Research Papers

As the field of artificial intelligence (AI) and machine learning (ML) continues to prioritize fairness and the concern for historically marginalized communities, the importance of intersectionality in AI research has gained significant recognition. However, few studies provide practical guidance on how researchers can effectively incorporate intersectionality into critical praxis. In response, this paper presents a comprehensive framework grounded in critical reflexivity as intersectional praxis. Operationalizing intersectionality within the AI/DS (Artificial Intelligence/Data Science) pipeline, Quantitative Intersectional Data (QUINTA) is introduced as a methodological paradigm that challenges conventional and superficial research habits, particularly in data-centric processes, to identify and mitigate negative impacts such as the inadvertent marginalization caused by these practices. The framework centers researcher reflexivity to call attention to the AI researchers' power in creating and analyzing AI/DS artifacts through data-centric approaches. To illustrate the effectiveness of QUINTA, we provide a reflexive AI/DS researcher demonstration utilizing the \#metoo movement as a case study. Note: This paper was accepted as a poster presentation at Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) Conference in 2023.

This study deploys methods in explainable artificial intelligence (XAI), e.g. decision trees and Pearson's correlation coefficient (PCC), to investigate electricity usage in multiple locales. It addresses the vital issue of energy burden, i.e. total amount spent on energy divided by median household income. Socio-demographic data is analyzed with energy features, especially using decision trees and PCC, providing explainable predictors on factors affecting energy burden. Based on the results of the analysis, a pilot energy equity web portal is designed along with a novel energy burden calculator. Leveraging XAI, this portal (with its calculator) serves as a prototype information system that can offer tailored actionable advice to multiple energy stakeholders. The ultimate goal of this study is to promote greater energy equity through the adaptation of XAI methods for energy-related analysis with suitable recommendations.

Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide, with gastrointestinal (GI) polyps serving as critical precursors according to the World Health Organization (WHO). Early and accurate segmentation of polyps during colonoscopy is essential for reducing CRC progression, yet manual delineation is labor-intensive and prone to observer variability. Deep learning methods have demonstrated strong potential for automated polyp analysis, but their limited interpretability remains a barrier to clinical adoption. In this study, we present PolypSeg-GradCAM, an explainable deep learning framework that integrates a U-Net architecture with a pre-trained ResNet-34 backbone and Gradient-weighted Class Activation Mapping (Grad-CAM) for transparent polyp segmentation. To ensure rigorous benchmarking, the model was trained and evaluated using 5-Fold Cross-Validation on the Kvasir-SEG dataset of 1,000 annotated endoscopic images. Experimental results show a mean Dice coefficient of 0.8902 +/- 0.0125, a mean Intersection-over-Union (IoU) of 0.8023, and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.9722. Advanced quantitative analysis using an optimal threshold yielded a Sensitivity of 0.9058 and Precision of 0.9083. Additionally, Grad-CAM visualizations confirmed that predictions were guided by clinically relevant regions, offering insight into the model's decision-making process. This study demonstrates that integrating segmentation accuracy with interpretability can support the development of trustworthy AI-assisted colonoscopy tools.

The upsurge of policies and guidelines that aim to ensure Artificial Intelligence (AI) systems are safe and trustworthy has led to a fragmented landscape of AI governance. The European Union (EU) is a key actor in the development of such policies and guidelines. Its High-Level Expert Group (HLEG) issued an influential set of guidelines for trustworthy AI, followed in 2024 by the adoption of the EU AI Act. While the EU policies and guidelines are expected to be aligned, they may differ in their scope, areas of emphasis, degrees of normativity, and priorities in relation to AI. To gain a broad understanding of AI governance from the EU perspective, we leverage qualitative thematic analysis approaches to uncover prevalent themes in key EU documents, including the AI Act and the HLEG Ethics Guidelines. We further employ quantitative topic modelling approaches, specifically through the use of the BERTopic model, to enhance the results and increase the document sample to include EU AI policy documents published post-2018. We present a novel perspective on EU policies, tracking the evolution of its approach to addressing AI governance.

A key concern for AI safety remains understudied in the machine learning (ML) literature: how can we ensure users of ML models do not leverage predictions on incorrect personal data to harm others? This is particularly pertinent given the rise of open-weight models, where simply masking model outputs does not suffice to prevent adversaries from recovering harmful predictions. To address this threat, which we call *test-time privacy*, we induce maximal uncertainty on protected instances while preserving accuracy on all other instances. Our proposed algorithm uses a Pareto optimal objective that explicitly balances test-time privacy against utility. We also provide a certifiable approximation algorithm which achieves $(\varepsilon, δ)$ guarantees without convexity assumptions. We then prove a tight bound that characterizes the privacy-utility tradeoff that our algorithms incur. Empirically, our method obtains at least $>3\times$ stronger uncertainty than pretraining with marginal drops in accuracy on various image recognition benchmarks. Altogether, this framework provides a tool to guarantee additional protection to end users.

Responsible AI (RAI) content work, such as annotation, moderation, or red teaming for AI safety, often exposes crowd workers to potentially harmful content. While prior work has underscored the importance of communicating well-being risk to employed content moderators, designing effective disclosure mechanisms for crowd workers while balancing worker protection with the needs of task designers and platforms remains largely unexamined. To address this gap, we conducted individual co-design sessions with 15 task designers, 11 crowdworkers, and 3 platform representatives. We investigated task designer preferences for support in disclosing tasks, worker preferences for receiving risk disclosure warnings, and how platform representatives envision their role in shaping risk disclosure practices. We identify design tensions and map the sociotechnical tradeoffs that shape disclosure practices. We contribute design recommendations and feature concepts for risk disclosure mechanisms in the context of RAI content work.

Roundabouts reduce severe crashes, yet risk patterns vary by conditions. This study analyzes 2017-2021 Ohio roundabout crashes using a two-step, explainable workflow. Cluster Correspondence Analysis (CCA) identifies co-occurring factors and yields four crash patterns. A tree-based severity model is then interpreted with SHAP to quantify drivers of injury within and across patterns. Results show higher severity when darkness, wet surfaces, and higher posted speeds coincide with fixed-object or angle events, and lower severity in clear, low-speed settings. Pattern-specific explanations highlight mechanisms at entries (fail-to-yield, gap acceptance), within multi-lane circulation (improper maneuvers), and during slow-downs (rear-end). The workflow links pattern discovery with case-level explanations, supporting site screening, countermeasure selection, and audit-ready reporting. The contribution to Information Systems is a practical template for usable XAI in public safety analytics.

The impressive capabilities of deep learning models are often counterbalanced by their inherent opacity, commonly termed the "black box" problem, which impedes their widespread acceptance in high-trust domains. In response, the intersecting disciplines of interpretability and explainability, collectively falling under the Explainable AI (XAI) umbrella, have become focal points of research. Although these terms are frequently used as synonyms, they carry distinct conceptual weights. This document offers a comparative exploration of interpretability and explainability within the deep learning paradigm, carefully outlining their respective definitions, objectives, prevalent methodologies, and inherent difficulties. Through illustrative examinations of the MNIST digit classification task and IMDB sentiment analysis, we substantiate a key argument: interpretability generally pertains to a model's inherent capacity for human comprehension of its operational mechanisms (global understanding), whereas explainability is more commonly associated with post-hoc techniques designed to illuminate the basis for a model's individual predictions or behaviors (local explanations). For example, feature attribution methods can reveal why a specific MNIST image is recognized as a '7', and word-level importance can clarify an IMDB sentiment outcome. However, these local insights do not render the complex underlying model globally transparent. A clear grasp of this differentiation, as demonstrated by these standard datasets, is vital for fostering dependable and sound artificial intelligence.

The proliferation of clickbait headlines poses significant challenges to the credibility of information and user trust in digital media. While recent advances in machine learning have improved the detection of manipulative content, the lack of explainability limits their practical adoption. This paper presents a model for explainable clickbait detection that not only identifies clickbait titles but also attributes them to specific linguistic manipulation strategies. We introduce a synthetic dataset generated by systematically augmenting real news headlines using a predefined catalogue of clickbait strategies. This dataset enables controlled experimentation and detailed analysis of model behaviour. We present a two-stage framework for automatic clickbait analysis comprising detection and tactic attribution. In the first stage, we compare a fine-tuned BERT classifier with large language models (LLMs), specifically GPT-4.0 and Gemini 2.4 Flash, under both zero-shot prompting and few-shot prompting enriched with illustrative clickbait headlines and their associated persuasive tactics. In the second stage, a dedicated BERT-based classifier predicts the specific clickbait strategies present in each headline. This work advances the development of transparent and trustworthy AI systems for combating manipulative media content. We share the dataset with the research community at https://github.com/LLM-HITCS25S/ClickbaitTacticsDetection

Machine Unlearning (MU) has emerged as a promising approach to addressing persistent challenges in Machine Learning (ML) systems. By enabling the selective removal of learned data, MU introduces protective, corrective, and adaptive capabilities that are central to advancing Responsible and Adaptive AI. However, despite its growing prominence in other domains, MU remains underexplored within education, a sector uniquely characterized by sensitive learner data, dynamic environments, and the high-stakes implications of algorithmic decision-making. This paper examines the potential of MU as both a mechanism for operationalizing Responsible AI principles and a foundation for Adaptive AI in ML-driven educational systems. Drawing on a structured review of 42 peer-reviewed studies, the paper analyzes key MU mechanisms and technical variants, and how they contribute to the practical realization of Responsible and Adaptive AI. Four core intervention domains where MU demonstrates significant promise are identified: privacy protection, resilience to adversarial or corrupted data, fairness through bias mitigation, and adaptability to evolving contexts. Furthermore, MU interventions are mapped to the technical, ethical, and pedagogical challenges inherent in educational AI. This mapping illustrates the role of MU as a strategic mechanism for enhancing compliance, reinforcing ethical safeguards, and supporting adaptability by ensuring that models remain flexible, maintainable, and contextually relevant over time. As a conceptual contribution, the paper introduces MU4RAAI, a reference architecture integrating MU within Responsible and Adaptive AI frameworks for educational contexts. MU is thus positioned not merely as a data deletion process but as a transformative approach for ensuring that educational AI systems remain ethical, adaptive, and trustworthy.

Most explainable AI (XAI) frameworks are limited in their expressiveness, summarizing complex feature effects as single scalar values φ_i. This approach answers "what" features are important but fails to reveal "how" they interact. Furthermore, methods that attempt to capture interactions, like those based on Shapley values, often face an exponential computational cost. We present STRIDE, a scalable framework that addresses both limitations by reframing explanation as a subset-enumeration-free, orthogonal "functional decomposition" in a Reproducing Kernel Hilbert Space (RKHS). In the tabular setups we study, STRIDE analytically computes functional components f_S(x_S) via a recursive kernel-centering procedure. The approach is model-agnostic and theoretically grounded with results on orthogonality and L^2 convergence. In tabular benchmarks (10 datasets, median over 10 seeds), STRIDE attains a 3.0 times median speedup over TreeSHAP and a mean R^2=0.93 for reconstruction. We also introduce "component surgery", a diagnostic that isolates a learned interaction and quantifies its contribution; on California Housing, removing a single interaction reduces test R^2 from 0.019 to 0.027.

Spin coating polymer thin films to achieve specific mechanical properties is inherently a multi-objective optimization problem. We present a framework that integrates an active Pareto front learning algorithm (PyePAL) with visualization and explainable AI techniques to optimize processing parameters. PyePAL uses Gaussian process models to predict objective values (hardness and elasticity) from the design variables (spin speed, dilution, and polymer mixture), guiding the adaptive selection of samples toward promising regions of the design space. To enable interpretable insights into the high-dimensional design space, we utilize UMAP (Uniform Manifold Approximation and Projection) for two-dimensional visualization of the Pareto front exploration. Additionally, we incorporate fuzzy linguistic summaries, which translate the learned relationships between process parameters and performance objectives into linguistic statements, thus enhancing the explainability and understanding of the optimization results. Experimental results demonstrate that our method efficiently identifies promising polymer designs, while the visual and linguistic explanations facilitate expert-driven analysis and knowledge discovery.