Using Computer Simulation to Study Nurse-to-Patient Ratios in an Emergency Department.

OBJECTIVE: To study the impact of nurse-to-patient ratios on patient length of stay (LOS) in computer simulations of emergency department (ED) care. METHODS: Multiple 24-hour computer simulations of emergency care were used to evaluate the impact of different minimum nurse-to-patient ratios related to ED LOS, which is composed of wait (arrival to bed placement) and bedtime (bed placement to leave bed). RESULTS: Increasing the number of patients per nurse resulted in increased ED LOS. Mean bedtimes in minutes were impacted by nurse-to-patient ratios. CONCLUSIONS: In computer simulation of ED care, increasing the number of patients per nurse resulted in increasing delays in care (ie, increasing bedtime).

My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in Machine Learning.

Machine learning technology has become ubiquitous, but, unfortunately, often exhibits bias. As a consequence, disparate stakeholders need to interact with and make informed decisions about using machine learning models in everyday systems. Visualization technology can support stakeholders in understanding and evaluating trade-offs between, for example, accuracy and fairness of models. This paper aims to empirically answer "Can visualization design choices affect a stakeholder's perception of model bias, trust in a model, and willingness to adopt a model?" Through a series of controlled, crowd-sourced experiments with more than 1,500 participants, we identify a set of strategies people follow in deciding which models to trust. Our results show that men and women prioritize fairness and performance differently and that visual design choices significantly affect that prioritization. For example, women trust fairer models more often than men do, participants value fairness more when it is explained using text than as a bar chart, and being explicitly told a model is biased has a bigger impact than showing past biased performance. We test the generalizability of our results by comparing the effect of multiple textual and visual design choices and offer potential explanations of the cognitive mechanisms behind the difference in fairness perception and trust. Our research guides design considerations to support future work developing visualization systems for machine learning.

Preventing undesirable behavior of intelligent machines.

Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior-that they do not, for example, cause harm to humans-is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.

Self-assembly of DNA double-double crossover complexes into high-density, doubly connected, planar structures.

We designed a molecular complex, the double-double crossover, consisting of four DNA double helices connected by six reciprocal exchanges. Atomic force micrographs suggest that double-double crossover complexes self-assemble into high-density, doubly connected, two-dimensional, planar structures. Such structures may be suitable as substrates for the deposition of nanomaterials in the creation of high-density electrical and quantum devices. We speculate about a modified double-double crossover complex that might self-assemble into high-density, doubly connected, three-dimensional structures.

DNA triangles and self-assembled hexagonal tilings.

We have designed and constructed DNA complexes in the form of triangles. We have created hexagonal planar tilings from these triangles via self-assembly. Unlike previously reported structures self-assembled from DNA, our structures appear to involve bending of double helices. Bending helices may be a useful design option in the creation of self-assembled DNA structures. It has been suggested that DNA self-assembly may lead to novel materials and efficient computational devices.