Camila Hernandez Flowerman

Whether or not large language models (LLMs) can be said to have representational attitudes like beliefs (or motivational attitudes like intentions) remains an open question. In this paper I argue that on some commonly accepted views about belief, LLMs, given their structure, are not capable of having beliefs. To do so, I draw from the normativity of belief literature to distinguish three types of views about the kinds of things beliefs are. The first category of view includes those which deny that there are any norms of belief at all, in any sense. The second category of views are those which allow that there might exist, in some way or another, a norm of belief, but hold that this is only true in a trivial or purely conceptual sense. I will call these the mere constitutivist accounts. The third category of views are any that suggest a stronger normative picture for the norm of belief. I’ll refer to these views as normativism about belief. My hope is to show that if either the mere constituivist or normativist accounts about belief are correct, then LLMs are not capable of having attitudes like beliefs or intentions.

In this paper I provide a preliminary account of weakness of political will (political akrasia). My aim is to use theories from the weakness of will literature as a guide to develop a model of the same phenomenon as it occurs in collective agents. Though the account will parallel the traditional view of weakness of will in individuals, weakness of political will is a distinctly political concept that will apply to group agents such as governments, institutional actors, and other political collectives. I further argue that blaming inaction by collective political agents on a “lack of political will” should be understood as blaming a kind of akratic break within the deliberative process, leading to an inability of the collective agent to capitalize on existing group attitudes in order to translate them into policy or legislation. My primary example throughout will be U.S. political inaction with respect to the climate crisis.

In this paper I argue that some examples of what we label ‘algorithmic bias’ would be better understood as cases of institutional bias. Even when individual algorithms appear unobjectionable, they may produce biased outcomes given the way that they are embedded in the background structure of our social world. Therefore, the problematic outcomes associated with the use of algorithmic systems cannot be understood or accounted for without a kind of structural account. Understanding algorithmic bias as institutional bias in particular (as opposed to other structural accounts) has at least two important upshots. First, I argue that the existence of bias that is intrinsic to certain institutions (whether algorithmic or not) suggests that at least in some cases, the algorithms now substituting as pieces of institutional norms or rules are not “fixable” in the relevant sense, because the institutions they help make up are not fixable. Second, I argue that in other cases, changing the algorithms being used within our institutions (rather than getting rid of them entirely) is essential to changing the background structural conditions of our society.