Unless otherwise noted, all seminars will be held from 13:00–14:00 in Room 113 in the Philosophy Department’s building at 17 Wally's Walk and on Zoom at this link.
The seminars are followed by afternoon tea with the speaker, seminar attendees, and other members of the Philosophy Discipline.
Sally Haslanger (1995) influentially distinguished between causal and constitutive varieties of social construction. According to her, something is socially causally constructed iff social factors play a causal role in bringing it into existence or, to some substantial extent, in its being the way it is. But setting things issue up like this makes pretty much everything in recorded history into a social construction because almost everything has some social causes. I think this makes the idea of social construction uninformative. I will consider some ways of differentiating between social construction proper and the effects of social processes considered more generically. I will also make a case for recognising a further category: social meaning, seen as the interpretation by a culture of existing phenomena regardless of their causes. So just as a natural phenomenon can be open to interpretation, something can be both a social construction and also open to further interpretation and contest
[No meeting]
Suppose a language model and human subjects both achieve high performance on a test designed to measure a given cognitive ability (e.g., analogical reasoning or theory of mind). Should we conclude that they both have that ability to the same degree? Performance data alone can’t settle this question. Cognitive evaluation is an inverse problem: the target ability is a latent variable that is not directly observed, and many hidden causes can produce the same pattern of performance. Memorization, surface cues, and response bias can enable good performance without the target ability, while auxiliary task demands or measurement error can result in observed failure despite the ability. We propose a Bayesian framework to formalise the evaluation of cognitive capacities in humans, animals, and machines. In this framework, a pattern of performance provides evidence only through a comparison. The observed result supports attributing an ability to the extent that it was more expected under a predictive model of the subject with the ability than under a model of the subject without it. This ratio (the Bayes factor) depends on modelling credible routes to success and failure under each hypothesis, and comes apart from prior beliefs about whether subject has the ability. It follows that the same pattern of evidence can carry different evidence for different subjects. This framework explains what makes a cognitive test diagnostic, and how to choose which experiment to perform next as function of the uncertainty it is expected to remove about the subject. In particular, it suggests that the relative evidential weight of behavioural and mechanistic experiments is quite different for human subjects and for artificial neural networks. Beyond the framework’s value as a regulative ideal for comparative cognitive science, we review some of its practical implications for experimental design.
Modern AI systems can produce works which at least seem as creative as those produced by humans. It is therefore unsurprising that the question of whether AI systems can be genuinely creative has emerged. A common approach is to appeal to the absence of distal intentions in order to reject AI creativity, but we argue that the activation and fulfilment of distal intentions is neither necessary for creativity, nor sufficient for the type of intentionality needed for genuine creativity. Instead, we develop an alternative focus on proximal intentions, the activation and fulfilment of which is necessary for genuine creativity, and sufficient for the type of intentionality needed for genuine creativity. Further, we show that the relevant proximal intentions need not be held overtly in the mind during the creative process but can also be held subdoxastically. Finally, we argue that AI systems do not possess proximal intentions and therefore cannot be genuinely creative.
Colombetti (2005) has challenged the standard dichotomous conceptualisation of affective valence (pleasure/pain: Russell 1980, good/bad: Kahneman 1999) showing that emotional experience is much more complex than the classical contrasting of polarised hedonic feelings implies. Drawing on the enactive approach to embodied cognition, I expand the investigation into tensional valence as emotional friction. By emotional friction, I mean a tension between activity and passivity while facing a situation that does not reply to one’s values and existential commitments (Candiotto 2023a, forthcoming, Candiotto & Tempone-Wilthshire, forthcoming). Emotional friction has been introduced in the debate by José Medina (2019) in the context of racial violence and active ignorance. He argued that not just epistemic friction, i.e., disagreements about worldviews and conflicts about beliefs, but emotional friction can be catalysts for “epistemic activism”, i.e., challenging and transforming harmful knowledge practices, also by disrupting complacency. I maintain that to fully appreciate the transformative role of emotional friction, we need to further develop the discussion into its phenomenal character. By diving deeper into emotional friction as a felt sense of vulnerability, I shed light on the tensional valence that is immanent to bodily feelings. My claim is that emotional friction motivates and constitutes sense-making and, so, has a crucial ethical value in character revision responsibility.
Biologists define sex by ‘anisogamy’ - “The biological definition of the two sexes is simple: biologically, the female sex is defined as the adult phenotype that produces the larger gametes in anisogamous systems, while the male sex is the adult phenotype that produces the smaller gametes.” (de Vries and Lehtonen 2023, 928). After noting some ways in which this definition has been misunderstood in the philosophical literature, this paper defends three claims about biological sexes defined by anisogamy. First, sexes are life-history stages: collections of phenotypic traits which implement a stage in an evolved life-history strategy. This is most obvious in sequential hermaphrodites, but is equally true of species that only develop one sex during their life cycle. Second, sex-determination is a process. The operation of a ‘sex determination mechanism’ does not instantly create a sexual phenotype. Sex determination mechanisms initiate developmental processes that result in the development of one or more sexual phenotypes later in the life-cycle. Third, because sex determination is an extended developmental process many factors can interfere with it, and some biological individuals do not follow the standard patterns of sexual development in their species. In some cases there is no strictly biological rationale for describing the resulting ‘idiosyncratic’ phenotype as male, female or hermaphrodite. Biology can explain why development has departed from a typical life-cycle but when asked for the ‘real’ biological sex of the individual, biology should answer ‘it is what it is’.
[No meeting - Mid-semester break]
[No meeting - Mid-semester break]
[No meeting]
Good sex – sex that is consensual, welcome, fulfilling, and conducive to one’s flourishing – is both an ethical issue and an issue of justice (Gavey 2005). In this talk I develop two concepts, ‘mutual regard’ and ‘sexual fluency,’ which I take to be promising for thinking through what constitutes good sex and its conditions of possibility. Developing these concepts and exploring how they interrelate brings into focus the consequential (yet oft-overlooked) interplay between imagination, affect, institutions, and wider social imaginaries. I show how this interplay can constrain and enable different groups of sexual actors in highly uneven and problematic ways, and how it troubles existing proposals for scaffolding good sex.
I propose a novel account for why the outputs of LLMs are meaningful that is inspired by Lewis’s (1975) work on convention. The account does not require LLMs to have any mental states, such as intentions or beliefs. The account also avoids the problems associated with knowledge-first approaches such as Cappelen and Dever’s (2021) approach; problems resulting from the peculiar features of LLMs– such as high enough hallucination rates and various token sampling protocols.
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