Jul 2026· Journal of Information, Communication and Ethics in Society· pp. 1-25· 0 citations· 90 references
TL;DR
The most influential factor in ethical AI development is “Autonomy and human bias,” followed by “Intentionality and responsibility,” followed by “Intentionality and responsibility”; the “Automation and replacement” factor was ranked the least influential.
Abstract
This study aims to explore the ethical dilemmas posed by generative artificial intelligence (AI) and frames a responsible development framework for AI. It studies influential factors in ethical AI development. The primary study further discusses concerns about data privacy, autonomy and accountability in the context of generative AI systems.
The suggested framework then relies on gray influence analysis (GINA) to assess the degree of influence, which is essential to ethical AI development. It identifies eight key factors, which include transparency, accountability and human bias, as important for ethical AI development. The framework focuses on interventions through GINA to reduce bias and achieve equal AI systems.
This study shows that the most influential factor in ethical AI development is “Autonomy and human bias,” followed by “Intentionality and responsibility.” In contrast, the “Automation and replacement” factor was ranked the least influential.
These factors are systematically considered, with stakeholders developing strategies to facilitate ethical AI development and societal welfare. Future research directions would include the necessary competencies and resources for controlling generative AI, studies of biases in training data sets and the identification of optimal contexts for the deployment of generative AI systems.
This study uniquely explores and identifies influential factors in ethical AI development to address bias and advance fairness.
A Multi-Layer Social-Theoretical AI Ethics Framework (MLST-AEF) that integrates normative ethical reasoning, stakeholder analysis, institutional context, bias and power assessment, and structured decision support is developed.
M. Fakrudeen, J. Otieno· AI and Ethics· 0 citations
This study explores the governance tensions among Google, OpenAI, Meta, and Microsoft, focusing on how competitive AI development can lead to the ethics suppression. The study aims to examine how ethical oversight loses practical influence even when responsible AI structures are formally in place, especially during rapid technological competition. Despite the rapid growth of responsible AI frameworks and ethical principles in the tech sector, there has been less focus on how these concerns are actually applied when pressure to deploy increases. This paper introduces the concept of ethics suppression in AI development, highlighting that while ethical oversight may exist on paper, its practical authority can diminish during decision-making. Using a comparative qualitative case study examining corporate governance documents, public statements, and reports on governance issues, we identify common issues such as reduced authority, a weakened ability to escalate concerns, urgency in deployment, and fragmented governance. Our findings indicate that heightened competition can weaken the influence of ethical oversight, even in organisations with established responsible AI practices. By differentiating ethics suppression from ethics washing, it offers a framework for organisations and policymakers to evaluate the effectiveness of ethical oversight in competitive AI development
Victor Frimpong· International Journal of App...· 0 citations
This research examined how public relations professionals conceptualize, enact, and advocate for responsible AI (RAI) during a period of rapid technological change. Drawing on 22 semi-structured interviews with experienced U.S.-based communications professionals and using constructivist grounded theory (Charmaz, 2006), the study examined how professionals navigate such core ethical values of truth, trust, and transparency while adopting AI and counseling their organizations. Findings reveal that responsible AI in public relations is not given or simply adopted but actively constructed through everyday professional judgment, human oversight, and organizational influence. The study finds that responsible AI practice depends on the interdependence of AI literacy and ethics literacy, with public relations professionals translating both into advocacy, governance, disclosure judgments, and counsel. The study introduces the AI-Ethics Literacy Model for Human Advocacy[trademark] (AELHA[trademark], pronounced “ah-LEE-ha”) as its original conceptual contribution. AELHA encodes a foundational relationship: AI Literacy + Ethics Literacy = Human Advocacy. The model proposes that neither competency alone is sufficient for responsible AI practice and that their combination enables public relations professionals to advocate not only for organizational publics, but for humanity as the emergent public of the AI age.
Human–Artificial Intelligence (AI) co-creation refers to the collaborative creation of content, designs, decisions, software, and research outputs by humans and intelligent systems. The rapid advancement of generative AI technologies has transformed creativity and innovation across education, healthcare, business, engineering, media, and scientific research. While AI enhances productivity, creativity, and problem-solving, it also raises significant ethical concerns related to accountability, transparency, fairness, privacy, intellectual property, trust, and human agency. Unlike traditional human–computer interaction, AI actively contributes to ideation and decision-making, making ethical governance increasingly important. This study examines the ethical challenges of Human–AI co-creation through a literature review and proposes a conceptual framework encompassing fairness, accountability, transparency, privacy, intellectual property protection, trustworthiness, and human oversight. The findings indicate that accountability and transparency are the most influential factors for responsible AI collaboration, while bias and intellectual property disputes remain major barriers to adoption. The study highlights the need for ethical governance frameworks, transparent AI systems, regulatory compliance, and human oversight to promote trustworthy and responsible co-creative practices. Overall, the proposed framework supports sustainable Human–AI collaboration by balancing technological innovation with ethical responsibility, ensuring that AI complements rather than replaces human creativity.
Seshagiri N· International Journal of Inn...· 0 citations
It is suggested in the paper that a methodology of ethical governance based on principles of responsible AI should be structured, fairness-by-design, transparency, human-in-the-loop oversight, and constant impact assessment, which underscores the fact that AI systems have ethical failures that are seldom technical but rather socio-technical, which necessitate interventions at the policy, organizational governance, and technical design levels.
Fatou Diop· International Journal of Inn...· 0 citations
The study presents the T-STAEF (Temporal Socio-Technical AI Ethics Framework) to provide a temporal socio-technical lens to view the ethical management of AI systems and provides structural, conceptual model to assist organisations in understanding why there are consistent ethical issues related to AI.
Ahmad Ali· AI and Ethics· 0 citations
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