May 2019· EUROMICRO Conference on Software Engineering and Advanced Applications· pp. 46-50· 17 citations· ⚡ 2 influential· 37 references
Computer Science
TL;DR
A key finding from the study indicates that simply the presence of an ethical tool has an effect on ethical consideration, creating more responsibility even in instances where the use of the tool is not intrinsically motivated.
Abstract
Use of artificial intelligence (AI) in human contexts calls for ethical considerations for the design and development of AI-based systems. However, little knowledge currently exists on how to provide useful and tangible tools that could help software developers and designers implement ethical considerations into practice. In this paper, we empirically evaluate a method that enables ethically aligned design in a decision-making process. Though this method, titled the RESOLVEDD strategy, originates from the field of business ethics, it is being applied in other fields as well. We tested the RESOLVEDD strategy in a multiple case study of five student projects where the use of ethical tools was given as one of the design requirements. A key finding from the study indicates that simply the presence of an ethical tool has an effect on ethical consideration, creating more responsibility even in instances where the use of the tool is not intrinsically motivated.
A comprehensive model for integrating ethical standards into the phases of the Software Development Life Cycle (SDLC) is proposed, founded on the pillars of fairness, transparency, accountability, and sustainability, offering practical recommendations aimed at developers, organizations, and policymakers.
A. Alaswad· Al-Farooq Journal of Science...· 0 citations
As Artificial Intelligence (AI) is incorporated into the workflow, several ethical issues and risks which can cause actual harms are being introduced. Despite the well-established frameworks of ethical AI principles, there are few practice-oriented approaches for structured interdisciplinary assessment of the ethical aspects associated with AI use and design. To supply the principles and address the increasing focus on AI-risks from an ethical perspective, an Ethical risk assessmeNt of Ai iN pracTice (ENACT) methodology is proposed. To develop ENACT together with a cross-sectoral, interdisciplinary consortium of Norwegian private and public businesses, the core principles of Design-Based Research (DBR) were applied including real context orientation, collaborative partnership and focus on testing and multiple interactions. Four aspects of the ENACT methodology were collaboratively developed and tested and are proposed in this paper including format, structure, scope and support tools. This paper describes and details three steps of the ENACT methodology and discusses its potential and limitations for qualitative ethical risk assessment of AI in organisational settings.
N. Murashova, Leonora Onarheim Bergsjø, Heidi Dahl et al.· Nordic Machine Intelligence· 0 citations
A critical analysis of tools and trust mark frameworks intended to operationalize trustworthy AI (TAI), drawing on a comprehensive dataset from the OECD identifies significant asymmetries in ethical focus, lifecycle coverage, stakeholder targeting, and tool typology.
Michael Papademas, Xenia Ziouvelou, K. Karpouzis et al.· arXiv.org· 0 citations
This study examines how organizations design and implement ethical AI systems to enhance customer and employee experience (CX and EX) while navigating concerns around transparency, autonomy, and trust. Drawing on stakeholder theory and socio-technical systems theory, we conducted qualitative multi-case study across four technology organizations, utilizing interviews, focus groups, document analysis, and observations. Through thematic analysis, we identified nine key themes that cluster into three overarching processes: stakeholder tensions, ethical design practices, and organizational governance. These insights are synthesized into a processual, testable conceptual model linking ethical design mechanisms to CX and EX outcomes. We also show the influence of organizational capabilities and contextual enablers and barriers. The study introduces and develops the construct of ethical knowledge governance and illuminates the psychological and emotional consequences of AI integration for stakeholders. Implications for theory development and organizational practice are discussed.
Artificial Intelligence (AI) is rapidly entering scientific research programmes on the assumption that the established ethical frameworks are enough to address failure risks. But that assumption is wrong: AI systems are structurally different from the tools that conventional research ethics was designed to govern. As illustrated by mainstream models, AI systems can be characterized by being stochastic, opaque, resource-intensive, and operate at population scale, meaning that ethical breaches can extend far beyond individual research projects, with potential consequences for public trust in democratic institutions. Taking the case of EU research as reference, this paper proposes the ARCHON framework,a set of ten requirements derived from the technical architecture of AI systems, to facilitate ethical compliance of scientific research for experts and practicioners engaging in roles such as proposal evaluators, project officers, ethics board members, or research administrators. The paper includes a mapping table intended to facilitate the application of the framework to evaluate ethical compliance, both for specific projects and general research programmes. Applied consistently, the ARCHON framework enables substantive rather than merely declarative AI ethical compliance, contributing this way for scientific research policy to continue addressing societal needs while remaining democratically accountable.
Jesus Manuel Benitez Baleato· Open Research Europe· 0 citations
The increasing integration of Artificial Intelligence (AI) in higher education is transforming approaches to the design and development of digital learning environments. Moodle, as an open-source Learning Management System (LMS), offers a flexible architecture conducive to innovation, but the integration of artificial intelligence into software development processes remains poorly formalized, particularly concerning human oversight and ethical governance. This study aims to formalize and assess a structured paradigm for human-artificial intelligence collaboration within the framework of educational software engineering. It introduces the Collaborative Iterative Co-Authoring Process (CICAP), a methodological framework integrating a generative artificial intelligence model into the development of a Moodle plugin for managing internship and job opportunities. The process was conducted under continuous human oversight, according to ethics-by-design principles, to ensure transparency, accountability, and explainability at each stage. The experimentation resulted in a complete plugin exhibiting high technical quality, stability, and maintainability. The results provide preliminary evidence that a human-supervised, AI-assisted workflow can increase efficiency while adhering to ethical and production-quality standards. The CICAP paradigm thus proposes a reproducible and responsible model for ethical and sustainable co-creation in educational engineering.
Yassine Karroumi, Khalid El Khattabi, F. Ouzennou et al.· International Journal of Inf...· 0 citations
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