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The adoption of data science brings vast benefits to Small and Medium-sized Enterprises (SMEs) including business productivity, economic growth, innovation and jobs creation. Data Science can support SMEs to optimise production processes,…
In the financial field of the United States, the application of big data technology has become one of the important means for financial institutions to enhance competitiveness and reduce risks. The core objective of this article is to…
While incorporating LLMs into systems offers significant benefits in critical application areas such as healthcare, new security challenges emerge due to the potential cyber kill chain cycles that combine adversarial model, prompt injection…
Individuals and organizations cope with an always-growing amount of data, which is heterogeneous in its contents and formats. An adequate data management process yielding data quality and control over its lifecycle is a prerequisite to…
The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data…
Context: Empirical Software Engineering (ESE) faces increasing challenges due to data scale, methodological complexity, and reproducibility concerns. Large Language Models (LLMs) have emerged as promising tools to support empirical…
Although AI systems are increasingly being leveraged to provide value to organizations, individuals, and society, significant attendant risks have been identified and have manifested. These risks have led to proposed regulations,…
Research management applications (RMA) are widely used in clinical research environments to collect, transmit, analyze, and store sensitive data. This data is so valuable making RMAs susceptible to security threats. This analysis, analyzes…
As generative AI systems, including large language models (LLMs) and diffusion models, advance rapidly, their growing adoption has led to new and complex security risks often overlooked in traditional AI risk assessment frameworks. This…
Large Language Models (LLMs) are increasingly integrated into critical decision-making pipelines, a trend that raises the demand for robust and automated data analysis. Current approaches to dataset risk analysis are limited to manual…
Studying potential global catastrophes is vital. The high stakes of existential risk studies (ERS) necessitate serious scrutiny and self-reflection. We argue that existing approaches to studying existential risk are not yet fit for purpose,…
As AI systems integrate into critical infrastructure, security gaps in AI compliance frameworks demand urgent attention. This paper audits and quantifies security risks in three major AI governance standards: NIST AI RMF 1.0, UK's AI and…
Governments, industry, and academia have undertaken efforts to identify and mitigate harms in ML-driven systems, with a particular focus on social and ethical risks of ML components in complex sociotechnical systems. However, existing…
Risk assessment tools in healthcare commonly employ point-based scoring systems that map patients to ordinal risk categories via thresholds. While electronic health record (EHR) data presents opportunities for data-driven optimization of…
The cybersecurity threat landscape is constantly actively making it imperative to develop sound frameworks to protect the IT structures. Based on this introduction, this paper aims to discuss the application of cybersecurity frameworks into…
Data-driven cyberthreat detection has become a crucial defense technique in modern cybersecurity. Network defense, supported by Network Intrusion Detection Systems (NIDSs), has also increasingly adopted data-driven approaches, leading to…
In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as…
In the early 90s, researchers began to focus on security as an important property to address in combination with safety. Over the years, researchers have proposed approaches to harmonize activities within the safety and security…
The increasing availability of data and advancements in computational intelligence have accelerated the adoption of data-driven methods (DDMs) in product development. However, their integration into product development remains fragmented.…
Reproducibility should be a cornerstone of science as it enables validation and reuse. In recent years, the scientific community and the general public became increasingly aware of the reproducibility crisis, i.e. the wide-spread inability…