Operationalizing Normative Ethical Principles for Responsible AI Systems: A Taxonomy and Future Directions

핵심 개념

Operationalizing normative ethical principles, such as deontology, virtue ethics, and consequentialism, can promote responsible reasoning in AI systems by accommodating social contexts and human values.

초록

This paper presents a taxonomy of 21 normative ethical principles that have been discussed in the AI and computer science literature. It examines how each principle has been previously operationalized, highlighting key themes that AI practitioners should be aware of when seeking to implement ethical principles in the reasoning capacities of responsible AI systems.

The authors first provide an overview of the paper categorization, classifying works based on the ethical principles explicitly mentioned, the type of contribution, and the evaluation method used. They then explore the taxonomy of ethical principles, including deontology, egalitarianism, proportionalism, Kantian ethics, virtue ethics, consequentialism, utilitarianism, maximin, envy-freeness, doctrine of double effect, and do no harm.

For each principle, the authors summarize its definition, previous applications, and potential difficulties in operationalization. They find that certain principles, such as utilitarianism, are more commonly discussed than others, and that there is a need for more precise specification of the ethical principles used.

The authors envision that this taxonomy will facilitate the development of methodologies to incorporate normative ethical principles in the reasoning capacities of responsible AI systems, promoting ethical evaluation that considers social contexts and human values.

통계

"Ethical evaluation ought to be a reflective development process incorporating social contexts [66, 136]."
"Normative ethics is the study of practical means to determine the ethicality of actions through the use of principles and guidelines, or the rational and systematic study of the standards of right and wrong [99]."
"Operationalising normative ethics principles thereby enables systems to methodically reason about ethics [138]."
"Ethical principles can be operationalised in reasoning capacities as they imply certain logical propositions which must be true for a given action plan to be ethical, and provide frameworks for guiding judgement and action [18]."

인용구

"Ethical principles guide normative judgements, determine the moral permissibility of concrete courses of action and help to understand different perspectives [92]."
"Using ethical principles makes explicit the normative assumptions underlying ethical choices, improving propensity for accountability [49, 86]."
"Translating AI principles into practice is challenging [142]. AI principles do not provide guidance for how they can be implemented, and interpretation of their meaning may diverge [97]. Ethical principles, on the other hand, are abstract rules that provide logical propositions denoting which actions are morally acceptable."

핵심 통찰 요약

Macro Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions

by Jessica Wood... 게시일 arxiv.org 09-12-2024

https://arxiv.org/pdf/2208.12616.pdf

더 깊은 질문

How can the taxonomy of ethical principles be expanded to include principles that are currently underutilized in AI and computer science research?

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.

How can conflicts between ethical principles be resolved when they lead to unintuitive or contradictory outcomes?

Resolving conflicts between ethical principles in AI requires a structured approach that acknowledges the complexity of ethical dilemmas. Here are several strategies to address these conflicts:

Prioritization Frameworks: Establishing a framework for prioritizing ethical principles based on context can help navigate conflicts. For instance, in situations where fairness and transparency conflict, a framework could prioritize transparency in decision-making processes, allowing stakeholders to understand how fairness is being assessed.

Contextual Analysis: Conducting a thorough contextual analysis of the specific situation can illuminate which ethical principles should take precedence. This involves examining the stakeholders involved, the potential impacts of decisions, and the broader social and cultural implications.

Multi-Criteria Decision Analysis (MCDA): Utilizing MCDA techniques can help quantify and compare the implications of different ethical principles in a given scenario. By assigning weights to various principles based on their relevance and importance, decision-makers can arrive at a more balanced resolution.

Ethical Deliberation: Engaging in ethical deliberation processes that involve diverse stakeholders can facilitate discussions around conflicting principles. This collaborative approach allows for the exploration of different perspectives and the development of consensus-based solutions.

Adaptive Ethical Guidelines: Developing adaptive ethical guidelines that can evolve based on feedback and outcomes can help address conflicts. These guidelines should be flexible enough to accommodate new insights and changing societal values, allowing for continuous improvement in ethical decision-making.

By employing these strategies, AI practitioners can better navigate the complexities of ethical conflicts, leading to more intuitive and just outcomes in the deployment of AI systems.

How can the operationalization of ethical principles be better integrated with the broader sociotechnical context, considering the complex social, cultural, and organizational factors that influence the development and deployment of responsible AI systems?

Integrating the operationalization of ethical principles with the broader sociotechnical context requires a multifaceted approach that considers the interplay between technology, society, and culture. Here are key strategies to achieve this integration:

Sociotechnical Systems Framework: Adopting a sociotechnical systems (STS) framework can help ensure that ethical principles are operationalized within the context of social dynamics and technological capabilities. This approach emphasizes the interdependence of social and technical factors, allowing for a more comprehensive understanding of ethical implications.

Cultural Sensitivity: Recognizing and respecting cultural differences is crucial in the operationalization of ethical principles. AI systems should be designed to accommodate diverse cultural values and norms, which can be achieved through localized ethical guidelines that reflect the specific needs and expectations of different communities.

Organizational Ethics Training: Providing ethics training within organizations involved in AI development can foster a culture of ethical awareness and responsibility. This training should emphasize the importance of considering sociotechnical factors and encourage employees to think critically about the ethical implications of their work.

Feedback Mechanisms: Implementing feedback mechanisms that allow users and stakeholders to report ethical concerns and experiences can help organizations adapt their practices. This iterative process ensures that ethical principles remain relevant and responsive to real-world challenges.

Collaborative Governance: Establishing collaborative governance structures that involve multiple stakeholders, including policymakers, ethicists, and community representatives, can facilitate the integration of ethical principles into AI systems. This participatory approach ensures that diverse perspectives are considered in decision-making processes.

By employing these strategies, the operationalization of ethical principles can be effectively integrated with the sociotechnical context, leading to the development of responsible AI systems that are sensitive to the complexities of social, cultural, and organizational factors.