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To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services. To guide technology developments, this paper provides a systematic…
Sociotechnical requirements shape the governance of artificially intelligent (AI) systems. In an era where embodied AI technologies are rapidly reshaping various facets of contemporary society, their inherent dynamic adaptability presents a…
This paper examines the European Union's emerging regulatory landscape - focusing on the AI Act, corporate sustainability reporting and due diligence regimes (CSRD and CSDDD), and data center regulation - to assess whether it can…
Artificial intelligence (AI) is currently spearheaded by machine learning (ML) methods such as deep learning which have accelerated progress on many tasks thought to be out of reach of AI. These recent ML methods are often compute hungry,…
Thanks to the great progress of machine learning in the last years, several Artificial Intelligence (AI) techniques have been increasingly moving from the controlled research laboratory settings to our everyday life. AI is clearly…
Participatory approaches are widely invoked in AI governance, yet participation rarely translates into durable influence. In public sector and civic AI systems, community contributions such as deliberations, annotations, prompts, and…
As Artificial Intelligence (AI) becomes integral to business operations, integrating Responsible AI (RAI) within Environmental, Social, and Governance (ESG) frameworks is essential for ethical and sustainable AI deployment. This study…
This article analyzes the impact of artificial intelligence (AI) on contemporary society and the importance of adopting an ethical approach to its development and implementation within organizations. It examines the technocritical…
Digital systems have become simultaneously more powerful and more wasteful. Features accumulate that nobody uses. Data is collected that nobody analyzes. AI is deployed at significant energy and water costs for gains that a simpler approach…
Generative Artificial Intelligence (GenAI) is driving significant environmental impacts. The rapid development and deployment of increasingly larger algorithmic models capable of analysing vast amounts of data are contributing to rising…
Ensuring responsible use of artificial intelligence (AI) has become imperative as autonomous systems increasingly influence critical societal domains. However, the concept of trustworthy AI remains broad and multi-faceted. This thesis…
Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much…
Society's increasing dependence on Artificial Intelligence (AI) and AI-enabled systems require a more practical approach from software engineering (SE) executives in middle and higher-level management to improve their involvement in…
As AI-driven dataspaces become integral to data sharing and collaborative analytics, ensuring privacy, performance, and policy compliance presents significant challenges. This paper provides a comprehensive review of privacy-preserving and…
The growing need for accountability of the people behind AI systems can be addressed by leveraging processes in three fields of study: ethics, law, and computer science. While these fields are often considered in isolation, they rely on…
Sustainable AI projects are continuously responsive to the transformative effects as well as short-, medium-, and long-term impacts on individuals and society that the design, development, and deployment of AI technologies may have.…
The rapid growth in the deployment and scale of modern artificial intelligence (AI) systems has intensified concerns regarding their environmental impacts, yet we still lack a comprehensive view of where and how these impacts arise across…
Artificial Intelligence (AI) techniques continue to broaden across governmental and public sectors, such as power and energy - which serve as critical infrastructures for most societal operations. However, due to the requirements of…
Despite extensive investment in artificial intelligence, 95% of enterprises report no measurable profit impact from AI deployments (MIT, 2025). In this theoretical paper, we argue that this gap reflects paradigmatic lock-in that channels AI…
As AI/ML models, including Large Language Models, continue to scale with massive datasets, so does their consumption of undeniably limited natural resources, and impact on society. In this collaboration between AI, Sustainability, HCI and…