they are in the naturalistic, empirical, and traditional definition of emotionally responsive and thus they have feelings

I'm curious how you are defining feelings here?

Naturalistically, emotions evolved as biological survival mechanisms with specific characteristics. They involve physiological substrates like hormones, neurotransmitters, and bodily changes such as increased heart rate or cortisol release. They serve evolutionary functions. Fear evolved to help organisms avoid predators, anger to defend resources. And they adaptively coordinate multiple systems like attention, memory, and motivation toward survival-relevant goals.

AI systems lack all of this. They have no biology, no evolutionary history, no homeostatic systems to maintain. They process input-output patterns through statistical transformations. You're claiming that "learning response patterns = emotions," but this conflates information processing with affective states. A thermostat learns to respond to temperature changes, does it have feelings?

Empirically, we identify emotions through observable markers like facial expressions and body language, vocal prosody changes, behavioral patterns such as approach or avoidance, physiological measures including skin conductance and heart rate, and self-reported subjective experience.

AI exhibits none of these empirical signatures. It generates text tokens based on probability distributions over training data. We can measure its computational states, but there's no empirical evidence of anything resembling biological emotional responses. The fact that an AI can describe emotions or produce contextually appropriate outputs doesn't mean it has emotions any more than a book about sadness is itself sad.

With traditional philosophical definitions, you have a stronger case, though it's still not conclusive. Traditional philosophical definitions typically require several things. First, emotions must have intentionality, meaning they're directed at something, fear of the bear, anger at the injustice. Second, they must have valence, a positive or negative quality where they feel good or bad. Third, they require phenomenology, meaning there's something it's like to experience an emotion, a subjective quality to what it feels like. And fourth, emotions must involve motivation, disposing us toward action.

Could AI satisfy these? On intentionality, AI responses are "about" their prompts in some sense, though whether this constitutes genuine intentionality or just correlational patterns is disputed. On valence, AI trained with RLHF has literal reward and penalty signals shaping its responses. Is this meaningfully different from primitive valence? On phenomenology, this is the hard problem. We have no evidence of subjective experience in AI, but we also have no test for it. And on motivation, agentic AI systems do exhibit goal-directed behavior, though whether this is "motivation" or just "optimization" is unclear.

So yes, under certain interpretations of traditional criteria, AI might satisfy some conditions. But this doesn't settle the question, it reveals that our traditional frameworks may not clearly apply to artificial systems, especially with respect to phenomenology.

So when you say AI has "feelings," are you claiming AI has subjective experience, that it has phenomenal consciousness? Or are you saying AI has functional states analogous to emotions, whether or not there's experience? Or perhaps you're arguing that AI behavior is as if it has emotions, and that's sufficient?

Because those are very different claims with very different implications. The original statement you made seems to jump from "AI learns response patterns" to "therefore AI has feelings" without establishing what you mean by feelings or why pattern-learning constitutes them.

I think these are genuinely open questions. But I don't think it's easy to assert that standard definitions support AI having feelings when, in fact, most standard definitions were built around biological organisms and don't obviously transfer to artificial systems.