Artificial Intelligence, Values and Alignment (2001.09768v2)

Abstract: This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, which combines these elements in a systematic way, has considerable advantages in this context. Third, the central challenge for theorists is not to identify 'true' moral principles for AI; rather, it is to identify fair principles for alignment, that receive reflective endorsement despite widespread variation in people's moral beliefs. The final part of the paper explores three ways in which fair principles for AI alignment could potentially be identified.

Philosophical Dimensions of AI Alignment: An In-Depth Analysis

The paper, "Artificial Intelligence, Values, and Alignment" by Iason Gabriel, explores the philosophical intricacies of AI alignment, exploring how normative and technical dimensions intersect to form a comprehensive approach to AI value alignment. The research is structured around three central propositions that aim to address the perennial problem of aligning AI systems with human values, especially in a world marked by moral pluralism.

Key Arguments and Propositions

1. Interrelationship Between Normative and Technical Aspects: The paper posits that the technical task of aligning AI agents with specific values is deeply intertwined with normative questions about which values should be selected. Gabriel challenges the 'simple thesis'—the notion that technical issues can be solved in isolation from philosophical considerations. This interdependency suggests that the development of AI requires an integrated approach that marries ethical theorizing with technical proficiency.
2. Clarification of Alignment Objectives: Gabriel emphasizes the need for clarity in defining the alignment goals. The paper elucidates the distinctions among aligning AI with instructions, intentions, revealed preferences, ideal preferences, interests, and values. A principle-based approach, which methodically integrates these elements, is advocated as advantageous. The research warns against overly simplistic interpretations, such as literal alignment with explicit instructions, highlighting the risks of unintended harmful outcomes as illustrated by the King Midas problem.
3. Focus on Fair, Endorsed Moral Principles: Instead of identifying 'true' moral principles, the paper argues for the identification of moral principles that can be reflectively endorsed despite widespread moral disagreement. The paper presents three methods for deriving such principles: global public morality grounded in human rights, hypothetical agreement models like Rawls' veil of ignorance, and social choice theory.

Implications and Future Speculations

The paper's findings have numerous implications. Practically, this research calls for an interdisciplinary approach in the development of AI that integrates ethical reasoning with technical innovation. This reflects a need for a diverse spectrum of societal inputs, ensuring that AI aligns with a broad spectrum of human values while maintaining robust operational effectiveness.

Theoretically, Gabriel’s work prescribes a shift from attempts to discover absolute moral truths to seeking a consensus-driven approach that accommodates human diversity. It suggests that future research should focus on developing methodologies for achieving an 'overlapping consensus,' bypassing metaphysical disputes over the existence of objective values.

Furthermore, the paper highlights that the methodologies employed in AI development can shape the range of moral principles that can feasibly be encoded, indicating that flexibility and openness in design are essential to accommodate evolving ethical standards.

Conclusion

Gabriel’s paper constitutes a significant contribution to the discourse on AI ethics, as it calls for a nuanced approach to value alignment that concurrently respects normative diversity and meets technical demands. By advocating a combination of global consensus practices, hypothetical agreement principles, and mechanisms from social choice theory, this work invites ongoing dialogue in the AI research community about the ethical direction of AI development.

As AI continues to evolve, understanding the interplay between moral philosophy and technical design will be essential. This paper provides a foundational framework for those engaged in the challenging work of ensuring that intelligent systems are aligned not merely with some static conception of human values but with an adaptable, ethically sound consensus reflective of ongoing human development.