arXiv subjects

Shang Lu

Publications and source records attributed to Shang Lu.

3 recordsLinked to original sources

arXiv2026

Meta-ethics and AI: exploring the novel meta-ethical questions in the era of AI

With the development of artificial intelligence (AI), the landscape of meta-ethics, which has largely centred on human ethics, faces pressures that may significantly reconfigure it. In particular, if future AI systems were to exhibit sufficiently integrated capacities for moral reasoning, moral intentionality, and moral reflection, novel meta-ethical questions would arise concerning what I call "AI's own ethics", as distinct from ethical principles merely imposed on AI by human designers. This paper offers a conditional and methodological framework for identifying the questions that would emerge if such AI systems were to arise. On that basis, the paper distinguishes four domains of meta-ethical inquiry in the era of AI: questions about the nature of human ethics from the human perspective; questions about the nature of AI's own ethics from the human perspective; questions about the nature of human ethics from the AI perspective; and questions about the nature of AI's own ethics from the AI perspective. The paper then considers how some existing mainstream meta-ethical theories (such as cognitivism and non-cognitivism, error theory and success theory, relativism, and objective realism) might illuminate these domains, while arguing that many familiar human-centred formulations of those theories may not transfer straightforwardly to AI cases without substantial revision. The overall conclusion is that the emergence of AI's own ethics would place significant pressure on current frameworks and may require substantial refinement, reconstruction, or reconceptualisation.

cs.AI↗

arXiv2025

Sample Complexity of Identifying the Nonredundancy of Nontransitive Games in Dueling Bandits

Dueling bandit is a variant of the Multi-armed bandit to learn the binary relation by comparisons. Most work on the dueling bandit has targeted transitive relations, that is, totally/partially ordered sets, or assumed at least the existence of a champion such as Condorcet winner and Copeland winner. This work develops an analysis of dueling bandits for non-transitive relations. Jan-ken (a.k.a. rock-paper-scissors) is a typical example of a non-transitive relation. It is known that a rational player chooses one of three items uniformly at random, which is known to be Nash equilibrium in game theory. Interestingly, any variant of Jan-ken with four items (e.g., rock, paper, scissors, and well) contains at least one useless item, which is never selected by a rational player. This work investigates a dueling bandit problem to identify whether all $n$ items are indispensable in a given win-lose relation. Then, we provide upper and lower bounds of the sample complexity of the identification problem in terms of the determinant of $A$ and a solution of $\mathbf{x}^{\top} A = \mathbf{0}^{\top}$ where $A$ is an $n \times n$ pay-off matrix that every duel follows.

cs.GT↗

arXiv2022

Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation

Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise uncertainty maps of the segmentation and allows an implicit ensemble of segmentations to boost the segmentation performance. However, DDPM requires many iterative denoising steps to generate segmentations from Gaussian noise, resulting in extremely inefficient inference. To mitigate the issue, we propose a principled acceleration strategy, called pre-segmentation diffusion sampling DDPM (PD-DDPM), which is specially used for medical image segmentation. The key idea is to obtain pre-segmentation results based on a separately trained segmentation network, and construct noise predictions (non-Gaussian distribution) according to the forward diffusion rule. We can then start with noisy predictions and use fewer reverse steps to generate segmentation results. Experiments show that PD-DDPM yields better segmentation results over representative baseline methods even if the number of reverse steps is significantly reduced. Moreover, PD-DDPM is orthogonal to existing advanced segmentation models, which can be combined to further improve the segmentation performance.

eess.IV↗