Search NASA⌕ Search

SEARCH · Search NASA

Results for “gender”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification

Electronic health records (EHR) are not designed for population‐based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA ) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow‐up to the methodological work, we demonstrate how to apply these methods for two real‐world case studies, and we evaluate their performance. In both examples, we use individual patient‐level data collected through the University of Michigan Health System and various external population‐based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age‐related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR‐based research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Understanding the Effects of Vehicle Platoons on Crash Type and Severity

Crash type is an informative indicator to infer driving behaviors and conditions that cause a crash. For example, rear-end and side-swipe crashes are typically caused by improper vehicle interaction such as sudden lane-changing or speed control while hit-object crashes are likely the result of a single driver’s mistake. This study investigated the impact of vehicles travelling as a group (platoon) and its configuration (i.e., types of vehicles consisting of the platoon) on crash type and severity since the vehicles could affect each other when travelling in close proximity. This study applied Generalized Structure Equation Modeling (GSEM) to capture the complex relationships among the various crash factors such as traffic condition, driver characteristics, environmental conditions, and vehicle interaction to the crash attributes including type and severity. This study collected over 3 million individual vehicle data from 39 traffic count sites in California to estimate the vehicle interactions and driving behaviors. The microscopic traffic data are matched to 1417 crash reports. Results showed that vehicles traveling in platoons are associated with more rear-end and side-swipe crashes. Speed difference in the platoon had a positive effect on hit-object crashes if the platoon comprises vehicles of homogeneous type – either trucks or non-trucks. In addition, human factors such as age and gender were identified as significant influential factors in all type of crashes, however truck involvement particularly played an important role amongst side-swipe crashes. Crash severity was negatively affected by total flow, and rear-end crashes were more likely to be severe compared with hit-object crashes. Based on findings, this study suggests practical operational strategies to reduce traffic instability associated with platooned vehicle patterns. Understanding the high-risk factors for different crash types and severities would provide valuable insights for decision-makers and transportation engineers to develop targeted intervention strategies in consideration of road users and traffic conditions such as fleet mix and speed.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Prospective associations among relationship abuse, sexual harassment and bullying in a community sample of sexual minority and exclusively heterosexual youth

ABSTRACT Introduction Sexual minority youth (SMY) are at significantly greater risk for experiencing adolescent relationship abuse (ARA) than exclusively heterosexual youth, yet little is known about the factors that elevate their risks for such abuse. Peer victimization (i.e., bullying, sexual harassment) has been associated with ARA among heterosexual youth. SMY experience higher rates of peer victimization than heterosexual youth, suggesting that it may be a risk factor for ARA among these youth. Using longitudinal data from a community sample of adolescents recruited from the northeastern US, we examined whether sexual identity was associated prospectively with ARA, and whether that relationship would be indirect, and mediated via bullying and sexual harassment at a 12‐month follow‐up. We expected to find higher rates of peer victimization and ARA among SMY than heterosexual youth, and we expected that sexual harassment and bullying would predict subsequent relationship abuse. Methods Adolescents ( N = 800; 58% female; 81% European‐American; 19% SMY) between 13 and 15 years ( M = 14.45, SD = 0.85) completed a web survey at baseline, 6‐months and 12‐months. Results Consistent with prior studies, SMY reported higher rates of bullying, sexual harassment, and relationship abuse than heterosexual youth. SMY who reported sexual harassment at baseline were more likely to report relationship abuse over time. Contrary to our expectations, however, bully victimization did not predict subsequent relationship abuse. Conclusions Sexual harassment can be particularly harmful for SMY because it targets gender and sexual identity at a formative developmental period and puts youth at risk for relationship abuse.

Hequembourg, Amy L.↗
DOE OSTI2023

Predictive models of long COVID

Background: The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future. Methods: We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models — logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the ‘long COVID’ label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741). Findings: LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75. Interpretation: ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Localized energy burden, concentrated disadvantage, and the feminization of energy poverty

Energy burden directly influences households' health and safety. Amid a growing literature on energy, poverty and gender remains relatively understudied. We evaluate socioeconomic, geographic, and health factors as multidimensions of concentrated disadvantage that magnify energy burden in the United States over time. We show that the energy burden is more pronounced in disadvantaged counties with larger elderly, impoverished, disabled people, and racialized populations where people do not have health insurance. Neighborhoods with households headed by women of color (especially Black women) are more likely to face a high energy burden, which worsened during the COVID-19 pandemic. Although energy costs are often regarded as an individual responsibility, these findings illustrate the feminization of energy poverty and indicate the need for an intersectional and interdisciplinary framework in devising energy policy directed to households with the most severe energy burden.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

The use of analgesics for intentional self-poisoning: Trends in U.S. poison center data

In the U.S., intentional self-poisonings with analgesics that are available without a prescription increased from 2000 to 2018. Given concerns regarding mental health outcomes during the COVID-19 pandemic, we examined and compared trends in pediatric and adult intentional self-poisoning with acetaminophen, aspirin, ibuprofen, and naproxen from 2016 to 2021 using the National Poison Data System (NPDS) to see if these trends have continued. We extracted annual case counts of all suspected suicide attempts from intentional poisoning, and of suspected suicide attempts resulting in major effects or death, from the NPDS for non-prescription single ingredient adult formulation acetaminophen, non-prescription single ingredient adult formulation aspirin, single ingredient formulation ibuprofen, and single ingredient formulation naproxen. We enumerated the cases by year, age, and gender. Most cases of intentional self-poisoning within the review period involved acetaminophen and ibuprofen and the 13–19-year-olds constituted the highest proportion of intentional self-poisoning cases across age groups for all four analgesics. Cases involving females predominated cases involving males by 3:1 or greater. Here, the 13–19-year-old age group also represented the largest proportion of cases that resulted in major clinical effects or deaths. An increasing trend in suicide poisoning cases with acetaminophen and ibuprofen was observed in the 6-19-years age group and this trend appeared to exacerbate from 2020 to 2021 corresponding with the start of the COVID-19 pandemic period.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A review of criticality dosimetry at the Y-12 National Security Complex and practical importance of dose accuracy in emergency response

A nuclear criticality results in the emission of both neutron and gamma radiation and can produce doses to personnel near the event that exceed 0.1 Gy (10 rad). The primary purpose of nuclear accident dosimetry is to rapidly identify affected personnel in need of prompt medical treatment and to reassure personnel who have been only minimally exposed. While accurate dosimetry is desired, it must be recognized that dose determinations made from whole-body dosimeters or simple triage methods are very rough estimates and contain significant uncertainties. Even when accounting for factors like varying neutron energy spectra, mean photon energies, body orientation within the radiation field, and transient effects on dosimeter response, etc., the end value is a dosimetric quantity defined for very specific radiological conditions and determined within a simple phantom usually at a single depth. Of more importance is the biological response to the radiation, which will vary by person and can be affected by the individual’s radiation sensitivity, age, gender, mass, and underlying health conditions. The overall biological, person-specific response to a given dose cannot be precisely determined except by patient symptom observation and individual biological dosimetry (e.g. chromosome analysis, lymphocyte ratios, etc.). This work describes and discusses the criticality accident dosimetry program at the Y-12 National Security Complex, a United States Department of Energy National Nuclear Security Administration facility. In conclusion, the primary goals of the Y-12 accident dosimetry program are, among others, the rapid identification of significantly exposed persons, prompt routing of exposed workers for medical evaluation and treatment, and the ultimate processing of dosimeters to assign doses to personnel.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

E-scooter safety: How attitudinal factors influence risky behavior among shared e-scooter riders

In recent years, e-scooter usage for short-distance trips has grown rapidly. This surge in e-scooter use, combined with the high exposure of e-scooter riders to accident risk, has sparked concerns regarding e-scooter safety. Despite some studies focusing on e-scooter safety, little is known about how attitudinal factors lead e-scooter riders to engage in risky riding behaviors. In this paper, we developed a survey-based empirical model to identify the attitudinal factors influencing engagement in risky behaviors among e-scooter users. We used survey data collected from 420 shared e-scooter users in Chicago in 2022. The survey showed that 47.7% of respondents had experienced at least one collision or fall-off while riding e-scooters. We employed the Partial Least Squares Structural Equation Model (PLS-SEM) to examine the relationships between latent attitudinal factors and risky behavior engagement. Moreover, we conducted Permutation Multi-group Analysis (PMGA) to assess the moderating effect of socio-demographic factors within the estimated model. The findings suggest that riders’ unsafe riding attitude and riding confidence are the most influential factors shaping their risky behavior engagement. In addition, accident experience, infrastructure suitability, perceived enjoyment, traffic risk perception, and operational risk perception are among the other significant predictors. Among socio-demographic factors, gender, age, education, and car use frequency significantly influence riders’ engagement in risky behaviors. The results highlight the importance of infrastructure suitability and accident experience in analyzing e-scooter users’ riding behavior. The developed model advances our understanding of factors contributing to e-scooter riders’ risky behavior engagement. The findings offer valuable insights for policymakers and e-scooter vendors aiming to mitigate e-scooter users’ accident risk. Specifically, we recommend three safety countermeasures: (1) safety training programs to encourage a safer attitude, (2) practice-based initiatives to enhance riding confidence, and (3) infrastructure improvements, especially the expansion of bike lanes.

E-scooter↗
DOE OSTI2023

Toward a Diverse Next-Generation Energy Workforce: Teaching Artificial Photosynthesis and Electrochemistry in Elementary Schools through Active Learning

Artificial photosynthesis is a promising approach to generate commodity chemicals using abundant chemical feedstocks and renewable energy sources. Despite its importance, affordable and effective hands-on classroom activities that demonstrate artificial photosynthesis and teach key concepts, especially for primary school students, are lacking. Educating young students on this topic is a critical step in the development of the next-generation energy workforce, especially one that is diverse in race and gender. Here, we hypothesize that an effective approach to educate a broad range of young students on the topic of artificial photosynthesis is through the use of an active learning-based lesson plan that employs cheap and accessible materials. This hypothesis is confirmed by evaluating the understanding of fifth grade students with a survey before and after a lesson plan on artificial photosynthesis that uses active-learning techniques and uses safe and highly accessible materials (baking soda, tap water, plastic jars, Ni coil, alligator clips, and a solar cell) to perform solar-powered water splitting. The lesson plan and survey questions are designed to align with the educational outcomes for fifth grade classrooms in California and to address four general learning objectives: (1) Motivations of Artificial Photosynthesis, (2) Applications of Artificial Photosynthesis, (3) Inputs and Outputs of Artificial Photosynthesis, and (4) Engineering Design for Artificial Photosynthesis. The survey data demonstrate a statistically significant improvement in overall student understanding from the lesson plan. Importantly, the data show that the lesson plan presented here is effective at narrowing the performance gap between minority students and overly represented groups.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Update on Our Action Plan for Equity, Inclusion, and Diversity in Publishing at JGR: Biogeosciences

We made a commitment to better include underrepresented members of our community in the publication pipeline of JGR: Biogeosciences. This commitment consists of regular updates on our policies and practices, and concrete actions we intend to implement over the next year. So far, our progress to tackle biases and ensure equitable research in the biogeosciences has focused on improving diversity of our associate editor and reviewer pools, increasing awareness of unconscious bias in peer-review, and promoting inclusion in global collaborations. In this update, we explore manuscript submissions and manuscript decisions by gender, and we present a pilot that aims to promote ethical and equitable global collaborations in resource-poor settings. Here, we end our editorial by presenting our next set of actions that we plan on completing over the next year, which include a more thorough analysis of reviewer demographics.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Chromatin structure predicts survival in glioma patients

The pathological changes in epigenetics and gene regulation that accompany the progression of low-grade to high-grade gliomas are under-studied. The authors use a large set of paired atac-seq and RNA-seq data from surgically resected glioma specimens to infer gene regulatory relationships in glioma. Thirty-eight glioma patient samples underwent atac-seq sequencing and 16 samples underwent additional RNA-seq analysis. Using an atac-seq/RNA-seq correlation matrix, atac-seq peaks were paired with genes based on high correlation values (|r 2 | > 0.6). Samples clustered by IDH1 status but not by grade. Surprisingly there was a trend for IDH1 mutant samples to have more peaks. The majority of peaks are positively correlated with survival and positively correlated with gene expression. Constructing a model of the top six atac-seq peaks created a highly accurate survival prediction model (r 2 = 0.68). Four of these peaks were still significant after controlling for age, grade, pathology, IDH1 status and gender. Grade II, III, and IV (primary) samples have similar transcription factors and gene modules. However, grade IV (recurrent) samples have strikingly few peaks. Patient-derived glioma cultures showed decreased peak counts following radiation indicating that this may be radiation-induced. This study supports the notion that IDH1 mutant and IDH1 wildtype gliomas have different epigenetic landscapes and that accessible chromatin sites mapped by atac-seq peaks tend to be positively correlated with expression. The data in this study leads to a new model of treatment response wherein glioma cells respond to radiation therapy by closing open regions of DNA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Shared Use Travel Behavior for Improving Rural Mobility: Insights from Greene County, Pennsylvania

Rural communities are considered disadvantaged communities as they suffer from a lack of transport options. Thus, rural regionsprovide less accessibility for commuters to reach their destination as opposed to urban regions. However, the issues of transport disadvantageand shared use mobility in rural areas within the United States (US) have not been well investigated. Furthermore, transport disadvantagediffers between communities and regions across the globe; thus, there is a need to study the behavioral choices of rural commuters within theUS context. This study contributes by analyzing the behavioral choices of rural communities within the US through a case study site ofWaynesburg, Pennsylvania, for adopting a shared use shuttle service. K-means clusters showed that trips from the survey data were a goodrepresentation of real trips from Ecolane. Furthermore, random parameter-based binary logit models were calibrated using data collected fromstudents, faculty, and residents in Waynesburg, Greene County, to study the behavioral choices of commuters. The findings for the faculty andstudents group revealed that prior experience with shared services increases the likelihood of using a shared shuttle. An important personalcharacteristic of inconvenience showed a higher propensity toward using existing modes as opposed to a shared shuttle. Such commutersvalue personal vehicles as more convenient as they have childcare responsibilities and varying schedules for work that require them to moveback and forth across locations, thus making a shared shuttle less attractive for them. The socioeconomic factors of age and gender show ahigher propensity for using shared shuttles. Furthermore, the findings from this study could be helpful for agencies in improving rural mobility andconsidering such shared mobility services for rural communities

42 ENGINEERING↗
DOE OSTI2021

Equity for women and underrepresented minorities in STEM: Graduate experiences and career plans in chemistry

Significance On June 10, 2020, as part of the Black Lives Matter movement, scientists in the United States and throughout the world paused to consider how systematic racism affects the scientific enterprise. As a result, many academic departments are now assessing policies and practices that may contribute to this situation. This paper provides evidence of the nature of inequities related to race–ethnicity and gender in graduate school experiences and career plans of PhD students in one science, technology, engineering, and math (STEM) discipline, chemistry. The results can help promote understanding of the problems and guide efforts toward equity within STEM and, potentially, other academic areas. In turn, these changes can strengthen the scientific enterprise and the well-being of society.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A qualitative study of marginalized students’ academic, physical, and social self-efficacy in a multiweek geoscience field program

Undergraduate summer field programs are valuable experiences that can foster or reduce students’ self-efficacy, an important factor in students’ success and retention in geoscience. Growing research findings show that science field experiences can be hostile and unwelcoming to students with marginalized identities, which may negatively impact their self-efficacy in geoscience, a discipline with a dearth of students from underrepresented, marginalized identities. We conducted an interpretive qualitative study examining how summer geoscience field programs affected two undergraduate, marginalized students’ self-efficacy. Adding to existing theoretical explanations of self-efficacy, we identified three types of self-efficacy impacted positively and negatively by geoscience field experiences: academic, physical, and social self-efficacy. We developed a nuanced understanding of the specific field experiences that influenced the ‘ups and downs’ of students’ self-efficacy and, ultimately, their intent in continuing to pursue a geoscience education or career. Despite negative experiences, including gender discrimination, crude sexual jokes, and a lack of belonging, the students described their intent to persist in geoscience. Here, our findings can assist geoscience educators (and others in field-based sciences) to consider experiences that support and hinder marginalized students’ self-efficacy. Also, our findings can guide efforts to improve geoscience field programs to create more inclusive environments.

case study↗
DOE OSTI2020

Supplemental selenium source on gut health: insights on fecal microbiome and fermentation products of growing puppies

ABSTRACT Selenium is an essential trace element that can modulate the gut microbiome with an impact on host health. The present study aimed to evaluate the effects of organic (selenium-enriched yeast) vs inorganic (sodium selenite) selenium source on fecal end-fermentation products and gut microbiome of puppies from 20 to 52 weeks of age. Alpha and beta diversity of the gut bacterial community were affected by age but not by gender or selenium source. The relative abundance of taxa was differently affected by age, and the DNA concentration of all selected bacterial groups increased with age, although total volatile fatty acids (VFA), acetate, propionate, caproate and lactate concentrations decreased. Organic selenium was associated with a higher concentration of total VFA, propionate and butyrate, a higher number of DNA copies of Lactobacillus, and a trend to lower DNA copies of Escherichia coli. Effects on fecal microbiome during growth differed with selenium source. Females had higher fecal end-fermentation products related to protein degradation, whereas males had higher DNA concentration of Bifidobacterium. Organic selenium might be beneficial over inorganic for dog food supplementation due to the positive modulation of the gut microbiome observed in puppies.

Pereira, Ana Margarida↗
DOE OSTI2024

Comparative Performance of Three Eye-Tracking Devices in Detection of Mild Traumatic Brain Injury in Acute Versus Chronic Subject Populations

ABSTRACT Introduction Presently, traumatic brain injury (TBI) triage in field settings relies on symptom-based screening tools such as the updated Military Acute Concussion Evaluation. Objective eye-tracking may provide an alternative means of neurotrauma screening due to sensitivity to neurotrauma brain-health changes. Previously, the US Army Medical Research and Development Command Non-Invasive NeuroAssessment Devices (NINAD) Integrated Product Team identified 3 commercially available eye-tracking devices (SyncThink EYE-SYNC, Oculogica EyeBOX, NeuroKinetics IPAS) as meeting criteria toward being operationally effective in the detection of TBI in service members. We compared these devices to assess their relative performance in the classification of mild traumatic brain injury (mTBI) subjects versus normal healthy controls. Materials and Methods Participants 18 to 45 years of age were assigned to Acute mTBI, Chronic mTBI, or Control group per study criteria. Each completed a TBI assessment protocol with all 3 devices counterbalanced across participants. Acute mTBI participants were tested within 72 hours following injury whereas time since last injury for the Chronic mTBI group ranged from months to years. Discriminant analysis was undertaken to determine device classification performance in separating TBI subjects from controls. Area Under the Curves (AUCs) were calculated and used to compare the accuracy of device performance. Device-related factors including data quality, the need to repeat tests, and technical issues experienced were aggregated for reporting. Results A total of 63 participants were recruited as Acute mTBI subjects, 34 as Chronic mTBI subjects, and 119 participants without history of TBI as controls. To maximize outcomes, poorer quality data were excluded from analysis using specific criteria where possible. Final analysis utilized 49 (43 male/6 female, mean [x̅] age = 24.3 years, SD [s] = 5.1) Acute mTBI subjects, and 34 (33 male/1 female, x̅ age = 38.8 years, s = 3.9) Chronic mTBI subjects were age- and gender-matched as closely as possible with Control subjects. AUCs obtained with 80% of total dataset ranged from 0.690 to 0.950 for the Acute Group and from 0.753 to 0.811 for the Chronic mTBI group. Validation with the remaining 20% of dataset produced AUCs ranging from 0.600 to 0.750 for Acute mTBI group and 0.490 to 0.571 for the Chronic mTBI group. Conclusions Potential eye-tracking detection of mTBI, per training model outcomes, ranged from acceptable to excellent for the Acute mTBI group; however, it was less consistent for the Chronic mTBI group. The self-imposed targeted performance (AUC of 0.850) appears achievable, but further device improvements and research are necessary. Discriminant analysis models differed for the Acute versus Chronic mTBI groups, suggesting performance differences in eye-tracking. Although eye-tracking demonstrated sensitivity in the Chronic group, a more rigorous and/or longitudinal study design is required to evaluate this observation. mTBI injuries were not controlled for this study, potentially reducing eye-tracking assessment sensitivity. Overall, these findings indicate that while eye-tracking remains a viable means of mTBI screening, device-specific variability in data quality, length of testing, and ease of use must be addressed to achieve NINAD objectives and DoD implementation.

General & Internal Medicine↗
DOE OSTI2020

Lung cancer mortality among construction workers: implications for early detection

Objectives This study examined predictors of lung cancer mortality, beyond age and smoking, among construction workers employed at US Department of Energy (DOE) sites to better define eligibility for low-dose CT (LDCT) lung cancer screening. Methods Predictive models were based on 17 069 workers and 352 lung cancer deaths. Risk factors included age, gender, race/ethnicity, cigarette smoking, years of trade or DOE work, body mass index (BMI), chest X-ray results, spirometry results, respiratory symptoms, beryllium sensitisation and personal history of cancer. Competing risk Cox models were used to obtain HRs and to predict 5-year risks. Results Factors beyond age and smoking included in the final predictive model were chest X-ray changes, abnormal lung function, chronic obstructive pulmonary disease (COPD), respiratory symptoms, BMI, personal history of cancer and having worked 5 or more years at a DOE site or in construction. Risk-based LDCT eligibility demonstrated improved sensitivity, specificity and positive predictive value compared with current US Preventive Services Task Force guidelines. The risk of lung cancer death from 5 years of work in the construction industry or at a DOE site was comparable with the risk from a personal cancer history, a family history of cancer or a diagnosis of COPD. LDCT eligibility criteria used for DOE construction workers, which includes factors beyond age and smoking, identified 86% of participants who eventually would die from lung cancer compared with 51% based on age and smoking alone. Conclusions Results support inclusion of risk from occupational exposures and non-malignant respiratory clinical findings in LDCT clinical guidelines.

Public, Environmental & Occupational Health↗
DOE OSTI2023

Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition

Over the past decade, the machine learning security community has developed a myriad of defenses for evasion attacks. An understudied question in that community is: for whom do these defenses defend? This work considers common approaches to defending learned systems and how security defenses result in performance inequities across different sub-populations. We outline appropriate parity metrics for analysis and begin to answer this question through empirical results of the fairness implications of machine learning security methods. We find that many methods that have been proposed can cause direct harm, like false rejection and unequal benefits from robustness training. The framework we propose for measuring defense equality can be applied to robustly trained models, preprocessing-based defenses, and rejection methods. We identify a set of datasets with a user-centered application and a reasonable computational cost suitable for case studies in measuring the equality of defenses. In our case study of speech command recognition, we show how such adversarial training and augmentation have non-equal but complex protections for social subgroups across gender, accent, and age in relation to user coverage. We present a comparison of equality between two rejection-based defenses: randomized smoothing and neural rejection, finding randomized smoothing more equitable due to the sampling mechanism for minority groups. This represents the first work examining the disparity in the adversarial robustness in the speech domain and the fairness evaluation of rejection-based defenses.

• Artificial intelligence (AI) / machine learning ↗