Research Papers

Mainstream AI ethics, with its reliance on top-down, principle-driven frameworks, fails to account for the situated realities of diverse communities affected by AI (Artificial Intelligence). Critics have argued that AI ethics frequently serves corporate interests through practices of 'ethics washing', operating more as a tool for public relations than as a means of preventing harm or advancing the common good. As a result, growing scepticism among critical scholars has cast the field as complicit in sustaining harmful systems rather than challenging or transforming them. In response, this paper adopts a Science and Technology Studies (STS) perspective to critically interrogate the field of AI ethics. It hence applies the same analytic tools STS has long directed at disciplines such as biology, medicine, and statistics to ethics. This perspective reveals a core tension between vertical (top-down, principle-based) and horizontal (risk-mitigating, implementation-oriented) approaches to ethics. By tracing how these models have shaped the discourse, we show how both fall short in addressing the complexities of AI as a socio-technical assemblage, embedded in practice and entangled with power. To move beyond these limitations, we propose a threefold reorientation of AI ethics. First, we call for a shift in foundations: from top-down abstraction to empirical grounding. Second, we advocate for pluralisation: moving beyond Western-centric frameworks toward a multiplicity of onto-epistemic perspectives. Finally, we outline strategies for reconfiguring AI ethics as a transformative force, moving from narrow paradigms of risk mitigation toward co-creating technologies of hope.

Assessing fairness in artificial intelligence (AI) typically involves AI experts who select protected features, fairness metrics, and set fairness thresholds to assess outcome fairness. However, little is known about how stakeholders, particularly those affected by AI outcomes but lacking AI expertise, assess fairness. To address this gap, we conducted a qualitative study with 26 stakeholders without AI expertise, representing potential decision subjects in a credit rating scenario, to examine how they assess fairness when placed in the role of deciding on features with priority, metrics, and thresholds. We reveal that stakeholders' fairness decisions are more complex than typical AI expert practices: they considered features far beyond legally protected features, tailored metrics for specific contexts, set diverse yet stricter fairness thresholds, and even preferred designing customized fairness. Our results extend the understanding of how stakeholders can meaningfully contribute to AI fairness governance and mitigation, underscoring the importance of incorporating stakeholders' nuanced fairness judgments.

Forecasting earthquake sequences remains a central challenge in seismology, particularly under non-stationary conditions. While deep learning models have shown promise, their ability to generalize across time remains poorly understood. We evaluate neural and hybrid (NN plus Markov) models for short-term earthquake forecasting on a regional catalog using temporally stratified cross-validation. Models are trained on earlier portions of the catalog and evaluated on future unseen events, enabling realistic assessment of temporal generalization. We find that while these models outperform a purely Markovian model on validation data, their test performance degrades substantially in the most recent quintile. A detailed attribution analysis reveals a shift in feature relevance over time, with later data exhibiting simpler, more Markov-consistent behavior. To support interpretability, we apply Integrated Gradients, a type of explainable AI (XAI) to analyze how models rely on different input features. These results highlight the risks of overfitting to early patterns in seismicity and underscore the importance of temporally realistic benchmarks. We conclude that forecasting skill is inherently time-dependent and benefits from combining physical priors with data-driven methods.

Bone health studies are crucial in medical practice for the early detection and treatment of Osteopenia and Osteoporosis. Clinicians usually make a diagnosis based on densitometry (DEXA scans) and patient history. The applications of AI in this field are ongoing research. Most successful methods rely on deep learning models that use vision alone (DEXA/X-ray imagery) and focus on prediction accuracy, while explainability is often disregarded and left to post hoc assessments of input contributions. We propose ProtoMedX, a multi-modal (multimodal) model that uses both DEXA scans of the lumbar spine and patient records. ProtoMedX's prototype-based architecture is explainable by design, which is crucial for medical applications, especially in the context of the upcoming EU AI Act, as it allows explicit analysis of model decisions, including incorrect ones. ProtoMedX demonstrates state-of-the-art performance in bone health classification while also providing explanations that can be visually understood by clinicians. Using a dataset of 4,160 real NHS patients, the proposed ProtoMedX achieves 87.58% accuracy in vision-only tasks and 89.8% in its multi-modal variant, both surpassing existing published methods.

From Sea to System: Exploring User-Centered Explainable AI for Maritime Decision Support

Doreen Jirak, Pieter Maes, Armeen Saroukanoff +1

As autonomous technologies increasingly shape maritime operations, understanding why an AI system makes a decision becomes as crucial as what it decides. In complex and dynamic maritime environments, trust in AI depends not only on performance but also on transparency and interpretability. This paper highlights the importance of Explainable AI (XAI) as a foundation for effective human-machine teaming in the maritime domain, where informed oversight and shared understanding are essential. To support the user-centered integration of XAI, we propose a domain-specific survey designed to capture maritime professionals' perceptions of trust, usability, and explainability. Our aim is to foster awareness and guide the development of user-centric XAI systems tailored to the needs of seafarers and maritime teams.

AI For Privacy in Smart Homes: Exploring How Leveraging AI-Powered Smart Devices Enhances Privacy Protection

Wael Albayaydh, Ivan Flechais, Rui Zhao +1

Privacy concerns and fears of unauthorized access in smart home devices often stem from misunderstandings about how data is collected, used, and protected. This study explores how AI-powered tools can offer innovative privacy protections through clear, personalized, and contextual support to users. Through 23 in-depth interviews with users, AI developers, designers, and regulators, and using Grounded Theory analysis, we identified two key themes: Aspirations for AI-Enhanced Privacy - how users perceive AI's potential to empower them, address power imbalances, and improve ease of use- and AI Ethical, Security, and Regulatory Considerations-challenges in strengthening data security, ensuring regulatory compliance, and promoting ethical AI practices. Our findings contribute to the field by uncovering user aspirations for AI-driven privacy solutions, identifying key security and ethical challenges, and providing actionable recommendations for all stakeholders, particularly targeting smart device designers and AI developers, to guide the co-design of AI tools that enhance privacy protection in smart home devices. By bridging the gap between user expectations, AI capabilities, and regulatory frameworks, this work offers practical insights for shaping the future of privacy-conscious AI integration in smart homes.

State-of-the-art (SOTA) Convolutional Neural Networks (CNNs) are criticized for their extensive computational power, long training times, and large datasets. To overcome this limitation, we propose a reasonable network (R-Net), a lightweight CNN only to detect and classify colorectal cancer (CRC) using the Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset (EBHI). Furthermore, six SOTA CNNs, including Multipath-based CNNs (DenseNet121, ResNet50), Depth-based CNNs (InceptionV3), width-based multi-connection CNNs (Xception), depth-wise separable convolutions (MobileNetV2), spatial exploitation-based CNNs (VGG16), Transfer learning, and two ensemble models are also tested on the same dataset. The ensemble models are a multipath-depth-width combination (DenseNet121-InceptionV3-Xception) and a multipath-depth-spatial combination (ResNet18-InceptionV3-VGG16). However, the proposed R-Net lightweight achieved 99.37% accuracy, outperforming MobileNet (95.83%) and ResNet50 (96.94%). Most importantly, to understand the decision-making of R-Net, Explainable AI such as SHAP, LIME, and Grad-CAM are integrated to visualize which parts of the EBHI image contribute to the detection and classification process of R-Net. The main novelty of this research lies in building a reliable, lightweight CNN R-Net that requires fewer computing resources yet maintains strong prediction results. SOTA CNNs, transfer learning, and ensemble models also extend our knowledge on CRC classification and detection. XAI functionality and the impact of pixel intensity on correct and incorrect classification images are also some novelties in CRC detection and classification.

The Intercepted Self: How Generative AI Challenges the Dynamics of the Relational Self

Sandrine R. Schiller, Camilo Miguel Signorelli, Filippos Stamatiou

Generative AI is changing our way of interacting with technology, others, and ourselves. Systems such as Microsoft copilot, Gemini and the expected Apple intelligence still awaits our prompt for action. Yet, it is likely that AI assistant systems will only become better at predicting our behaviour and acting on our behalf. Imagine new generations of generative and predictive AI deciding what you might like best at a new restaurant, picking an outfit that increases your chances on your date with a partner also chosen by the same or a similar system. Far from a science fiction scenario, the goal of several research programs is to build systems capable of assisting us in exactly this manner. The prospect urges us to rethink human-technology relations, but it also invites us to question how such systems might change the way we relate to ourselves. Building on our conception of the relational self, we question the possible effects of generative AI with respect to what we call the sphere of externalised output, the contextual sphere and the sphere of self-relating. In this paper, we attempt to deepen the existential considerations accompanying the AI revolution by outlining how generative AI enables the fulfilment of tasks and also increasingly anticipates, i.e. intercepts, our initiatives in these different spheres.

Chemists in search of structure-property relationships face great challenges due to limited high quality, concordant datasets. Machine learning (ML) has significantly advanced predictive capabilities in chemical sciences, but these modern data-driven approaches have increased the demand for data. In response to the growing demand for explainable AI (XAI) and to bridge the gap between predictive accuracy and human comprehensibility, we introduce LAMeL - a Linear Algorithm for Meta-Learning that preserves interpretability while improving the prediction accuracy across multiple properties. While most approaches treat each chemical prediction task in isolation, LAMeL leverages a meta-learning framework to identify shared model parameters across related tasks, even if those tasks do not share data, allowing it to learn a common functional manifold that serves as a more informed starting point for new unseen tasks. Our method delivers performance improvements ranging from 1.1- to 25-fold over standard ridge regression, depending on the domain of the dataset. While the degree of performance enhancement varies across tasks, LAMeL consistently outperforms or matches traditional linear methods, making it a reliable tool for chemical property prediction where both accuracy and interpretability are critical.

The deployment of large language models (LLMs) in mental health and other sensitive domains raises urgent questions about ethical reasoning, fairness, and responsible alignment. Yet, existing benchmarks for moral and clinical decision-making do not adequately capture the unique ethical dilemmas encountered in mental health practice, where confidentiality, autonomy, beneficence, and bias frequently intersect. To address this gap, we introduce Ethical Reasoning in Mental Health (EthicsMH), a pilot dataset of 125 scenarios designed to evaluate how AI systems navigate ethically charged situations in therapeutic and psychiatric contexts. Each scenario is enriched with structured fields, including multiple decision options, expert-aligned reasoning, expected model behavior, real-world impact, and multi-stakeholder viewpoints. This structure enables evaluation not only of decision accuracy but also of explanation quality and alignment with professional norms. Although modest in scale and developed with model-assisted generation, EthicsMH establishes a task framework that bridges AI ethics and mental health decision-making. By releasing this dataset, we aim to provide a seed resource that can be expanded through community and expert contributions, fostering the development of AI systems capable of responsibly handling some of society's most delicate decisions.

AI Academy: Building Generative AI Literacy in Higher Ed Instructors

Si Chen, Xiuxiu Tang, Alison Cheng +3

Generative AI is reshaping higher education, yet research has focused largely on students, while instructors remain understudied despite their central role in mediating adoption and modeling responsible use. We present the \textit{AI Academy}, a faculty development program that combined AI exploration with pedagogical reflection and peer learning. Rather than a course evaluated for outcomes, the Academy provided a setting to study how instructors build AI literacies in relation to tools, policies, peer practices, and institutional supports. We studied 25 instructors through pre/post surveys, learning logs, and facilitator interviews. Findings show AI literacy gains alongside new insights. We position instructors as designers of responsible AI practices and contribute a replicable program model, a co-constructed survey instrument, and design insights for professional development that adapts to evolving tools and fosters ethical discussion.

Artificial intelligence (AI) is transforming financial planning by expanding access, lowering costs, and enabling dynamic, data-driven advice. Yet without clear safeguards, digital platforms risk reproducing longstanding market inefficiencies such as information asymmetry, misaligned incentives, and systemic fragility. This paper develops a framework for responsible AI in financial planning, anchored in five principles: fiduciary duty, adaptive personalization, technical robustness, ethical and fairness constraints, and auditability. We illustrate these risks and opportunities through case studies, and extend the framework into a five-level roadmap of AI financial intermediaries. By linking technological design to economic theory, we show how AI can either amplify vulnerabilities or create more resilient, trustworthy forms of financial intermediation.