Joseph Benjamin Karaganis

A Sentimental Education… For Robots: Incommensurability and the Bounds of Artificial Reason. This paper compares and evaluates two distinct proposals for the ‘alignment’ of sophisticated AI models: Peter Railton’s ‘ethical learning’ approach and Ruth Chang’s argument that humans should be ‘put in the loop’ when AI agents face ‘hard choices’. I suggest that while both of these strategies have their limitations, they each address an important piece of the broader alignment puzzle, and do so in complementary ways. Thus, an alignment approach that combines the two—and preserves their most important insights—is likely to be more successful than either pursued individually.

In Defense of Bibliotechnism: Immoderate Interpretationism in the Philosophy of AI. This paper defends ‘Bibliotechnism’—the view that all LLM-produced text is semantically-derivative, and therefore, that LLMs lack intentional attitudes. While some have argued that LLMs can create ‘novel references’, I suggest that these are actually derivative with respect to the model’s original dataset. I further claim that even if LLMs could generate original semantic content, this would not warrant the attribution of intentionality.