Epistemic deference (ED) is the practical and rational appeal to another agent as an epistemic authority, whose authority stems from pre-established legitimate expertise, experience, access to relevant evidence, and dependable systems of knowledge. However, within standpoint epistemology, there is a pushback against this norm. Olufemi Taiwo voices this resistance. In this paper, I critically examine Taiwo’s account of ED, which is characterised by conferring conversational authority and attentional goods to individuals based on superficial social identity markers to represent the marginalised. Accordingly, I argue that there is a fundamental definitional difference between Taiwo’s account of ED and how it is accounted for in epistemology. Based on this distinction, I contend that the failures attributed to Taiwo’s account — namely, that ED leads to epistemic complacency and the reinforcement of oppressive systems — are not inherent flaws of ED as an epistemic norm. Rather, they stem from Taiwo’s conceptualisation. I conclude by considering ED beyond elite spaces, demonstrating that it is indispensable so long as epistemically privileged standpoint is rooted in experience-based knowledge.
Monday July 6, 2026 11:00am - 11:55am AEST Steele-262
What, if anything, can we come to know about other people through virtual embodiment? Critics of virtual reality (VR) which virtually embodies users as marginalised persons argue such experiences inevitably misrepresent marginalised lives, encourage epistemic overconfidence in users, and reduce complex social identities to decontextualised simulations. While these concerns are valid, I argue that they do not exhaust the epistemic possibilities of VR. Drawing on empirical studies and philosophical analysis, I present a positive account of what we can come to know about others through VR-mediated perspective-taking.
Focusing on a subclass of prosocial behaviour-promoting virtual reality, I argue that certain VR experiences can confer propositional, practical, and what I call grounded inferential knowledge of marginalised experiences. Grounded inferential knowledge refers to a user's ability to learn about their own affective and bodily responses to virtual harms, and to correctly infer—on that basis—how those same harms might feel to others in real life. While VR cannot give us direct access to others’ experiences, it can function as a scaffold for more accurate, situated, and reflective understanding of shared human vulnerabilities. By reframing the epistemic stakes of virtual embodiment, this account offers a more nuanced framework for evaluating the promise and limits of VR as an ethical and educational tool.
Monday July 6, 2026 12:00pm - 12:55pm AEST Steele-262
Generative AI language models are increasingly being positioned as epistemic tools, used to aid enquiry and generally help us find things out. However, they suffer from certain flaws which both limit their usefulness as epistemic tools and risk causing epistemic harm. While AI bias and hallucinations have been written about as being epistemically harmful, an underexplored trait is that of sycophancy. Sycophantic AI models produce outputs which match user beliefs over truthful ones. This draws parallels with other forms of algorithmic feed-back loops and epistemic bubbles which can limit the user's ability to see beyond their own perspective and to acquire knowledge. The trait of sycophancy in AI has been attributed to stages in the training process where models learn from human feedback to reflect user preferences. This work further sketches a possible application of vice epistemology to language models. It does so, not by giving agency to these models, but by looking at whether aggregating human preferences in the training process can manifest a kind of collective epistemic vice. I will ask whether epistemically harmful character traits, arising from collective training process, can meaningfully qualify as (non-agential) epistemic vices of AI.
Monday July 6, 2026 2:00pm - 2:55pm AEST Steele-262
Can virtual reality help us to “walk in the shoes” of other people? Optimists claim that VR is the “ultimate empathy machine”, a way for those who have never been to war, or lived in solitary confinement, to know what it is like to have these experiences from the comfort of their living room. Pessimists hold that it is absurd and dangerous to think that VR could be a way of acquiring this kind of ‘what it is like’ knowledge. In this paper I develop a position which can accommodate the important insights of the pessimist’s critique, whilst also allowing us to agree with (a qualified version of) the optimist’s claim that VR can help us to acquire ‘what it is like’ knowledge. This position is based on recent work I have done showing how ‘what it is like’ knowledge comes in different grades and degrees.
Monday July 6, 2026 3:00pm - 3:55pm AEST Steele-262
I introduce an analytical framework for the fine-grained study of generative AI bias, applying the notion of social marking. When we say that a feature is socially marked, we mean that it stands out as unusual or noteworthy within a given social context, and that it prompts special treatment, for good or ill. I propose that much of the homogeneity of generative AI outputs results from the AI systems codifying that certain demographics and their features are marked, and in response to marked features AI systems will change the demographics it portrays, sometimes radically so. As a result, these systems will over-represent non-marked, dominant groups in the absence of certain features, and over-represent marked, non-dominant groups in the presence of such features. There are positive and negative forms of bias that result. The positive bias, where AI systems have stereotypical outputs, has attracted the most attention. But the negative bias is also noteworthy: some demographics are typically left out of depictions unless there is some marked feature in play, meaning that these systems selectively omit non-dominant groups in contexts where they should be visible.
Monday July 6, 2026 4:30pm - 5:25pm AEST Steele-262
