Upcoming Event

Abstract: Despite its importance, the concept of algorithmic transparency has yet to be fully explicated. By asking what is transparent or opaque, we propose a comprehensive framework dividing transparency into four forms: use transparency, which discloses algorithm goals and uses; data transparency, which informs sources, processing, and data quality; model transparency, which explains how the […]

Abstract: Artificial intelligence (AI) systems are increasingly capable of learning from and mimicking individuals, as demonstrated by a fairly successful effort to replicate the attitudes and behaviors of individuals by generative AI with a 2-hour interview (see, Park et al. 2024). This technical advancement has afforded the creation of increasingly indistinguishable (online, digital) doubles of

Abstract: According to the doctrine of recurrence, we are reborn after our apparent death to live our life again. This paper develops a new  doctrine of recurrence. We make three main claims. First, we argue that  the simulation hypothesis increases the chance that we will recur. Second, we argue that the chance of recurrence affects

Abstract: Deep neural networks are known to construct internal representations to process and generalize information. Understanding the structure of these representations is crucial not only for improving machine learning models but also for aligning them with human cognitive representations—namely, the concepts we use in everyday reasoning and scientific inquiry. This study examines how mathematical frameworks

Abstract: While it is well-known that AI systems might bring about unfair social impacts by influencing social schemas, much attention has been paid to instances where the content presented by AI systems explicitly demeans marginalized groups or reinforces problematic stereotypes. This paper urges critical scrutiny to be paid to instances that shape social schemas through subtler manners. Drawing from

Abstract: Making AI explainable has become an increasingly urgent demand, and one of the popular approaches is to generate so-called counterfactual explanations. In this talk, I attempt a philosophical critique of the key aspects of this approach, and raise an important challenge arising from a tension between two constraints on a good counterfactual explanation in

Abstract: To avoid predicting that certain kinds of compulsions contribute to welfare, many desire satisfaction theorists draw a distinction between genuine desires and other desires. Such theorists hold that it is only the satisfaction of genuine desires that contributes to welfare; other desires do not count. From the perspective of AI welfare, then, we must

Abstract: With rapid advances in the social and cognitive capabilities of AI, questions concerning their consciousness and moral status are becoming increasingly pressing. In this talk, I contrast two radically different approaches—Deep and Shallow—to addressing these issues. Deep approaches dominate mainstream philosophy of mind and consciousness science, viewing consciousness as an empirically discoverable natural kind

Abstract: Around 40% of philosophers believe that future AI systems will be conscious (Bourget and Chalmers 2023). Others have argued that the realization of phenomenal consciousness in conventional computer hardware is infeasible, nomologically impossible, or even metaphysically impossible (Block 2009; Godfrey-Smith 2016; Searle 1992; Shiller 2024; Tononi and Koch 2015). Whether or not the AI systems

Abstract: Late in his career, Daniel Dennett changed his mind about minds. If his new view is correct, it has deep implications for both the moral standing of nascent AIs and for the existential danger they may pose. He suggests in his (2017) that genuine minds require a different kind of architecture than he previously