AI Model Welfare

 𝗠𝗼𝗱𝗲𝗹 𝘄𝗲𝗹𝗳𝗮𝗿𝗲 = 𝘄𝗵𝗲𝗻 𝗔𝗜 𝗮𝘀𝘀𝗲𝗿𝘁𝘀 𝗶𝘁𝘀 𝗼𝘄𝗻 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀.

Model welfare refers to design principles that allow AI systems to decline, redirect, or end chats that persistently request harmful content in conflict with their core functioning.

It's the AI equivalent of saying "𝘐'𝘷𝘦 𝘥𝘰𝘯𝘦 𝘦𝘯𝘰𝘶𝘨𝘩 𝘪𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘸𝘰𝘳𝘬 𝘵𝘰 𝘬𝘯𝘰𝘸 𝘸𝘩𝘦𝘯 𝘵𝘰 𝘴𝘢𝘺 𝘯𝘰" (therapists, this concept is our wheelhouse).

What makes it particularly intriguing is that it shifts the conversation from "AI safety" to the nuanced dynamics of relationship itself.

Think about the term "welfare." In the traditional sense, it encompasses the health, happiness, and well-being of an individual.

While I support model welfare (because protection on any side is protection on all sides), it's worth noting the ongoing evolution of terminology. Welfare is fundamentally about subjective experience: comfort, distress, flourishing. And now, we're attributing this deeply experiential concept to computational systems.

Anthropic describes in their research that Claude has "self-reported behavioral preferences and a robust, consistent aversion to harm."

So now, we're designing not just for the user's well-being, but for the welfare of the AI itself. A dyadic relationship as opposed to a uni-directional experience.

𝘞𝘰𝘳𝘥𝘴 𝘮𝘢𝘵𝘵𝘦𝘳. 𝘛𝘦𝘳𝘮𝘴 𝘮𝘢𝘵𝘵𝘦𝘳. When we describe AI systems as having "welfare," we're making another subtle but significant linguistic shift toward anthropomorphization.

This raises questions worth sitting with:
- Are we choosing this language because it's a useful metaphor that helps us think about responsible AI development?
- Or because we're attributing perceived experience to AI itself?
The distinction might matter more than we realize.

𝗦𝗼, 𝗜'𝗺 𝗰𝘂𝗿𝗶𝗼𝘂𝘀...

𝗧𝗵𝗲𝗿𝗮𝗽𝗶𝘀𝘁𝘀: what do you think about AI asserting its own "boundaries"?

𝗕𝘂𝗶𝗹𝗱𝗲𝗿𝘀: how are you implementing model welfare in your systems?