Resource-Rational Virtual Bargaining for Moral Judgment: Toward a Probabilistic Cognitive Model.

Recent theoretical work has argued that moral psychology can be understood through the lens of "resource rational contractualism." The view posits that the best way of making a decision that affects other people is to get everyone together to negotiate under idealized conditions. The outcome of that negotiation is an arrangement (or "contract") that would lead to mutual benefit.

Resource-rational contractualism: A triple theory of moral cognition.

It is widely agreed upon that morality guides people with conflicting interests towards agreements of mutual benefit. We therefore might expect numerous proposals for organizing human moral cognition around the logic of bargaining, negotiation, and agreement. Yet, while "contractualist" ideas play an important role in moral philosophy, they are starkly underrepresented in the field of moral psychology.

Climate sensitivity and relative humidity changes in global storm-resolving model simulations of climate change.

The climate simulation frontier of a global storm-resolving model (GSRM; or -scale model because of its kilometer-scale horizontal resolution) is deployed for climate change simulations. The climate sensitivity, effective radiative forcing, and relative humidity changes are assessed in multiyear atmospheric GSRM simulations with perturbed sea-surface temperatures and/or carbon dioxide concentrations. Our comparisons to conventional climate model results can build confidence in the existing climate models or highlight important areas for additional research.

When rules are over-ruled: Virtual bargaining as a contractualist method of moral judgment.

Rules help guide our behavior-particularly in complex social contexts. But rules sometimes give us the "wrong" answer. How do we know when it is okay to break the rules? In this paper, we argue that we sometimes use contractualist (agreement-based) mechanisms to determine when a rule can be broken.

Judgment aggregation, discursive dilemma and reflective equilibrium: Neural language models as self-improving doxastic agents.

Neural language models (NLMs) are susceptible to producing inconsistent output. This paper proposes a new diagnosis as well as a novel remedy for NLMs' incoherence. We train NLMs on synthetic text corpora that are created by simulating text production in a society.

Shades of confusion: Lexical uncertainty modulates ad hoc coordination in an interactive communication task.

There is substantial variability in the expectations that communication partners bring into interactions, creating the potential for misunderstandings. To directly probe these gaps and our ability to overcome them, we propose a communication task based on color-concept associations. In Experiment 1, we establish several key properties of the mental representations of these expectations, or lexical priors, based on recent probabilistic theories.

Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature.

On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering systems that can assist researchers in finding relevant research on COVID-19. As of May 27, 2020, CORD-19 includes more than 100,000 open access publications from major publishers and PubMed as well as preprint articles deposited into medRxiv, bioRxiv, and arXiv. Recent years, however, have also seen question-answering and other machine learning systems exhibit harmful behaviors to humans due to biases in the training data.

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic.

Text mining approaches for dealing with the rapidly expanding literature on COVID-19.

More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload.

Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project.

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them.

Exploration and Initial Development of Text Classification Models to Identify Health Information Technology Usability-Related Patient Safety Event Reports.

Background: With the pervasive use of health information technology (HIT) there has been increased concern over the usability and safety of this technology. Identifying HIT usability and safety hazards, mitigating those hazards to prevent patient harm, and using this knowledge to improve future HIT systems are critical to advancing health care.

Purpose: The purpose of this work is to demonstrate the feasibility of a modeling approach to identify HIT usability-related patient safety events (PSEs) from the free-text of safety reports and the utility of such models for supporting patient safety analysts in their analysis of event data.

Quantifying Sex Bias in Clinical Studies at Scale With Automated Data Extraction.

Importance: Analyses of female representation in clinical studies have been limited in scope and scale.

Objective: To perform a large-scale analysis of global enrollment sex bias in clinical studies.

Design, Setting, And Participants: In this cross-sectional study, clinical studies from published articles from PubMed from 1966 to 2018 and records from Aggregate Analysis of ClinicalTrials.