Jun 2019· International Conference on Product Focused Software Process Improvement· pp. 331-338· 18 citations· ⚡ 3 influential· 20 references
Computer Science
TL;DR
This paper begins to address the focal problem of implementing ethics into AI system design and development in practice by providing elements needed for producing a baseline for ethics in AI based software development by means of an industrial multiple case study on AI systems development in the healthcare sector.
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
Despite the rise in the development and use of Artificial Intelligence (AI), the application of ethics to this new technology remains relatively nascent. This study investigates the implementation of AI ethics within health companies in Switzerland, their challenges and facilitating factors. A cross-sectional survey, with a 15% response rate (N = 28), was employed, targeting professionals in the private sector. The findings revealed a notable disparity between the claimed behaviours and the observed improvements, as well as in the formalisation of governance for AI ethics. The results find different practices between startups, small- and medium-sized enterprises (SMEs), and large multinational corporations (MNCs). Challenges were identified in the lack of standards, implementation guides, and ethical knowledge. The discussion addresses systemic issues, including governance and lack of ethical knowledge, as well as organisational factors such as company size and role. The paper concludes with institutional and organisational recommendations to develop clear regulatory standards, adopt proactive measures, and provide comprehensive AI ethics training and resources to support the ethical implementation of AI in Swiss health organisations.
Heidi Lee, Sara Kijewski, Agata Ferretti et al.· AI and Ethics· 0 citations
Abstract This article presents the AI Ethics Canvas: a graphical tool to guide public administrations in mainstreaming ethics in the development, deployment, and monitoring of artificial intelligence (AI) tools for their own everyday use. Our canvas is the result of an action-research effort conducted within the scope of a project involving the Autonomous Province of Trento (PAT), Italy. We initially developed it based on existing ethical and legal frameworks. Then, we conducted qualitative interviews with PAT functionaries and technologists developing software on behalf of the PAT to ensure the canvas’s content was clear and applicable to public administration work. While originally elaborated for the PAT, the canvas presented is applicable to all public administrations within the European Union who wish to adopt AI in their operations, as it references to core European legislation such as General Data Protection Regulation, AI Act, Data Act, and Data Governance Act. The article presents the AI Ethics Canvas both as a final product and in its creation process to enhance transparency in AI ethics tool development.
Riccardo Nanni, P. G. Bizzaro, Albana Celepija et al.· Data & Policy· 0 citations
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
Artificial intelligence (AI) is increasingly explored as a tool to support institutional decision-making, including within research ethics committees (RECs). However, empirical evidence remains limited regarding how REC members define the acceptable scope of AI integration in ethics review. This study examines how members of Spanish Research Ethics Committees (CEI/CEIm) conceptualise AI use within institutional oversight. A national cross-sectional survey was distributed to 202 committee members and technical secretariat staff, yielding 63 responses (31.2%). The questionnaire combined closed-ended and open-ended items addressing perceived usefulness, acceptable levels of automation, governance concerns, and implementation barriers. Quantitative data were analysed descriptively with exploratory chi-square tests, and qualitative responses were examined using inductive thematic analysis. Respondents expressed strong interest in AI for administrative and documentary support but consistently rejected fully automated ethical decision-making. A statistically significant association was observed between annual workload and interest in AI implementation (χ
2
= 6.20,
p
= 0.045), with higher-workload committees reporting greater openness. Years of experience and institutional role were not significantly associated with attitudes. Across responses, continuous human oversight, accountability, transparency, and data protection emerged as central conditions for acceptability. The findings indicate that AI integration in research ethics oversight is shaped not only by technological feasibility but by institutional concerns regarding responsibility, legitimacy, and deliberative authority. The study contributes empirical insight into how oversight institutions negotiate the acceptable boundaries of AI-assisted decision support in ethically sensitive governance contexts.
Daniel Wang, Marta Guix Arnau, Cristina Llop Julià et al.· Research Ethics· 0 citations
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