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The rapid proliferation of artificial intelligence (AI) models and methods presents growing challenges for research software engineers and researchers who must select, integrate, and maintain appropriate models within complex research…
Large Language Model safety alignment predominantly operates on a binary assumption that requests are either safe or unsafe. This classification proves insufficient when models encounter ethical dilemmas, where the capacity to reason…
AI systems increasingly shape critical decisions across personal and societal domains. While empirical risk minimization (ERM) drives much of the AI success, it typically prioritizes accuracy over trustworthiness, often resulting in biases,…
Reliability Assessment is an indispensable technology for identifying, interpreting, and lessening the potential failures in safety-critical systems like smart grids. Failure modes and effects analysis (FMEA) is one of the well documented…
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…
Artificial Intelligence (AI) presents transformative opportunities for industries and society, but its responsible development is essential to prevent unintended consequences. Ethically sound AI systems demand strategic planning, strong…
As intelligent systems are increasingly implemented in our society to make autonomous decisions, their commitment to human values raises serious concerns. Their alignment with human values remains a critical challenge because it can…
Large Language Models (LLMs) generate fluent, plausible text that can mislead users into mistaking simulated coherence for genuine understanding. This paper introduces the Epistemic Suite, a post-foundational diagnostic methodology for…
Large Language Models (LLMs) are increasingly integrated into software engineering (SE) tools for tasks that extend beyond code synthesis, including judgment under uncertainty and reasoning in ethically significant contexts. We present a…
Risk Assessment is a well known and powerful method for discovering and mitigating risks, and hence improving safety. Ethical Risk Assessment uses the same approach but extends the envelope of risk to cover ethical risks in addition to…
Ethical decision-making is a critical aspect of human judgment, and the growing use of LLMs in decision-support systems necessitates a rigorous evaluation of their moral reasoning capabilities. However, existing assessments primarily rely…
Blockchain technology adds records to a list using cryptographic links. Therefore, the security of blockchain smart contracts is among the most popular contemporary research topics. To improve the theorem-proving technology in this field,…
This paper investigates the ethical implications of aligning Large Language Models (LLMs) with financial optimization, through the case study of GreedLlama, a model fine-tuned to prioritize economically beneficial outcomes. By comparing…
Artificial intelligence (AI) offers incredible possibilities for patient care, but raises significant ethical issues, such as the potential for bias. Powerful ethical frameworks exist to minimize these issues, but are often developed for…
Group Decision-Making (GDM) plays a crucial role in various real-life scenarios where individuals express their opinions in natural language rather than structured numerical values. Traditional GDM approaches often overlook the subjectivity…
Artificial intelligence (AI) technologies should adhere to human norms to better serve our society and avoid disseminating harmful or misleading information, particularly in Conversational Information Retrieval (CIR). Previous work,…
Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy,…
This study implements a novel Fuzzy Cognitive Map (FCM) framework for addressing large complex socio-ecological problems. These are characterized as qualitative, dominated by uncertainty, human involvement with different and vague…
We find ourselves surrounded by a rapidly increasing number of autonomous and semi-autonomous systems. Two grand challenges arise from this development: Machine Ethics and Machine Explainability. Machine Ethics, on the one hand, is…
This empirical study proposes a novel methodology to measure users' perceived trust in an Explainable Artificial Intelligence (XAI) model. To do so, users' mental models are elicited using Fuzzy Cognitive Maps (FCMs). First, we exploit an…