and annotate a large corpus of 373 court decisions (2.3M tokens and 15k annotated argument spans). Finally, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain and provide a thorough expert-based evaluation. All datasets and source codes are available under open lincenses at https://github.com/trusthlt/mining-legal-arguments .
Keywords: Law; Artificial Intelligence

Incremental permutation feature importance (iPFI): towards online explanations on data streams - Fumagalli, Fabian; Muschalik, Maximilian; Hüllermeier, Eyke; Hammer, Barbara (Machine Learning)
Abstract Explainable artificial intelligence has mainly focused on static learning scenarios so far. We are interested in dynamic scenarios where data is sampled progressively, and learning is done in an incremental rather than a batch mode. We seek efficient incremental algorithms for computing feature importance (FI). Permutation feature importance (PFI) is a well-established model-agnostic measure to obtain global FI based on feature marginalization of absent features. We propose an efficient, model-agnostic algorithm called iPFI to estimate this measure incrementally and under dynamic modeling conditions including concept drift. We prove theoretical guarantees on the approximation quality in terms of expectation and variance. To validate our theoretical findings and the efficacy of our approaches in incremental scenarios dealing with streaming data rather than traditional batch settings, we conduct multiple experimental studies on benchmark data with and without concept drift.
Keywords: Artificial Intelligence; Software

“I do not know! but why?” — Local model-agnostic example-based explanations of reject - Artelt, André; Visser, Roel; Hammer, Barbara (Neurocomputing)
Keywords: Artificial Intelligence; Cognitive Neuroscience; Computer Science Applications

When do individuals choose care robots over a human caregiver? Insights from a laboratory experiment on choices under uncertainty - Hoppe, Julia Amelie; Tuisku, Outi; Johansson-Pajala, Rose-Marie; Pekkarinen, Satu; Hennala, Lea; Gustafsson, Christine; Melkas, Helinä; Thommes, Kirsten (Computers in Human Behavior Reports)
Keywords: Artificial Intelligence; Cognitive Neuroscience; Computer Science Applications; Human-Computer Interaction; Applied Psychology; Neuroscience (miscellaneous)

RISE: an open-source architecture for interdisciplinary and reproducible human–robot interaction research - Groß, André; Schütze, Christian; Brandt, Mara; Wrede, Britta; Richter, Birte (Frontiers in Robotics and AI)
In this article, we present RISE—a R obotics I ntegration and S cenario-Management E xtensible-Architecture—for designing human–robot dialogs and conducting Human–Robot Interaction (HRI) studies. In current HRI research, interdisciplinarity in the creation and implementation of interaction studies is becoming increasingly important. In addition, there is a lack of reproducibility of the research results. With the presented open-source architecture, we aim to address these two topics. Therefore, we discuss the advantages and disadvantages of various existing tools from different sub-fields within robotics. Requirements for an architecture can be derived from this overview of the literature, which 1) supports interdisciplinary research, 2) allows reproducibility of the research, and 3) is accessible to other researchers in the field of HRI. With our architecture, we tackle these requirements by providing a Graphical User Interface which explains the robot behavior and allows introspection into the current state of the dialog. Additionally, it offers controlling possibilities to easily conduct Wizard of Oz studies. To achieve transparency, the dialog is modeled explicitly, and the robot behavior can be configured. Furthermore, the modular architecture offers an interface for external features and sensors and is expandable to new robots and modalities.
Keywords: Artificial Intelligence; Computer Science Applications

AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization - Leffrang, Dirk (International Conference on Information Systems)
Many researchers and practitioners see artificial intelligence as a game changer compared to classical statistical models. However, some software providers engage in “AI washing”, relabeling solutions that use simple statistical models as AI systems. By contrast, research on algorithm aversion unsystematically varied the labels for advisors and treated labels such as "artificial intelligence" and "statistical model" synonymously. This study investigates the effect of individual labels on users' actual advice utilization behavior. Through two incentivized online within-subjects experiments on regression tasks, we find that labeling human advisors with labels that suggest higher expertise leads to an increase in advice-taking, even though the content of the advice remains the same. In contrast, our results do not suggest such an expert effect for advice-taking from algorithms, despite differences in self-reported perception. These findings challenge the effectiveness of framing intelligent systems as AI-based systems and have important implications for both research and practice.
Keywords: Artificial Intelligence; Algorithm Appreciation; Framing; Advice-taking; Expertise

Incremental permutation feature importance (iPFI): towards online explanations on data streams - Fumagalli, Fabian; Muschalik, Maximilian; Hüllermeier, Eyke; Hammer, Barbara (Machine Learning)
Abstract Explainable artificial intelligence has mainly focused on static learning scenarios so far. We are interested in dynamic scenarios where data is sampled progressively, and learning is done in an incremental rather than a batch mode. We seek efficient incremental algorithms for computing feature importance (FI). Permutation feature importance (PFI) is a well-established model-agnostic measure to obtain global FI based on feature marginalization of absent features. We propose an efficient, model-agnostic algorithm called iPFI to estimate this measure incrementally and under dynamic modeling conditions including concept drift. We prove theoretical guarantees on the approximation quality in terms of expectation and variance. To validate our theoretical findings and the efficacy of our approaches in incremental scenarios dealing with streaming data rather than traditional batch settings, we conduct multiple experimental studies on benchmark data with and without concept drift.
Keywords: Artificial Intelligence; Software

Developing a deep canonical correlation-based technique for seizure prediction - Vieluf, Solveig; Hasija, Tanuj; Kuschel, Maurice; Reinsberger, Claus; Loddenkemper, Tobias (Expert Systems with Applications)
Keywords: Artificial Intelligence; Computer Science Applications; General Engineering