Are intuitions about moral relevance susceptible to framing effects?

Various studies have reported that moral intuitions about the permissibility of acts are subject to framing effects. This paper reports the results of a series of experiments which further examine the susceptibility of moral intuitions to framing effects. The main aim was to test recent speculation that intuitions about the moral relevance of certain properties of cases might be relatively resistent to framing effects. If correct, this would provide a certain type of moral intuitionist with the resources to resist challenges to the reliability of moral intuitions based on such framing effects. And, fortunately for such intuitionists, although the results can’t be used to mount a strident defence of intuitionism, the results do serve to shift the burden of proof onto those who would claim that intuitions about moral relevance are problematically sensitive to framing effects

Plex: Towards Reliability using Pretrained Large Model Extensions

A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. Probing these models' abilities in diverse ways is therefore critical to the field. In this paper, we explore the reliability of models, where we define a reliable model as one that not only achieves strong predictive performance but also performs well consistently over many decision-making tasks involving uncertainty (e.g., selective prediction, open set recognition), robust generalization (e.g., accuracy and proper scoring rules such as log-likelihood on in- and out-of-distribution datasets), and adaptation (e.g., active learning, few-shot uncertainty). We devise 10 types of tasks over 40 datasets in order to evaluate different aspects of reliability on both vision and language domains. To improve reliability, we developed ViT-Plex and T5-Plex, pretrained large model extensions for vision and language modalities, respectively. Plex greatly improves the state-of-the-art across reliability tasks, and simplifies the traditional protocol as it improves the out-of-the-box performance and does not require designing scores or tuning the model for each task. We demonstrate scaling effects over model sizes up to 1B parameters and pretraining dataset sizes up to 4B examples. We also demonstrate Plex's capabilities on challenging tasks including zero-shot open set recognition, active learning, and uncertainty in conversational language understanding.Comment: Code available at https://goo.gle/plex-cod

Inappropriate stereotypical inferences? An adversarial collaboration in experimental ordinary language philosophy

This paper trials new experimental methods for the analysis of natural language reasoning and the (re)development of critical ordinary language philosophy in the wake of J.L. Austin. Philosophical arguments and thought experiments are strongly shaped by default pragmatic inferences, including stereotypical inferences. Austin suggested that contextually inappropriate stereotypical inferences are at the root of some philosophical paradoxes and problems, and that these can be resolved by exposing those verbal fallacies. This paper builds on recent efforts to empirically document inappropriate stereotypical inferences that may drive philosophical arguments. We demonstrate that previously employed questionnaire-based output measures do not suffice to exclude relevant confounds. We then report an experiment that combines reading time measurements with plausibility ratings. The study seeks to provide evidence of inappropriate stereotypical inferences from appearance verbs that have been suggested to lie at the root of the influential ‘argument from illusion’. Our findings support a diagnostic reconstruction of this argument. They provide the missing component for proof of concept for an experimental implementation of critical ordinary language philosophy that is in line with the ambitions of current ‘evidential’ experimental philosophy