Model Cards for Model Reporting

Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related AI technology, increasing transparency into how well AI technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation

Modelling trade offs between public and private conservation policies

To reduce global biodiversity loss, there is an urgent need to determine the most efficient allocation of conservation resources. Recently, there has been a growing trend for many governments to supplement public ownership and management of reserves with incentive programs for conservation on private land. At the same time, policies to promote conservation on private land are rarely evaluated in terms of their ecological consequences. This raises important questions, such as the extent to which private land conservation can improve conservation outcomes, and how it should be mixed with more traditional public land conservation. We address these questions, using a general framework for modelling environmental policies and a case study examining the conservation of endangered native grasslands to the west of Melbourne, Australia. Specifically, we examine three policies that involve: i) spending all resources on creating public conservation areas; ii) spending all resources on an ongoing incentive program where private landholders are paid to manage vegetation on their property with 5-year contracts; and iii) splitting resources between these two approaches. The performance of each strategy is quantified with a vegetation condition change model that predicts future changes in grassland quality. Of the policies tested, no one policy was always best and policy performance depended on the objectives of those enacting the policy. This work demonstrates a general method for evaluating environmental policies and highlights the utility of a model which combines ecological and socioeconomic processes.Comment: 20 pages, 5 figure

The golden ticket: gaining in-person access to relatives in long-term care homes during the Covid-19 pandemic

Context: Governments made emergency declarations to restrict the presence of family carers in long-term care homes (LTCHs) as part of infection control measures during the pandemic. Within Canada, two visitor statuses were created: ‘essential’ to the health of the resident and ‘non-essential’ or ‘social visitor’, who were subject to additional restrictions. Objective: This study explored family carers’ experiences navigating in-person access to their relatives in LTCH during the pandemic. Methods: Using interpretive description, a sample of 14 family carers (nine daughters, five spouses) living in British Columbia, Canada, participated in in-depth interviews via video call about their experiences between March 2020 and June 2021. Findings: Analyses illustrated variability in carers’ visitor status across families and over time. Two key themes were identified: 1) “Fighting a Losing Battle” describes how reductionist attitudes and policies minimized the role of caregiving and resulted in traumatic disruptions in familial relationships; 2) “Who’s In and Who’s Out” captures inequities in how visitor status policies were applied. Limitations: Restrictions on conducting research during the pandemic resulted in a smaller sample of family carer participants. Implications: Findings highlight the patchwork implementation of visitor policies over the initial 17 months of the pandemic and the precarious space family carers continue to occupy within the LTC sector. Future research should focus on formalising support for family presence during public health emergencies

Prevalence, Co-Occurring Difficulties, and Risk Factors of Developmental Language Disorder: First Evidence for Mandarin-Speaking Children in a Population-Based Study

Developmental language disorder (DLD) is a condition that significantly affects children\u27s achievement but has been understudied. We aim to estimate the prevalence of DLD in Shanghai, compare the co-occurrence of difficulties between children with DLD and those with typical development (TD), and investigate the early risk factors for DLD

Least-squares methods with Poissonian noise: an analysis and a comparison with the Richardson-Lucy algorithm

It is well-known that the noise associated with the collection of an astronomical image by a CCD camera is, in large part, Poissonian. One would expect, therefore, that computational approaches that incorporate this a priori information will be more effective than those that do not. The Richardson-Lucy (RL) algorithm, for example, can be viewed as a maximum-likelihood (ML) method for image deblurring when the data noise is assumed to be Poissonian. Least-squares (LS) approaches, on the other hand, arises from the assumption that the noise is Gaussian with fixed variance across pixels, which is rarely accurate. Given this, it is surprising that in many cases results obtained using LS techniques are relatively insensitive to whether the noise is Poissonian or Gaussian. Furthermore, in the presence of Poisson noise, results obtained using LS techniques are often comparable with those obtained by the RL algorithm. We seek an explanation of these phenomena via an examination of the regularization properties of particular LS algorithms. In addition, a careful analysis of the RL algorithm yields an explanation as to why it is more effective than LS approaches for star-like objects, and why it provides similar reconstructions for extended objects. We finish with a convergence analysis of the RL algorithm. Numerical results are presented throughout the paper. It is important to stress that the subject treated in this paper is not academic. In fact, in comparison with many ML algorithms, the LS algorithms are much easier to use and to implement, often provide faster convergence rates, and are much more flexible regarding the incorporation of constraints on the solution. Consequently, if little to no improvement is gained in the use of an ML approach over an LS algorithm, the latter will often be the preferred approach.Comment: High resolution images are available upon request. submitted to A&

Mapping the cultural identities of youths in Hong Kong from a social capital perspective

Abstract: With its unique geopolitical status and multicultural setting, Hong Kong has harbored different youth groups generated from cross-border migration with mainland China who are tied to different cultural values and identifications. This study aims to investigate how social capital embedded in the family, school, and community influences the cultural identities across three groups of Chinese youths in the educational system: local students; cross-border students (born in Hong Kong, living in the neighbor city of mainland China but attending schools in Hong Kong on daily commute); and new immigrant students (born in mainland China but living in Hong Kong for less than seven years). Using data from a cross-sectional survey with 2180 fourth- to ninth-grade students in Hong Kong, the logistic regression results suggest that family and community social capital play significant roles in shaping the cultural identity of youths. Implications of the research findings are discussed