Ethical horizons in generative AI
Addressing bias and advancing fairness through responsible frameworks
Bibliographic Data
| ID | 22428784 |
|---|---|
| Authors | Sachin Kumar (0000-0001-7643-4030, Indian Institute of Technology Jammu, corresponding author), Vinay Singh (0000-0001-9703-5282, Atal Bihari Vajpayee Indian Institute of Information Technology and Management), Vinayak Pandey (Atal Bihari Vajpayee Indian Institute of Information Technology and Management) |
| Year | 2026 |
| Pages | 1-25 |
| Publication date | 2026-07-20 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Information Communication and Ethics in Society (JOURNAL) |
| Journal identifiers | ISSN: 1758-8871 • E-ISSN: 1477-996X |
| Publisher | Emerald (PUBLISHER) |
| DOI | 10.1108/jices-01-2025-0025 |
| OpenAlex | W7169161485 |
| Language | EN |
| References cited | 95 |
Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Originality/value This study uniquely explores and identifies influential factors in ethical AI development to address bias and advance fairness.
Accountability · Autonomy · Generative grammar · Generative model · Psychological intervention · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI
The Potential of Generative Artificial Intelligence Across Disciplines
Integrating Ethics and Career Futures with Technical Learning to Promote AI Literacy for Middle School Students
Opinion Paper
Trust in AI and Its Role in the Acceptance of AI Technologies
Ethics and Privacy in AI and Big Data
Dissecting racial bias in an algorithm used to manage the health of populations
Intersectionality of social and philosophical frameworks with technology
Use case cards
User Perceptions of Algorithmic Decisions in the Personalized AI System
Are Algorithmic Decisions Legitimate? The Effect of Process and Outcomes on Perceptions of Legitimacy of AI Decisions
Advertising Benefits from Ethical Artificial Intelligence Algorithmic Purchase Decision Pathways
Categorization and challenges of utilitarianisms in the context of artificial intelligence
The Google self as digital human twin
Ethical approaches in designing autonomous and intelligent systems
Companies Committed to Responsible AI
Ethical assessments and mitigation strategies for biases in AI-systems used during the Covid-19 pandemic
Doing responsibilities in entangled worlds
A framework of artificial intelligence augmented design support
Designing fair AI for managing employees in organizations
The blended future of automation and AI
Ethical reasoning in technology
Uncovering the Hidden Curriculum in Generative AI
The ethics of generative AI in social science research
The philosophy of cognitive diversity
Generative AI tools (ChatGPT*) in social science research
Ethical concerns in AI development
| Citation velocity | historical |
|---|---|
| Highly cited | No |