Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results

Classifying Ellipsis in Dialogue: A Machine Learning Approach

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On Computing Probabilistic Abductive Explanations

The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches based on saliency maps. One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness. Unfortunately, intrinsic interpretability can display unwieldy explanation redundancy. Formal explainability represents the alternative to these non-rigorous approaches, with one example being PI-explanations. Unfortunately, PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size. Recently, it has been observed that the (absolute) rigor of PI-explanations can be traded off for a smaller explanation size, by computing the so-called relevant sets. Given some positive {\delta}, a set S of features is {\delta}-relevant if, when the features in S are fixed, the probability of getting the target class exceeds {\delta}. However, even for very simple classifiers, the complexity of computing relevant sets of features is prohibitive, with the decision problem being NPPP-complete for circuit-based classifiers. In contrast with earlier negative results, this paper investigates practical approaches for computing relevant sets for a number of widely used classifiers that include Decision Trees (DTs), Naive Bayes Classifiers (NBCs), and several families of classifiers obtained from propositional languages. Moreover, the paper shows that, in practice, and for these families of classifiers, relevant sets are easy to compute. Furthermore, the experiments confirm that succinct sets of relevant features can be obtained for the families of classifiers considered.Comment: arXiv admin note: text overlap with arXiv:2207.04748, arXiv:2205.0956

Disproving XAI Myths with Formal Methods -- Initial Results

The advances in Machine Learning (ML) in recent years have been both impressive and far-reaching. However, the deployment of ML models is still impaired by a lack of trust in how the best-performing ML models make predictions. The issue of lack of trust is even more acute in the uses of ML models in high-risk or safety-critical domains. eXplainable artificial intelligence (XAI) is at the core of ongoing efforts for delivering trustworthy AI. Unfortunately, XAI is riddled with critical misconceptions, that foster distrust instead of building trust. This paper details some of the most visible misconceptions in XAI, and shows how formal methods have been used, both to disprove those misconceptions, but also to devise practically effective alternatives

INVESTIGATING THE IMPACT OF ONLINE HUMAN COLLABORATION IN EXPLANATION OF AI SYSTEMS

An important subdomain in research on Human-Artificial Intelligence interaction is Explainable AI (XAI). XAI aims to improve human understanding and trust in machine intelligence and automation by providing users with visualizations and other information explaining the AI’s decisions, actions, or plans and thereby to establish justified trust and reliance. XAI systems have primarily used algorithmic approaches designed to generate explanations automatically that help understanding underlying information about decisions and establish justified trust and reliance, but an alternate that may augment these systems is to take advantage of the fact that user understanding of AI systems often develops through self-explanation (Mueller et al., 2021). Users attempt to piece together different sources of information and develop a clearer understanding, but these self-explanations are often lost if not shared with others. This thesis research demonstrated how this ‘Self-Explanation’ could be shared collaboratively via a system that is called collaborative XAI (CXAI). It is akin to a Social Q&A platform (Oh, 2018) such as StackExchange. A web-based system was built and evaluated formatively and via user studies. Formative evaluation will show how explanations in an XAI system, especially collaborative explanations, can be assessed based on ‘goodness criteria’ (Mueller et al., 2019). This thesis also investigated how the users performed with the explanations from this type of XAI system. Lastly, the research investigated whether the users of CXAI system are satisfied with the human-generated explanations generated in the system and check if the users can trust this type of explanation

SLISEMAP: Explainable Dimensionality Reduction

Existing explanation methods for black-box supervised learning models generally work by building local models that explain the models behaviour for a particular data item. It is possible to make global explanations, but the explanations may have low fidelity for complex models. Most of the prior work on explainable models has been focused on classification problems, with less attention on regression. We propose a new manifold visualization method, SLISEMAP, that at the same time finds local explanations for all of the data items and builds a two-dimensional visualization of model space such that the data items explained by the same model are projected nearby. We provide an open source implementation of our methods, implemented by using GPU-optimized PyTorch library. SLISEMAP works both on classification and regression models. We compare SLISEMAP to most popular dimensionality reduction methods and some local explanation methods. We provide mathematical derivation of our problem and show that SLISEMAP provides fast and stable visualizations that can be used to explain and understand black box regression and classification models