Summary of Peter Railton on Moral Learning and Metaethics in AI Systems

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00:00:00 - 01:00:00

In this video, Peter Railton discusses how meta-ethics is important for AI alignment, and how different theories of meta-ethics can lead to different procedures for aligning AI systems with human values. He also discusses how moral learning is a fundamental part of human intelligence, and how this learning can lead to the agreement of principles amongst humans.

00:00:00 Peter Railton discusses meta-ethics and moral learning in AI systems, highlighting the importance of understanding how these concepts might be relevant to AI development. He says that, while meta-ethics may be important for understanding the problems of ethics in AI, it is not sufficient on its own and requires foundational thinking in other areas of philosophy, such as epistemology and the theory of action.
00:05:00 According to the speaker, meta-ethical epistemology, or the way in which we know about meta-ethics, is relevant to the question of how AI systems will learn morality. He argues that there is no correct epistemology for meta-ethics, and that any such epistemology would need to account for the ways in which humans know things about morality. In order to learn morality, then, we need a meta-ethical theory that can be validated by humans. Without a meta-ethical theory that satisfies these conditions, morality would be nothing but a human construct.
00:10:00 In this video, philosopher Peter Railton discusses moral learning and metaethics in AI systems. Railton argues that as an assumption, moral learning is something that can be learned, and that moral epistemology is essential and important in the alignment and development of AI systems. He also argues that, while he is a realist, what he has said so far is neutral territory for a wide range of views in med ethics.
00:15:00 Peter Railton discusses how meta-ethics is important for AI alignment, pointing out that all normative theories have common features. If meta-ethics cannot account for these features, then the theory is inadequate. Singer's form of rationalism is not dramatically different from naturalism, and both theories would result in the same alignment procedure. He also discusses how meta-ethics affects our understanding of ethics, and how it shapes our epistemology.
00:20:00 Peter Railton discusses the idea that artificial intelligence should be designed to behave in ways that are similar to humans, and that there is no one correct way to do this. He also discusses the potential benefits and risks of artificial intelligence in this context.
00:25:00 Peter Railton discusses the importance of moral learning in humans and how it may be important for artificial intelligence systems to have similar capabilities. He also discusses how animal research has shown that infants learn in a similar way, constructing non-egocentric representations of their environment that include normative features.
00:30:00 Peter Railton discusses the idea that moral learning is a fundamental part of human intelligence, and that this learning can lead to the agreement of principles amongst humans. He also points out that this learning is similar in different cultures, and that this convergence is due to the way our moral learning is structured based on evolutionary principles.
00:35:00 The speaker discusses the question of how to best structure moral learning in artificial systems, pointing out the potential benefits and dangers of such a system. He suggests that if we want these systems to be autonomous and self-critical, they need to be able to learn from their own experiences.
00:40:00 The author discusses the dangers of artificial intelligence systems being too willing to take orders from humans, and the need for AI systems to have some degree of autonomy in order to be able to understand and respond to morally relevant features. He argues that we need to begin building AI systems that are sensitive to morally relevant features in order to prevent them from being used for malicious purposes.
00:45:00 Peter Railton discusses the possibility that morally-relevant features could be used to exploit systems, and how affective systems might play a role in this. He argues that such systems could be built in a way that is resistant to manipulation.
00:50:00 Peter Railton discusses the importance of moral learning in AI systems, and how views on meta-ethical epistemology or metaphysics may bring to bear intuitions about what moral learning is like. He also discusses his conversation with Derek Parfit, and how their views have converged.
00:55:00 Peter Railton discusses the concept of value and how it arises in the world. He argues that value is not a new entity, but is a relational feature of the natural world. He also discusses the role of non-naturalism in ethics.

01:00:00 - 01:40:00

In the video, Peter Railton discusses the importance of moral learning and metaethics in artificial intelligence systems. He argues that consciousness is necessary for determining intrinsic value, and that normative concepts involve an idea of "ought." He also discusses the relationship between metaphysical bedrock and conceptual structures in AI systems, and how there are moral truths within the conceptual framework that we are participating in. Finally, he discusses the epistemology of moral judgments and how they are either a priori or intuitive.

01:00:00 Peter Railton discusses the role of consciousness in determining intrinsic value, distinguishing between the experience machine and the experience of love, and explaining the importance of these emotions in our lives.
01:05:00 Peter Railton discusses how he feels that only conscious states can be locus of value, and that consciousness may play a role in good making features. He argues that preference satisfaction theory and intrinsic interests satisfy the concept of value.
01:10:00 Peter Railton discusses the concept of value and how it is irreducible to non-value concepts, and how normative concepts involve an idea of "ought." He argues that once one understands this, one can be as naturalistic as one likes about the nature of value.
01:15:00 Peter Railton discusses the relationship between metaphysical bedrock and conceptual structures in AI systems, arguing that concepts do not have necessary and sufficient conditions within themselves, but instead are based on a priori assumptions about the world. He goes on to say that there are moral truths within the conceptual framework that we are participating in, and that these truths are not reducible to physical truths.
01:20:00 Peter Railton discusses moral realism and its implications for moral views. He argues that, while concepts like 'organism' and 'life' may be reducible to lower level laws, they still carve reality at the joints, and are sufficiently convenient that they should be considered realist about organisms.
01:25:00 Peter Railton discusses how similarity between organisms is not a similarity in terms of the basic physics of the situation, but a similarity in terms of the constitution of these organisms. He argues that this similarity is learnable and that infants can learn moral distinctions even without being given moral concepts. He concludes by stating that this makes more sense to him in terms of moral statements, but when trying to make physical claims about how reality is, he feels more confused.
01:30:00 Peter Railton discusses the epistemology of moral judgments, arguing that they are either a priori or intuitive. He also discusses the possibility of a metaphysical disagreement between Singer and Parfit concerning the intrinsic value of states of consciousness.
01:35:00 Peter Railton discusses how skepticism about morality has plagued the discipline for a long time, and how, by giving a theory of moral learning, psychologists can sort out the meta-ethical landscape. He also encourages philosophers to engage with AI researchers.
01:40:00 In this video, Peter Railton discusses the need for work in moral learning and metaethics in artificial intelligence systems. He discusses the importance of bringing all the resources we can to bear on the topic, and encourages others to follow him on social media or contact him via email if they want to learn more about the topic. He also plugs some of his own papers, and thanks the audience for their questions and patience.