Sure, training data selection impacts the output. If you feed an AI nothing but anime, the images it produces will look like anime. If all it knows is K-pop, then the music it puts out will sound like K-pop. Tweaking a computational process through selective input is not the same as a human being actively absorbing stimuli and forming their own, unique response.

AI doesn’t have an innate taste or feeling for what it likes. It won’t walk into a second hand CD store, browse the boxes, find something that’s intriguing and check it out. It won’t go for a walk and think “I want to take a photo of that tree there in the open field”. It won’t see or hear a piece of art and think “I’d like to be learn how to paint/write/play an instrument like that”. And it will never make art for the sake of making art, for the pure enjoyment that is the process of creating something, irrespective of who wants to see or hear the result. All it is designed to do is regurgitate an intersection of what it knows that best suits the parameters of a given request (aka prompt). Actively learning, experimenting, practicing techniques, trying to emulate specific techniques of someone else - making art for the sake of making art - is a key component to humans learning from others and being influenced by others.

So the comparison between human learning and influencing, and the selective feeding of data to an AI to ‘tune’ its output are entirely different things that cannot and should not be compared.