Recent advances in deep learning have improved multivariate time series (MTS) classification and regression by capturing complex patterns, but their lack of transparency hinders decision-making. Explainable AI (XAI) methods offer partial insights, yet often fall short of conveying the full decision space. Counterfactual Explanations (CE) provide a promising alternative, but current approaches typically prioritize either accuracy, proximity or sparsity -- rarely all -- limiting their practical value. To address this, we propose CONFETTI, a novel multi-objective CE method for MTS. CONFETTI identifies key MTS subsequences, locates a counterfactual target, and optimally modifies the time series to balance prediction confidence, proximity and sparsity. This method provides actionable insights with minimal changes, improving interpretability, and decision support. CONFETTI is evaluated on seven MTS datasets from the UEA archive, demonstrating its effectiveness in various domains. CONFETTI consistently outperforms state-of-the-art CE methods in its optimization objectives, and in six other metrics from the literature, achieving ≥ 10% higher confidence while improving sparsity in ≥ 40%.

As data is a central component of many modern systems, the cause of a system malfunction may reside in the data, and, specifically, particular properties of data. E.g., a health-monitoring system that is designed under the assumption that weight is reported in lbs will malfunction when encountering weight reported in kilograms. Like software debugging, which aims to find bugs in the source code or runtime conditions, our goal is to debug data to identify potential sources of disconnect between the assumptions about some data and systems that operate on that data. We propose DataPrism, a framework to identify data properties (profiles) that are the root causes of performance degradation or failure of a data-driven system. Such identification is necessary to repair data and resolve the disconnect between data and systems. Our technique is based on causal reasoning through interventions: when a system malfunctions for a dataset, DataPrism alters the data profiles and observes changes in the system's behavior due to the alteration. Unlike statistical observational analysis that reports mere correlations, DataPrism reports causally verified root causes -- in terms of data profiles -- of the system malfunction. We empirically evaluate DataPrism on seven real-world and several synthetic data-driven systems that fail on certain datasets due to a diverse set of reasons. In all cases, DataPrism identifies the root causes precisely while requiring orders of magnitude fewer interventions than prior techniques.

Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous outputs, the pipeline may fail to execute or produce incorrect results. Inferring the root cause(s) of such failures is challenging, usually requiring time and much human thought, while still being error-prone. We propose a new approach that makes use of iteration and provenance to automatically infer the root causes and derive succinct explanations of failures. Through a detailed experimental evaluation, we assess the cost, precision, and recall of our approach compared to the state of the art. Our experimental data and processing software is available for use, reproducibility, and enhancement.

Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous outputs, the pipeline may fail to execute or produce incorrect results. Inferring the root cause(s) of such failures is challenging, usually requiring time and much human thought, while still being error-prone. We recently proposed a new approach that makes provenance to automatically and iteratively infer root causes and derive succinct explanations of failures; such an approach was implemented in our prototype, BugDoc. In this demonstration, we will illustrate BugDoc's capabilities to debug pipelines using few configuration instances.