scholarly journals Algorithmic Bias in Education

2021 ◽  
Author(s):  
Ryan Shaun Baker ◽  
Aaron Hawn

Draft Preprint. In this paper, we review algorithmic bias in education, discussing the causes of that bias and reviewing the empirical literature on the specific ways that algorithmic bias is known to have manifested in education. While other recent work has reviewed mathematical definitions of fairness and expanded algorithmic approaches to reducing bias, our review focuses instead on solidifying the current understanding of the concrete impacts of algorithmic bias in education—which groups are known to be impacted and which stages and agents in the development and deployment of educational algorithms are implicated. We discuss theoretical and formal perspectives on algorithmic bias, connect those perspectives to the machine learning pipeline, and review metrics for assessing bias. Next, we review the evidence around algorithmic bias in education, beginning with the most heavily-studied categories of race/ethnicity, gender, and nationality, and moving to the available evidence of bias for less-studied categories, such as socioeconomic status, disability, and military-connected status. Acknowledging the gaps in what has been studied, we propose a framework for moving from unknown bias to known bias and from fairness to equity. We discuss obstacles to addressing these challenges and propose four areas of effort for mitigating and resolving the problems of algorithmic bias in AIED systems and other educational technology.

2013 ◽  
Author(s):  
Christopher S. Bartlett ◽  
Tulay Koru-Sengul ◽  
Feng Miao ◽  
Stacey L. Tannenbaum ◽  
David J. Lee ◽  
...  

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1098.2-1099
Author(s):  
O. Russell ◽  
S. Lester ◽  
R. Black ◽  
C. Hill

Background:Socioeconomic status (SES) influences disease outcomes in rheumatoid arthritis (RA) patients. (1, 2) Differences in medication use could partly explain this association. (3) A scoping review was used to identify research conducted on this topic and determine what knowledge gaps remain.Objectives:To determine what research has been conducted on this topic, how this research has defined SES and medication use, and establish what knowledge gaps remain.Methods:MEDLINE, EMBASE and PsychInfo were searched from their inception until May 2019 for studies which assessed SES and medication use as outcome variables. Studies were included if they measured medication use and incorporated an SES measure as a comparator variable.SES was defined using any of the “PROGRESS” framework variables (4) including patients’ stated gender, age, educational attainment, employment, occupational class, personal income, marital status, health insurance coverage, area- (neighbourhood) level SES, or patients’ stated race and/or ethnicity. Medication use was broadly defined as either prescription or dispensation of a medicine, medication adherence, or delays in treatment. Data was extracted on studies’ primary objectives, measurement of specific SES measures, patients’ medication use, and whether studies assessed for differences in patients’ medication use according to SES variables.Results:1464 studies were identified by this search from which 74 studies were selected for inclusion, including 52 published articles. Studies’ publication year ranged from 1994-2019, and originated from 20 countries; most commonly from the USA.Studies measured a median of 4 SES variables (IQR 3-6), with educational achievement, area level SES and race/ethnicity the most frequently recorded.Likelihood of disease modifying antirheumatic drug (DMARD) prescription was the most frequent primary objective recorded.96% of studies reported on patients’ use of DMARDs, with glucocorticoids and analgesics being reported in fewer studies (51% and 23% respectively.)Most included studies found at least one SES measure to be significantly associated with differences in patients’ medication use. In some studies, however, this result was not necessarily drawn from the primary outcome and therefore may not have been adjusted for covariates.70% of published studies measuring patients’ income (n=14 of 20) and 58% of those that measured race/ethnicity (n=14 of 24) documented significant differences in patients’ medication use according to these SES variables, although the direction of this effect – whether it led to ‘greater’ or ‘lesser’ medication use – varied between studies.Conclusion:Multiple definitions of SES are used in studies of medication use in RA patients. Despite this, most identified studies found evidence of a difference in medication use by patient groups that differed by an SES variable, although how medication use differed was found to vary between studies. This latter observation may relate to contextual factors pertaining to differences in countries’ healthcare systems. Further prospective studies with clearly defined SES and medication use measures may help confirm the apparent association between SES and differences in medication use.References:[1]Jacobi CE, Mol GD, Boshuizen HC, Rupp I, Dinant HJ, Van Den Bos GA. Impact of socioeconomic status on the course of rheumatoid arthritis and on related use of health care services. Arthritis Rheum. 2003;49(4):567-73.[2]ERAS Study Group. Socioeconomic deprivation and rheumatoid disease: what lessons for the health service? ERAS Study Group. Early Rheumatoid Arthritis Study. Annals of the rheumatic diseases. 2000;59(10):794-9.[3]Verstappen SMM. The impact of socio-economic status in rheumatoid arthritis. Rheumatology (Oxford). 2017;56(7):1051-2.[4]O’Neill J, Tabish H, Welch V, Petticrew M, Pottie K, Clarke M, et al. Applying an equity lens to interventions: using PROGRESS ensures consideration of socially stratifying factors to illuminate inequities in health. J Clin Epidemiol. 2014;67(1):56-64.Acknowledgements:This research was supported by an Australian Government Research Training Program Scholarship.Disclosure of Interests:None declared


BMJ Open ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. e018826 ◽  
Author(s):  
Jacquie Boyang Lu ◽  
Kristin J Danko ◽  
Michael D Elfassy ◽  
Vivian Welch ◽  
Jeremy M Grimshaw ◽  
...  

BackgroundSocially disadvantaged populations carry a disproportionate burden of diabetes-related morbidity and mortality. There is an emerging interest in quality improvement (QI) strategies in the care of patients with diabetes, however, the effect of these interventions on disadvantaged groups remains unclear.ObjectiveThis is a secondary analysis of a systematic review that seeks to examine the extent of equity considerations in diabetes QI studies, specifically quantifying the proportion of studies that target interventions toward disadvantaged populations and conduct analyses on the impact of interventions on disadvantaged groups.Research design and methodsStudies were identified using Medline, HealthStar and the Cochrane Effective Practice and Organisation of Care database. Randomised controlled trials assessing 12 QI strategies targeting health systems, healthcare professionals and/or patients for the management of adult outpatients with diabetes were eligible. The place of residence, race/ethnicity/culture/language, occupational status, gender/sexual identity, religious affiliations, education level, socioeconomic status, social capital, plus age, disability, sexual preferences and relationships (PROGRESS-Plus) framework was used to identify trials that focused on disadvantaged patient populations, to examine the types of equity-relevant factors that are being considered and to explore temporal trends in equity-relevant diabetes QI trials.ResultsOf the 278 trials that met the inclusion criteria, 95 trials had equity-relevant considerations. These include 64 targeted trials that focused on a disadvantaged population with the aim to improve the health status of that population and 31 general trials that undertook subgroup analyses to assess the extent to which their interventions may have had differential impacts on disadvantaged subgroups. Trials predominantly focused on race/ethnicity, socioeconomic status and place of residence as potential factors for disadvantage in patients receiving diabetes care.ConclusionsLess than one-third of diabetes QI trials included equity-relevant considerations, limiting the relevance and applicability of their data to disadvantaged populations. There is a need for better data collection, reporting, analysis and interventions on the social determinants of health that may influence the health outcomes of patients with diabetes.PROSPERO registration numberCRD42013005165.


2006 ◽  
Vol 34 (3) ◽  
pp. 520-525 ◽  
Author(s):  
Margaret A. Winker

Race and ethnicity are commonly reported variables in biomedical research, but how they were initially determined is often not described and the rationale for analyzing them is often not provided. JAMA improved the reporting of these factors by implementing a policy and procedure for doing so. However, still lacking are careful consideration of what is actually being measured when race/ethnicity is described, consistent terminology, hypothesis-driven justification for analyzing race/ethnicity, and a consistent and generalizable measurement of socioeconomic status. Furthermore, some studies continue to use race/ethnicity as a proxy for genetics. Research into appropriate measures of race/ethnicity and socioeconomic factors, as well as education of researchers regarding issues of race/ethnicity, is necessary to clarify the meaning of race/ethnicity in the biomedical literature.


2016 ◽  
pp. gbw080 ◽  
Author(s):  
Laura J. Samuel ◽  
David L. Roth ◽  
Brian S. Schwartz ◽  
Roland J. Thorpe ◽  
Thomas A. Glass

Stroke ◽  
2013 ◽  
Vol 44 (2) ◽  
pp. 469-476 ◽  
Author(s):  
Amresh D. Hanchate ◽  
Lee H. Schwamm ◽  
Wei Huang ◽  
Elaine M. Hylek

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 31-31
Author(s):  
Laura Donovan ◽  
Donna Buono ◽  
Melissa Kate Accordino ◽  
Jason Dennis Wright ◽  
Andrew B. Lassman ◽  
...  

31 Background: GBM is associated with a poor prognosis and early death in elderly patients. Prior studies have demonstrated a high burden of hospitalization in this population. We sought to evaluate and examine trends in hospitalizations and EOL care in GBM survivors. Methods: Using SEER-Medicare linked data, we performed a retrospective observational cohort study of patients aged ≥ 65 years diagnosed with GBM from 2005-2017 who lived at least 6 months from the time of diagnosis. Aggressive EOL care was defined as: chemotherapy or radiotherapy within 14 days of death (DOD), surgery within 30 DOD, > 1 emergency department visit, ≥ 1 hospitalization or intensive care unit admission within 30 DOD; in-hospital death; or hospice enrollment ≤ 3 DOD. We evaluated age, race, ethnicity, marital status, gender, socioeconomic status, comorbidities, prior treatment and percentage of time hospitalized. Multivariable logistic regression was performed to determine factors associated with aggressive end of life care. Results: Of 5827 patients, 2269 (38.9%) survived at least 6 months. Among these, 1106 (48.7%) survived 6-12 months, 558 (24.6%) survived 12-18 months, and 605 (26.7%) survived > 18 months. Patients who survived 6-12 months had the highest burden of hospitalization and spent a median of 10.6% of their remaining life in the hospital compared to those surviving 12-18 months (5.4%) and > 18 months (3%) (P < 0.001). 10.1% of the cohort had claims for palliative care services; 49.8% of initial palliative care consults occurred in the last 30 days of life. Hospice claims existed in 83% with a median length of stay 33 days (IQR 12, 79 days). 30.1% of subjects received aggressive EOL care. Receiving chemo at any time (OR 1.510, 95% CI 1.221-1.867) and spending ≥ 20% of life in the hospital after diagnosis (OR 3.331, 95% CI 2.567-4.324) were associated with aggressive EOL care. Women (OR 0.759, 95% CI 0.624-0.922), patients with higher socioeconomic status (OR 0.533, 95% CI 0.342-0.829), and those diagnosed ≥ age 80 (OR 0.723, 95% CI 0.528-0.991) were less likely to receive aggressive EOL care. Race, ethnicity, marital status, and extent of initial resection were not associated with aggressive EOL care. Conclusions: A minority of elderly patients with GBM in the SEER-Medicare database survived ≥ 6 months; hospitalizations were common and patients spent a significant proportion of their remaining life hospitalized. Although hospice utilization was high in this cohort, 30% of patients received aggressive EOL care. Despite the aggressive nature of GBM, few patients had palliative care consults during their illness. Increased utilization of palliative care services may help reduce hospitalization burden and aggressive EOL care in this population.


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