Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. Given the complexity of the problem and the involvement of multiple stakeholders—including developers, end users, and third-parties—there is a need to understand the landscape of the sources of bias, and the solutions being proposed to address them, from a broad, cross-domain perspective. This survey provides a “fish-eye view,” examining approaches across four areas of research. The literature describes three steps toward a comprehensive treatment—bias detection, fairness management, and explainability management—and underscores the need to work from within the system as well as from the perspective of stakeholders in the broader context.

In this work, we investigate how students in fields adjacent to algorithms development perceive fairness, accountability, transparency, and ethics in algorithmic decision-making. Participants (N = 99) were asked to rate their agreement with statements regarding six constructs that are related to facets of fairness and justice in algorithmic decision-making using scenarios, in addition to defining algorithmic fairness and providing their view on possible causes of unfairness, transparency approaches, and accountability. The findings indicate that “agreeing” with a decision does not mean that the person “deserves the outcome,” perceiving the factors used in the decision-making as “appropriate” does not make the decision of the system “fair,” and perceiving a system's decision as “not fair” is affecting the participants' “trust” in the system. Furthermore, fairness is most likely to be defined as the use of “objective factors,” and participants identify the use of “sensitive attributes” as the most likely cause of unfairness.

Artificial Intelligence (AI) is now everywhere, including in the classroom. Thus, it is crucial for teachers not only to be able to use AI, but also to understand it, as they bear the responsibility of teaching the next generation with and about AI. Although there have been extensive discussions about the importance of digital skills and knowledge of AI, limited research has focused on the attitudes, digital skills, and AI literacy levels of teachers, within small populations where English is not the native language, such as Cyprus. Our study addresses this gap by adapting the General Attitudes Towards AI Scale (GAAIS) and the AI Literacy Scale (AILS) into the Greek language, and investigating the digital skills, attitudes towards AI, AI literacy, and intentions to teach and use AI among teachers in Cyprus. The Greek versions of GAAIS and AILS have been validated, demonstrating good validity and reliability. Our findings also indicate that Cypriot teachers generally possess intermediate digital skills, but maintain neutral attitudes towards AI and exhibit intermediate levels of AI literacy. Moreover, we discovered significant positive correlations between teachers’ digital skills and their AI literacy, attitudes towards AI, and intentions to use and teach AI.

Artificial intelligence (AI) is increasingly applied in a wide range of healthcare and Intensive Care Unit (ICU) areas to serve—among others—as a tool for disease detection and prediction, as well as for healthcare resources’ management. Since sepsis is a high mortality and rapidly developing organ dysfunction disease afflicting millions in ICUs and costing huge amounts to treat, the area can benefit from the use of AI tools for early and informed diagnosis and antibiotic administration. Additionally, resource allocation plays a crucial role when patient flow is increased, and resources are limited. At the same time, sensitive data use raises the need for ethical guidelines and reflective datasets. Additionally, explainable AI is applied to handle AI opaqueness. This study aims to present existing clinical approaches for infection assessment in terms of scoring systems and diagnostic biomarkers, along with their limitations, and an extensive overview of AI applications in healthcare and ICUs in terms of (a) sepsis detection/prediction and sepsis mortality prediction, (b) length of ICU/hospital stay prediction, and (c) ICU admission/hospitalization prediction after Emergency Department admission, each constituting an important factor towards either prompt interventions and improved patient wellbeing or efficient resource management. Challenges of AI applications in ICU are addressed, along with useful recommendations to mitigate them. Explainable AI applications in ICU are described, and their value in validating, and translating predictions in the clinical setting is highlighted. The most important findings and future directions including multimodal data use and Transformer-based models are discussed. The goal is to make research in AI advances in ICU and particularly sepsis prediction more accessible and provide useful directions on future work.

As Artificial Intelligence (AI) continues transforming workplaces globally, particularly within the Information Technology (IT) industry, understanding its impact on IT professionals and computing curricula is crucial. This research builds on joint work from two countries, addressing concerns about AI's increasing influence in IT sector workplaces and its implications for tertiary education. The study focuses on AI technologies such as generative AI (GenAI) and large language models (LLMs). It examines how they are perceived and adopted and their effects on workplace dynamics, task allocation, and human-system interaction. IT professionals, noted as early adopters of AI, offer valuable insights into the interplay between AI and work engagement, highlighting the significant competencies required for digital workplaces. This study employs a dual-method approach, combining a systematic and multi-vocal literature review and qualitative research methods. These included a thematic analysis of a set of 47 interviews conducted between March and May of 2024 with IT professionals in two countries (New Zealand and Sweden). The research aimed to understand the implications for computing students, education curricula, and the assessment of emerging professional competencies. The literature review found insufficient evidence addressing comprehensive AI practice methodologies, highlighting the need to both develop and regulate professional competencies for effective AI integration. Key interview findings revealed diverse levels of GenAI adoption, ranging from individual experimentation to institutional integration. Participants generally expressed positive attitudes toward the technology and were actively pursuing self-learning despite some concerns. The themes emerging from the interviews included AI's role in augmenting human tasks, privacy and security concerns, productivity enhancements, legal and ethical challenges, and the evolving need for new competencies in the workplace. The study underscores the critical role of competency frameworks in guiding professional development and ensuring preparedness for an AI-driven environment. Additionally, it highlights the need for educational institutions to adapt curricula to address these emerging demands effectively

In today’s world, children are increasingly interacting with AI technologies as part of their daily routines. However, many children may not fully grasp how these technologies function or their implications. Misconceptions about AI capabilities, risks, and benefits abound, underlining the importance of early education on the subject. Designing educational tools and materials tailored for children necessitates a deep understanding of their existing knowledge and perceptions of AI. By leveraging children’s insights and experiences with AI, we can develop effective educational strategies that cater to their specific needs and enhance their understanding of this rapidly evolving technology.