Dynamic feature selection (DFS) offers a compelling alternative to traditional, static feature selection by adapting the selected features to each individual sample. This provides insights into the decision-making process for each case, which makes DFS especially significant in settings where decision transparency is key, i.e., clinical decisions. However, existing DFS methods use opaque models, which hinder their applicability in real-life scenarios. DFS also introduces new own sources of uncertainty compared to the static setting, which is also not considered in the existing literature. In this paper, we formalize the additional sources of uncertainty in DFS, and give formulas to estimate them. We also propose novel approach by leveraging a rule-based system as a base classifier for the DFS process, which enhances decision interpretability compared to neural estimators. Finally, we demonstrate the competitive performance of our rule-based DFS approach against established and state-of-the-art greedy and reinforcement learning methods, which are mostly considered opaque, compared to our explainable rule-based system.

Neural Information Processing Systems http://nips.cc/

We present the Multi-value Rule Set (MRS) for interpretable classification with feature efficient presentations. Compared to rule sets built from single-value rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical single-value rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating an MRS model and develop an efficient inference method for learning a maximum a posteriori, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency. Experiments on synthetic and real-world data demonstrate that MRS models have significantly smaller complexity and fewer features than baseline models while being competitive in predictive accuracy. Human evaluations show that MRS is easier to understand and use compared to other rule-based models.

A task of interest in machine learning (ML) is that of ascribing explanations to the predictions made by ML models. Furthermore, in domains deemed high risk, the rigor of explanations is paramount. Indeed, incorrect explanations can and will mislead human decision makers. As a result, and even if interpretability is acknowledged as an elusive concept, so-called interpretable models are employed ubiquitously in high-risk uses of ML and data mining (DM). This is the case for rule-based ML models, which encompass decision trees, diagrams, sets and lists. This paper relates explanations with well-known undesired facets of rule-based ML models, which include negative overlap and several forms of redundancy. The paper develops algorithms for the analysis of these undesired facets of rule-based systems, and concludes that well-known and widely used tools for learning rule-based ML models will induce rule sets that exhibit one or more negative facets.

The unchecked spread of digital information, combined with increasing political polarization and the tendency of individuals to isolate themselves from opposing political viewpoints, has driven researchers to develop systems for automatically detecting political bias in media. This trend has been further fueled by discussions on social media. We explore methods for categorizing bias in US news articles, comparing rule-based and deep learning approaches. The study highlights the sensitivity of modern self-learning systems to unconstrained data ingestion, while reconsidering the strengths of traditional rule-based systems. Applying both models to left-leaning (CNN) and right-leaning (FOX) news articles, we assess their effectiveness on data beyond the original training and test sets.This analysis highlights each model's accuracy, offers a framework for exploring deep-learning explainability, and sheds light on political bias in US news media. We contrast the opaque architecture of a deep learning model with the transparency of a linguistically informed rule-based model, showing that the rule-based model performs consistently across different data conditions and offers greater transparency, whereas the deep learning model is dependent on the training set and struggles with unseen data.

Human trust is critical for trustworthy AI adoption. Trust is commonly understood as an attitude, but we cannot accurately measure this, nor manage it. We conflate trust in the overall system, ML, and ML's component parts; so most users do not understand the leap of faith they take when they trust ML. Current efforts to build trust explain ML's process, which can be hard for non-ML experts to comprehend because it is complex, and explanations are unrelated to their own (unarticulated) mental models. We propose an innovative way of directly building intrinsic trust in ML, by discerning and measuring the Leap of Faith (LoF) taken when a user trusts ML. Our LoF matrix identifies where an ML model aligns to a user's own mental model. This match is rigorously yet practically identified by feeding the user's data and objective function both into an ML model and an expert-validated rules-based AI model, a verified point of reference that can be tested a priori against a user's own mental model. The LoF matrix visually contrasts the models' outputs, so the remaining ML-reasoning leap of faith can be discerned. Our proposed trust metrics measure for the first time whether users demonstrate trust through their actions, and we link deserved trust to outcomes. Our contribution is significant because it enables empirical assessment and management of ML trust drivers, to support trustworthy ML adoption. Our approach is illustrated with a long-term high-stakes field study: a 3-month pilot of a sleep-improvement system with embedded AI.

Rule-based models offer a human-understandable representation, i.e. they are interpretable. For this reason, they are used to explain the decisions of non-interpretable complex models, referred to as black box models. The generation of such explanations involves the approximation of a black box model by a rule-based model. To date, however, it has not been investigated whether the rule-based model makes decisions in the same way as the black box model it approximates. Decision making in the same way is understood in this work as the consistency of decisions and the consistency of the most important attributes used for decision making. This study proposes a novel approach ensuring that the rule-based surrogate model mimics the performance of the black box model. The proposed solution performs an explanation fusion involving rule generation and taking into account the feature importance determined by the selected XAI methods for the black box model being explained. The result of the method can be both global and local rule-based explanations. The quality of the proposed solution was verified by extensive analysis on 30 tabular benchmark datasets representing classification problems. Evaluation included comparison with the reference method and an illustrative case study. In addition, the paper discusses the possible pathways for the application of the rule-based approach in XAI and how rule-based explanations, including the proposed method, meet the user perspective and requirements for both content and presentation. The software created and a detailed report containing the full experimental results are available on the GitHub repository (https://github.com/ruleminer/FI-rules4XAI ).

This study explores the formality style transfer in Persian, particularly relevant in the face of the increasing prevalence of informal language on digital platforms, which poses challenges for existing Natural Language Processing (NLP) tools. The aim is to transform informal text into formal while retaining the original meaning, addressing both lexical and syntactic differences. We introduce a novel model, Fa-BERT2BERT, based on the Fa-BERT architecture, incorporating consistency learning and gradient-based dynamic weighting. This approach improves the model's understanding of syntactic variations, balancing loss components effectively during training. Our evaluation of Fa-BERT2BERT against existing methods employs new metrics designed to accurately measure syntactic and stylistic changes. Results demonstrate our model's superior performance over traditional techniques across various metrics, including BLEU, BERT score, Rouge-l, and proposed metrics underscoring its ability to adeptly navigate the complexities of Persian language style transfer. This study significantly contributes to Persian language processing by enhancing the accuracy and functionality of NLP models and thereby supports the development of more efficient and reliable NLP applications, capable of handling language style transformation effectively, thereby streamlining content moderation, enhancing data mining results, and facilitating cross-cultural communication.