Research Papers

As artificial intelligence (AI) systems become increasingly integrated into critical domains, ensuring their responsible design and continuous development is imperative. Effective AI quality management (QM) requires tools and methodologies that address the complexities of the AI lifecycle. In this paper, we propose an approach for AI lifecycle planning that bridges the gap between generic guidelines and use case-specific requirements (MQG4AI). Our work aims to contribute to the development of practical tools for implementing Responsible AI (RAI) by aligning lifecycle planning with technical, ethical and regulatory demands. Central to our approach is the introduction of a flexible and customizable Methodology based on Quality Gates, whose building blocks incorporate RAI knowledge through information linking along the AI lifecycle in a continuous manner, addressing AIs evolutionary character. For our present contribution, we put a particular emphasis on the Explanation stage during model development, and illustrate how to align a guideline to evaluate the quality of explanations with MQG4AI, contributing to overall Transparency.

Most commonly used non-linear machine learning methods are closed-box models, uninterpretable to humans. The field of explainable artificial intelligence (XAI) aims to develop tools to examine the inner workings of these closed boxes. An often-used model-agnostic approach to XAI involves using simple models as local approximations to produce so-called local explanations; examples of this approach include LIME, SHAP, and SLISEMAP. This paper shows how a large set of local explanations can be reduced to a small "proxy set" of simple models, which can act as a generative global explanation. This reduction procedure, ExplainReduce, can be formulated as an optimisation problem and approximated efficiently using greedy heuristics. We show that, for many problems, as few as five explanations can faithfully emulate the closed-box model and that our reduction procedure is competitive with other model aggregation methods.

My research explores integrating deep learning and logic programming to set the basis for a new generation of AI systems. By combining neural networks with Inductive Logic Programming (ILP), the goal is to construct systems that make accurate predictions and generate comprehensible rules to validate these predictions. Deep learning models process and analyze complex data, while ILP techniques derive logical rules to prove the network's conclusions. Explainable AI methods, like eXplainable Answer Set Programming (XASP), elucidate the reasoning behind these rules and decisions. The focus is on applying ILP frameworks, specifically ILASP and FastLAS, to enhance explainability in various domains. My test cases span weather prediction, the legal field, and image recognition. In weather forecasting, the system will predict events and provides explanations using FastLAS, with plans to integrate recurrent neural networks in the future. In the legal domain, the research focuses on interpreting vague decisions and assisting legal professionals by encoding Italian legal articles and learning reasoning patterns from Court of Cassation decisions using ILASP. For biological laboratories, we will collaborate with a research group to automate spermatozoa morphology classification for Bull Breeding Soundness Evaluation using YOLO networks and ILP to explain classification outcomes. This hybrid approach aims to bridge the gap between the high performance of deep learning models and the transparency of symbolic reasoning, advancing AI by providing interpretable and trustworthy applications.

The recent surge in advanced generative models, such as diffusion models and generative adversarial networks (GANs), has led to an alarming rise in AI-generated images across various domains on the web. While such technologies offer benefits such as democratizing artistic creation, they also pose challenges in misinformation, digital forgery, and authenticity verification. Additionally, the uncredited use of AI-generated images in media and marketing has sparked significant backlash from online communities. In response to this, we introduce DejAIvu, a Chrome Web extension that combines real-time AI-generated image detection with saliency-based explainability while users browse the web. Using an ONNX-optimized deep learning model, DejAIvu automatically analyzes images on websites such as Google Images, identifies AI-generated content using model inference, and overlays a saliency heatmap to highlight AI-related artifacts. Our approach integrates efficient in-browser inference, gradient-based saliency analysis, and a seamless user experience, ensuring that AI detection is both transparent and interpretable. We also evaluate DejAIvu across multiple pretrained architectures and benchmark datasets, demonstrating high accuracy and low latency, making it a practical and deployable tool for enhancing AI image accountability. The code for this system can be found at https://github.com/Noodulz/dejAIvu.

Around 50 percent of Irelands rural population relies on unregulated private wells vulnerable to agricultural runoff and untreated wastewater. High national rates of Shiga toxin-producing Escherichia coli (STEC) and other waterborne illnesses have been linked to well water exposure. Periodic well testing is essential for public health, yet the lack of government incentives places the financial burden on households. Understanding environmental, cognitive, and material factors influencing well-testing behavior is critical. This study employs Agent-Based Modeling (ABM) to simulate policy interventions based on national survey data. The ABM framework, designed for private well-testing behavior, integrates a Deep Q-network reinforcement learning model and Explainable AI (XAI) for decision-making insights. Key features were selected using Recursive Feature Elimination (RFE) with 10-fold cross-validation, while SHAP (Shapley Additive Explanations) provided further interpretability for policy recommendations. Fourteen policy scenarios were tested. The most effective, Free Well Testing plus Communication Campaign, increased participation to 435 out of 561 agents, from a baseline of approximately 5 percent, with rapid behavioral adaptation. Free Well Testing plus Regulation also performed well, with 433 out of 561 agents initiating well testing. Free testing alone raised participation to over 75 percent, with some agents testing multiple times annually. Scenarios with free well testing achieved faster learning efficiency, converging in 1000 episodes, while others took 2000 episodes, indicating slower adaptation. This research demonstrates the value of ABM and XAI in public health policy, providing a framework for evaluating behavioral interventions in environmental health.

The perspective of developing trustworthy AI for critical applications in science and engineering requires machine learning techniques that are capable of estimating their own uncertainty. In the context of regression, instead of estimating a conditional mean, this can be achieved by producing a predictive interval for the output, or to even learn a model of the conditional probability $p(y|x)$ of an output $y$ given input features $x$. While this can be done under parametric assumptions with, e.g. generalized linear model, these are typically too strong, and non-parametric models offer flexible alternatives. In particular, for scalar outputs, learning directly a model of the conditional cumulative distribution function of $y$ given $x$ can lead to more precise probabilistic estimates, and the use of proper scoring rules such as the weighted interval score (WIS) and the continuous ranked probability score (CRPS) lead to better coverage and calibration properties. This paper introduces novel algorithms for learning probabilistic regression trees for the WIS or CRPS loss functions. These algorithms are made computationally efficient thanks to an appropriate use of known data structures - namely min-max heaps, weight-balanced binary trees and Fenwick trees. Through numerical experiments, we demonstrate that the performance of our methods is competitive with alternative approaches. Additionally, our methods benefit from the inherent interpretability and explainability of trees. As a by-product, we show how our trees can be used in the context of conformal prediction and explain why they are particularly well-suited for achieving group-conditional coverage guarantees.

Data science has emerged as fourth paradigm of scientific exploration. However many machine learning models operate as black boxes offering limited insight into the reasoning behind their predictions. This lack of transparency is one of the drawbacks to generate new knowledge from data. Recently Kolmogorov-Arnold Network or KAN has been proposed as an alternative model which embeds explainable AI. This study demonstrates the potential of KAN for new scientific exploration. KAN along with five other popular supervised machine learning models are applied to the well-known problem of airfoil lift prediction in aerospace engineering. Standard data generated from an earlier study on 2900 different airfoils is used. KAN performed the best with an R2 score of 96.17 percent on the test data, surpassing both the baseline model and Multi Layer Perceptron. Explainability of KAN is shown by pruning and symbolizing the model resulting in an equation for coefficient of lift in terms of input variables. The explainable information retrieved from KAN model is found to be consistent with the known physics of lift generation by airfoil thus demonstrating its potential to aid in scientific exploration.

Developers play a central role in determining how machine learning systems are explained in practice, yet they are rarely trained to design explanations for non-technical audiences. Despite this, transparency and explainability requirements are increasingly codified in regulation and organizational policy. It remains unclear how such policies influence developer behavior or the quality of the explanations they produce. We report results from two controlled experiments with 194 participants, typical developers without specialized training in human-centered explainable AI, who designed explanations for an ML-powered diabetic retinopathy screening tool. In the first experiment, differences in policy purpose and level of detail had little effect: policy guidance was often ignored and explanation quality remained low. In the second experiment, stronger enforcement increased formal compliance, but explanations largely remained poorly suited to medical professionals and patients. We further observed that across both experiments, developers repeatedly produced explanations that were technically flawed or difficult to interpret, framed for developers rather than end users, reliant on medical jargon, or insufficiently grounded in the clinical decision context and workflow, with developer-centric framing being the most prevalent. These findings suggest that policy and policy enforcement alone are insufficient to produce meaningful end-user explanations and that responsible AI frameworks may overestimate developers' ability to translate high-level requirements into human-centered designs without additional training, tools, or implementation support.

Network slicing aims to enhance flexibility and efficiency in next-generation wireless networks by allocating the right resources to meet the diverse requirements of various applications. Managing these slices with machine learning (ML) algorithms has emerged as a promising approach however explainability has been a challenge. To this end, several Explainable Artificial Intelligence (XAI) frameworks have been proposed to address the opacity in decision-making in many ML methods. In this paper, we propose a Prioritized Value-Decomposition Network (PVDN) as an XAI-driven approach for resource allocation in a multi-agent network slicing system. The PVDN method decomposes the global value function into individual contributions and prioritizes slice outputs, providing an explanation of how resource allocation decisions impact system performance. By incorporating XAI, PVDN offers valuable insights into the decision-making process, enabling network operators to better understand, trust, and optimize slice management strategies. Through simulations, we demonstrate the effectiveness of the PVDN approach with improving the throughput by 67% and 16%, while reducing latency by 35% and 22%, compared to independent and VDN-based resource allocation methods.

With the rapid growth of generative AI in numerous applications, explainable AI (XAI) plays a crucial role in ensuring the responsible development and deployment of generative AI technologies. XAI has undergone notable advancements and widespread adoption in recent years, reflecting a concerted push to enhance the transparency, interpretability, and credibility of AI systems. Recent research emphasizes that a proficient XAI method should adhere to a set of criteria, primarily focusing on two key areas. Firstly, it should ensure the quality and fluidity of explanations, encompassing aspects like faithfulness, plausibility, completeness, and tailoring to individual needs. Secondly, the design principle of the XAI system or mechanism should cover the following factors such as reliability, resilience, the verifiability of its outputs, and the transparency of its algorithm. However, research in XAI for generative models remains relatively scarce, with little exploration into how such methods can effectively meet these criteria in that domain. In this work, we propose PXGen, a post-hoc explainable method for generative models. Given a model that needs to be explained, PXGen prepares two materials for the explanation, the Anchor set and intrinsic & extrinsic criteria. Those materials are customizable by users according to their purpose and requirements. Via the calculation of each criterion, each anchor has a set of feature values and PXGen provides examplebased explanation methods according to the feature values among all the anchors and illustrated and visualized to the users via tractable algorithms such as k-dispersion or k-center.

In epidemiology, traditional statistical methods such as logistic regression, linear regression, and other parametric models are commonly employed to investigate associations between predictors and health outcomes. However, non-parametric machine learning techniques, such as deep neural networks (DNNs), coupled with explainable AI (XAI) tools, offer new opportunities for this task. Despite their potential, these methods face challenges due to the limited availability of high-quality, high-quantity data in this field. To address these challenges, we introduce SEANN, a novel approach for informed DNNs that leverages a prevalent form of domain-specific knowledge: Pooled Effect Sizes (PES). PESs are commonly found in published Meta-Analysis studies, in different forms, and represent a quantitative form of a scientific consensus. By direct integration within the learning procedure using a custom loss, we experimentally demonstrate significant improvements in the generalizability of predictive performances and the scientific plausibility of extracted relationships compared to a domain-knowledge agnostic neural network in a scarce and noisy data setting.

In this paper we describe the development and evaluation of AITK, the Artificial Intelligence Toolkit. This open-source project contains both Python libraries and computational essays (Jupyter notebooks) that together are designed to allow a diverse audience with little or no background in AI to interact with a variety of AI tools, exploring in more depth how they function, visualizing their outcomes, and gaining a better understanding of their ethical implications. These notebooks have been piloted at multiple institutions in a variety of humanities courses centered on the theme of responsible AI. In addition, we conducted usability testing of AITK. Our pilot studies and usability testing results indicate that AITK is easy to navigate and effective at helping users gain a better understanding of AI. Our goal, in this time of rapid innovations in AI, is for AITK to provide an accessible resource for faculty from any discipline looking to incorporate AI topics into their courses and for anyone eager to learn more about AI on their own.