Professional ethics committees currently lack structured tools to identify relevant ethical concepts from complex narratives and compare them against prior decisions. ProEthica analyzes professional ethical scenarios against established codes and precedent cases. The system uses large language models (LLMs), leveraging their natural language processing capabilities to extract nine types of components (Roles, Principles, Obligations, States, Resources, Actions, Events, Capabilities, and Constraints) from case text and scenario descriptions. Domain-specific ontologies provide precise definitions that constrain LLM output to match formal concept specifications, ensuring consistency across extraction and validation. Case-based reasoning identifies precedent cases and analogous situations relevant to the scenario under analysis. The current implementation processes engineering ethics cases with complete provenance tracking and ontology-driven validation. The framework supports extension to other professional domains with established codes and precedent systems.

Pretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on text classification tasks with their powerful word embeddings, but their black-box nature, which leads to a lack of interpretability, has been a major concern. In this work, we introduce GAProtoNet, a novel white-box Multi-head Graph Attention-based Prototypical Network designed to explain the decisions of text classification models built with LM encoders. In our approach, the input vector and prototypes are regarded as nodes within a graph, and we utilize multi-head graph attention to selectively construct edges between the input node and prototype nodes to learn an interpretable prototypical representation. During inference, the model makes decisions based on a linear combination of activated prototypes weighted by the attention score assigned for each prototype, allowing its choices to be transparently explained by the attention weights and the prototypes. Experiments on multiple public datasets show our approach achieves superior results without sacrificing the accuracy of the original black-box LMs. We also compare with four alternative prototypical network variations and our approach achieves the best accuracy and F1 among all. Our case study and visualization of prototype clusters also demonstrate the efficiency in explaining the decisions of black-box models built with LMs.

• A privacy-preserving synthetic ground-truth data generation method is proposed for additive feature attribution evaluation. • Additive Case-based Reasoning (AddCBR) is introduced as a model-aligned baseline for evaluating additive feature attribution. • Coefficient of Quartile Variation (CQV) is applied for the first time to assess the quality of feature attribution. • A complete evaluation pipeline for additive feature attribution is developed to benchmark regression explainability. • The novelty of the work is demonstrated for a real-world regression problem of flight take-off delay prediction. Explainable artificial intelligence (XAI) methods produce information outputs based on a target artificial intelligence model to be explained. The most popular information output is produced by XAI methods of the category feature attribution, which produce the relative contribution of each input feature in a local instance. These relative contributions indicate how important each input feature is in a decision; this type of information is expected to provide explanatory value to users. In real-world regression tasks, feature attribution methods are crucial for comprehending model predictions. However, robust evaluation of such methods remains challenging due to a lack of ground-truth data and widely accepted evaluation metrics, such as accuracy for classification or mean absolute error for regression. This paper proposes a novel approach for generating synthetic, privacy-preserving ground-truth datasets for regression problems that retain original feature behaviour, enabling rigorous feature attribution evaluation without compromising sensitive information. We introduce additive case-based reasoning (AddCBR) as a model-aligned and interpretable baseline to benchmark additive feature attribution methods. This work also demonstrates the first use of the coefficient of quartile variation (CQV) as a statistical measure to quantify the consistency and stability of feature attribution methods. Altogether, these contributions form a comprehensive evaluation methodology for objectively assessing and comparing feature attribution methods in regression models. By providing a controlled evaluation pipeline with reliable baselines and metrics, this work addresses the current lack of consensus and benchmarking in XAI evaluation for regression models.

Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside. Interpreting these images requires a high level of expertise, which may not be available during emergencies. In this paper, we support POCUS by developing classifiers that can aid medical professionals by diagnosing whether or not a patient has pneumothorax. We decomposed the task into multiple steps, using YOLOv4 to extract relevant regions of the video and a 3D sparse coding model to represent video features. Given the difficulty in acquiring positive training videos, we trained a small-data classifier with a maximum of 15 positive and 32 negative examples. To counteract this limitation, we leveraged subject matter expert (SME) knowledge to limit the hypothesis space, thus reducing the cost of data collection. We present results using two lung ultrasound datasets and demonstrate that our model is capable of achieving performance on par with SMEs in pneumothorax identification. We then developed an iOS application that runs our full system in less than 4 seconds on an iPad Pro, and less than 8 seconds on an iPhone 13 Pro, labeling key regions in the lung sonogram to provide interpretable diagnoses.

The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery and that existing tools for causal discovery can be used to dramatically improve the performance of SINDy as tool for robust sparse modeling and system identification. We then demonstrate empirically that augmenting the SINDy algorithm with tools from causal discovery can provides engineers with a tool for learning causally robust governing equations.

Microtext is sparse and informal content typical in social media that is being widely used to study various facets of today's society. This paper proposes the use of supplemental context to counteract the limitations imposed by the sparsity and the informality of microtext on the performance of word sense disambiguation (WSD). WSD relies on the senses of words around an ambiguous word to disambiguate it. Because microtext is sparse and informal, it lacks exploitable context. This creates a major challenge for using this kind of data and consequently to the analyses of studies that rely on microtext sources. This paper proposes, demonstrates, and describes some of the challenges in selecting and utilizing supplemental context. We present studies using twitter data. We validate our studies with around 10,000 tweets using a gold standard proxy we call the blue standard. The method relies on the notion of one sense per collocation, and we implement it by identifying collocated word sequences that are strongly indicative of the target word's sense.

We introduce LET (Lesson Elicitation Tool), which uses domain and linguistic knowledge to guide users during their submission of lessons learned. LET can detect a user's need for instructions and disambiguates expressions while collecting taxonomic domain knowledge.