It is argued that AI software is still software and needs to be approached from the software development perspective, and whether the focus should be on AI ethics or the quality of an AI system, called a maturity model for the development of AI systems is discussed.
Abstract
There appears to be a common agreement that ethical concerns are of high importance when it comes to systems equipped with some sort of Artificial Intelligence (AI). Demands for ethical AI are declared from all directions. As a response, in recent years, public bodies, governments, and universities have rushed in to provide a set of principles to be considered when AI based systems are designed and used. We have learned, however, that high-level principles do not turn easily into actionable advice for practitioners. Hence, also companies are publishing their own ethical guidelines to guide their AI development. This paper argues that AI software is still software and needs to be approached from the software development perspective. The software engineering paradigm has introduced maturity model thinking, which provides a roadmap for companies to improve their performance from the selected viewpoints known as the key capabilities. We want to voice out a call for action for the development of a maturity model for AI software. We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system, called a maturity model for the development of AI systems.
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
Ethical artificial intelligence (AI) is emerging as a widespread approach that refers to the development and use of AI technologies in a manner compatible with human values. However, the tendency of AI companies in Europe to relocate their operations to the United States is increasing debates about the applicability of this approach. One of the main reasons for this is the European Union's General Data Protection Regulation (GDPR); the costs, controls, and restrictions imposed by this regulation on data use may lead global technology firms to move outside of Europe. The high economic value-generating potential of AI creates pressure for the relaxation of these regulations, presenting a choice between ethical principles and economic interests. This study aims to provide a conceptual evaluation within the framework of the question of whether the economic gains provided by AI can outweigh human-centered ethical values. Accordingly, the research adopts a qualitative approach based on the literature and examines the current debates at a conceptual level. The assessments indicate that the European Union's desire to compete with the United States and China in the global artificial intelligence race creates a context that may make prioritizing ethical principles difficult. In this context, it is considered that the development of an ethical understanding of artificial intelligence cannot be considered independently of economic and political dynamics, and while it presents various limitations under current conditions, it offers a structure that can be improved with appropriate governance mechanisms.
Tolgay Ercan· Düzce Üniversitesi Sosyal Bi...· 0 citations
Technologies are being rapidly developed that enable artificial intelligence (AI) systems to dynamically emulate the voice, appearance, and mannerisms of real people. Although concerns about the potential harms from such systems are widespread, there is comparatively little discussion of the conditions under which they might be used to benefit impacted parties. Recent efforts to provide a formal model of benefit represent an advance, but do not represent with sufficient clarity how such systems mediate social relationships in a way that can make a wide range of parties vulnerable to failure modes such as deception and domination. We build on and extend the Beneficent Intelligence (BI) framework from [23] to clarify the mediating role that such AI systems play, how this impacts alignment with the goals of a range of parties, and identify conditions necessary for such systems to confer meaningful benefit. Our analysis can assist developers, users, and other interested parties in better managing the risks associated with emulating technologies.
Ida Mattsson, Mai-Lee Chang, Niloofar Nikookar et al.· AI and Ethics· 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
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
Artificial intelligence is reshaping decisions that affect people, institutions, and societies. Understanding how to design, deploy, and govern AI systems that can be trusted is now essential in many disciplines. This book offers a clear, concise introduction to trustworthy AI, treating AI not just as a technical artifact but as a socio-technical system embedded in human contexts. Developed from an internationally applicable educational framework, the book is designed for teaching and learning in computer science, data science, law, policy, business, and related fields. It equips students and professionals with the concepts and judgment needed to engage critically and responsibly with AI in practice. Combining ethics, governance, and practical insight, the book explains key concepts including transparency, fairness, accountability, human oversight, and stakeholder participation. An interdisciplinary approach makes the material accessible to both technical and non-technical audiences, with realistic scenarios and reflection questions so readers connect principles to real-world AI applications.
Andrea Aler Tubella, Virginia Dignum, Marçal Mora-Cantallops et al.· 0 citations
What does it take to trust AI-driven HVAC optimization? Our AI Model Factory combines agents, machine learning, reinforcement learning and deterministic checks in a governed workflow designed for messy, real-world building data. The post We built an AI factory for HVAC control appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduAug 18, 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
