Notes on: appropriateness and generative artificial intelligence
The paper's challenge is to lay down a definition of appropriateness: how should an individual act in an environment? In this paper, Leibo et al. start from the perspective that modelling the behaviour of AIs and humans is mutually instructive.
Introduction
- Fundamentally, how can we live together?
- How do humans make decisions? The paper focuses on the mechanism of predictive pattern completion.
- Facets of appropriateness: context dependence, arbitrariness, automaticity, dynamism, its relation to sanctioning.
- How does appropriateness relate to the alignment problem? We aren't working with tabula rasa RL systems, so the frame problem is mostly irrelevant in this context.
- We collectively have a "thick" morality (Walzer 1994). It's difficult to distinguish between a hypothetical "core" and the "peripheral" aspects. Instead of trying to identify the "thin" rational core, it is perhaps more productive to understand how we can live together anyway.
- Instead we tend to think about epistemic norms in communities. We might not be happy with a long string of conditionals - it allows self-serving, confirmatory biases to seep into an argument.
- We want the conditions for collective flourishing.
Contextualisation via specialisation
- Specialisation is needed for AI appropriateness, not capability? Does the curse of dimensionality apply to the sampling problem?
- Technical capabilities of LLMs are entirely unaffected by choice of interface. But this feature changes appropriateness requirements.
Interfaces
- Polycentric governance: the mechanism of sanctioning / feedback buffers cooperativity.
- Hierarchical governance.
Stylised facts to be explained by this account of appropriateness
- Context dependence: situation, role and identity, culture,
- Arbitrariness: frequency of occurrence has a large effect on our preferences. Historical contingence.
- Automaticity.