To expand the taxonomy of ethical principles in AI and computer science, it is essential to conduct a comprehensive review of existing literature across various disciplines, including philosophy, sociology, and ethics, to identify principles that have not been adequately addressed in the context of AI. This can involve the following steps:
Interdisciplinary Collaboration: Engaging with experts from diverse fields such as ethics, law, sociology, and psychology can help uncover ethical principles that are relevant but underutilized in AI research. For instance, principles from environmental ethics or indigenous knowledge systems may provide valuable insights into responsible AI development.
Stakeholder Engagement: Actively involving stakeholders, including marginalized communities, ethicists, and industry practitioners, can highlight ethical concerns that are often overlooked. This participatory approach can lead to the identification of principles that resonate with a broader audience and reflect diverse values.
Case Studies and Real-World Applications: Analyzing case studies where ethical principles have been applied in non-AI contexts can provide a foundation for their adaptation in AI. For example, principles related to restorative justice or community well-being could be integrated into AI systems that impact social dynamics.
Iterative Refinement: The taxonomy should be treated as a living document that evolves with ongoing research and societal changes. Regularly revisiting and updating the taxonomy based on new findings and societal shifts will ensure that it remains relevant and comprehensive.
Focus on Underrepresented Principles: Specific principles such as care ethics, which emphasizes relationships and responsibilities, or principles related to digital rights and privacy, should be explicitly included in the taxonomy. This can help address ethical dilemmas that arise from the deployment of AI technologies.
By implementing these strategies, the taxonomy can be enriched with a broader range of ethical principles, ensuring that responsible AI systems are developed with a more holistic understanding of ethical considerations.