31 citations · 67 across the 9 of their papers we have counts for
6 papers · 1 filter
Model-Free RL Agents Demonstrate System 1-Like Intentionality
Hal Ashton, Matija Franklin
This paper argues that model-free reinforcement learning (RL) agents, while lacking explicit planning mechanisms, exhibit behaviours that can be analogised to System 1 ("thinking f…
Beyond Preferences in AI Alignment
Tan Zhi-Xuan, Micah Carroll, Matija Franklin +1
The dominant practice of AI alignment assumes (1) that preferences are an adequate representation of human values, (2) that human rationality can be understood in terms of maximizi…
Concept Extrapolation: A Conceptual Primer
Matija Franklin, Rebecca Gorman, Hal Ashton +1
This article is a primer on concept extrapolation - the ability to take a concept, a feature, or a goal that is defined in one context and extrapolate it safely to a more general c…
Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI
Matija Franklin, Hal Ashton, Rebecca Gorman +1
As artificial intelligence becomes more powerful and a ubiquitous presence in daily life, it is imperative to understand and manage the impact of AI systems on our lives and decisi…
Definitions of intent suitable for algorithms
Hal Ashton
Intent modifies an actor's culpability of many types wrongdoing. Autonomous Algorithmic Agents have the capability of causing harm, and whilst their current lack of legal personhoo…
Extending counterfactual accounts of intent to include oblique intent
Hal Ashton
One approach to defining Intention is to use the counterfactual tools developed to define Causality. Direct Intention is considered the highest level of intent in the common law, a…