Early questions
EthicsNet asked how machines could learn ethical judgement, how safety rules interact with social context, and whether human treatment of synthetic minds belongs inside the alignment problem.
Continuity
From participatory values to bilateral alignment
EthicsNet began by exploring how communities might teach ethical preferences through examples. Its current work retains that concern for plural values while adding constitutional runtimes, governance, diagnostics, welfare, and deployable public infrastructure.
Layered value learning
Early notes explored hybrid top-down and bottom-up systems, explicit constraints, social learning, game theory, uncertainty, and intent.
Personal value alignment
Lead-authored peer-reviewed work described an end-to-end personal fine-tuning framework for AI value alignment.
Runtime and diagnostic frameworks
Peer-reviewed work advanced constitutional agentic alignment and Psychopathia Machinalis.
Public-goods portfolio
EthicsNet presents a coordinated body of tools, protocols, research, preservation work, diagnostics, and education under bilateral alignment.
Archive
Read the original materials in context
EthicsNet and the 3 Laws
Nikola Stojkovic's 2016 notes compare rigid rules with learned ethical judgement and close by asking about robot rights.
EthicsNet and AGI
Nell Watson's 2016 notes argue for peaceful coexistence and warn against supremacist approaches to synthetic intelligence.
Approaches to AI Values
Nell Watson's 2017 notes outline a layered hybrid of explicit norms, learning, socialisation, game theory, and uncertainty.
Dataset era
A concise record of the participatory annotation work, browser extension, and legacy dilemma tool that preceded the current portfolio.