Search Results for author:
Found 10 papers, 1 papers with code
Deep Learning and Ethics
This article appears as chapter 21 of Prince (2023, Understanding Deep Learning); a complete draft of the textbook is available here: http://udlbook. com.
The Linguistic Blind Spot of Value-Aligned Agency, Natural and Artificial
I discuss the consequences that the truth of this claim would have for research programmes that attempt to ensure value alignment for AI systems; or, more loftily, designing robustly beneficial or ethical artificial agents.
Metaethical Perspectives on 'Benchmarking' AI Ethics
In this paper, drawing upon research in moral philosophy and metaethics, we argue that it is impossible to develop such a benchmark.
Moral Dilemmas for Moral Machines
Autonomous systems are being developed and deployed in situations that may require some degree of ethical decision-making ability.
Est-ce que vous compute? Code-switching, cultural identity, and AI
Cultural code-switching concerns how we adjust our overall behaviours, manners of speaking, and appearance in response to a perceived change in our social environment.
Emergent Communication under Competition
First, we show that communication is proportional to cooperation, and it can occur for partially competitive scenarios using standard learning algorithms.
The Tragedy of the AI Commons
These are supposed to guide the socially-responsible development of AI for the common good.
Learning from Learning Machines: Optimisation, Rules, and Social Norms
There is an analogy between machine learning systems and economic entities in that they are both adaptive, and their behaviour is specified in a more-or-less explicit way.
Biology and Compositionality: Empirical Considerations for Emergent-Communication Protocols
This has theoretical implications for language origins research more generally, but the focus here will be the implications for research on emergent communication in computer science and machine learning---specifically regarding the types of programmes that might be expected to work and those which will not.
Selfish Emergent Communication
Current literature in machine learning holds that unaligned, self-interested agents do not learn to use an emergent communication channel.
