Selection Bias
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(HCW) Surveys in Humanitarian Contexts in Lmics
Analytics for Operations working group GUIDANCE BRIEF Guidance for Health Care Worker (HCW) Surveys in humanitarian contexts in LMICs Developed by the Analytics for Operations Working Group to support those working with communities and healthcare workers in humanitarian and emergency contexts. This document has been developed for response actors working in humanitarian contexts who seek rapid approaches to gathering evidence about the experience of healthcare workers, and the communities of which they are a part. Understanding healthcare worker experience is critical to inform and guide humanitarian programming and effective strategies to promote IPC, identify psychosocial support needs. This evidence also informs humanitarian programming that interacts with HCWs and facilities such as nutrition, health reinforcement, communication, SGBV and gender. In low- and middle-income countries (LMIC), healthcare workers (HCW) are often faced with limited resources, equipment, performance support and even formal training to provide the life-saving work expected of them. In humanitarian contexts1, where human resources are also scarce, HCWs may comprise formally trained doctors, nurses, pharmacists, dentists, allied health professionals etc. as well as community members who perform formal health worker related duties with little or no trainingi. These HCWs frequently work in contexts of multiple public health crises, including COVID-19. Their work will be affected by availability of resources (limited supplies, materials), behaviour and emotion (fear), flows of (mis)information (e.g. understanding of expected infection prevention and control (IPC) measures) or services (healthcare policies, services and use). Multiple factors can therefore impact patients, HCWs and their families, not only in terms of risk of exposure to COVID-19, but secondary health, socio-economic and psycho-social risks, as well as constraints that interrupt or hinder healthcare provision such as physical distancing practices. -
A Task-Based Taxonomy of Cognitive Biases for Information Visualization
A Task-based Taxonomy of Cognitive Biases for Information Visualization Evanthia Dimara, Steven Franconeri, Catherine Plaisant, Anastasia Bezerianos, and Pierre Dragicevic Three kinds of limitations The Computer The Display 2 Three kinds of limitations The Computer The Display The Human 3 Three kinds of limitations: humans • Human vision ️ has limitations • Human reasoning 易 has limitations The Human 4 ️Perceptual bias Magnitude estimation 5 ️Perceptual bias Magnitude estimation Color perception 6 易 Cognitive bias Behaviors when humans consistently behave irrationally Pohl’s criteria distilled: • Are predictable and consistent • People are unaware they’re doing them • Are not misunderstandings 7 Ambiguity effect, Anchoring or focalism, Anthropocentric thinking, Anthropomorphism or personification, Attentional bias, Attribute substitution, Automation bias, Availability heuristic, Availability cascade, Backfire effect, Bandwagon effect, Base rate fallacy or Base rate neglect, Belief bias, Ben Franklin effect, Berkson's paradox, Bias blind spot, Choice-supportive bias, Clustering illusion, Compassion fade, Confirmation bias, Congruence bias, Conjunction fallacy, Conservatism (belief revision), Continued influence effect, Contrast effect, Courtesy bias, Curse of knowledge, Declinism, Decoy effect, Default effect, Denomination effect, Disposition effect, Distinction bias, Dread aversion, Dunning–Kruger effect, Duration neglect, Empathy gap, End-of-history illusion, Endowment effect, Exaggerated expectation, Experimenter's or expectation bias, -
Cognitive Bias Mitigation: How to Make Decision-Making More Rational?
Cognitive Bias Mitigation: How to make decision-making more rational? Abstract Cognitive biases distort judgement and adversely impact decision-making, which results in economic inefficiencies. Initial attempts to mitigate these biases met with little success. However, recent studies which used computer games and educational videos to train people to avoid biases (Clegg et al., 2014; Morewedge et al., 2015) showed that this form of training reduced selected cognitive biases by 30 %. In this work I report results of an experiment which investigated the debiasing effects of training on confirmation bias. The debiasing training took the form of a short video which contained information about confirmation bias, its impact on judgement, and mitigation strategies. The results show that participants exhibited confirmation bias both in the selection and processing of information, and that debiasing training effectively decreased the level of confirmation bias by 33 % at the 5% significance level. Key words: Behavioural economics, cognitive bias, confirmation bias, cognitive bias mitigation, confirmation bias mitigation, debiasing JEL classification: D03, D81, Y80 1 Introduction Empirical research has documented a panoply of cognitive biases which impair human judgement and make people depart systematically from models of rational behaviour (Gilovich et al., 2002; Kahneman, 2011; Kahneman & Tversky, 1979; Pohl, 2004). Besides distorted decision-making and judgement in the areas of medicine, law, and military (Nickerson, 1998), cognitive biases can also lead to economic inefficiencies. Slovic et al. (1977) point out how they distort insurance purchases, Hyman Minsky (1982) partly blames psychological factors for economic cycles. Shefrin (2010) argues that confirmation bias and some other cognitive biases were among the significant factors leading to the global financial crisis which broke out in 2008. -
THE ROLE of PUBLICATION SELECTION BIAS in ESTIMATES of the VALUE of a STATISTICAL LIFE W
THE ROLE OF PUBLICATION SELECTION BIAS IN ESTIMATES OF THE VALUE OF A STATISTICAL LIFE w. k i p vi s c u s i ABSTRACT Meta-regression estimates of the value of a statistical life (VSL) controlling for publication selection bias often yield bias-corrected estimates of VSL that are substantially below the mean VSL estimates. Labor market studies using the more recent Census of Fatal Occu- pational Injuries (CFOI) data are subject to less measurement error and also yield higher bias-corrected estimates than do studies based on earlier fatality rate measures. These re- sultsareborneoutbythefindingsforalargesampleofallVSLestimatesbasedonlabor market studies using CFOI data and for four meta-analysis data sets consisting of the au- thors’ best estimates of VSL. The confidence intervals of the publication bias-corrected estimates of VSL based on the CFOI data include the values that are currently used by government agencies, which are in line with the most precisely estimated values in the literature. KEYWORDS: value of a statistical life, VSL, meta-regression, publication selection bias, Census of Fatal Occupational Injuries, CFOI JEL CLASSIFICATION: I18, K32, J17, J31 1. Introduction The key parameter used in policy contexts to assess the benefits of policies that reduce mortality risks is the value of a statistical life (VSL).1 This measure of the risk-money trade-off for small risks of death serves as the basis for the standard approach used by government agencies to establish monetary benefit values for the predicted reductions in mortality risks from health, safety, and environmental policies. Recent government appli- cations of the VSL have used estimates in the $6 million to $10 million range, where these and all other dollar figures in this article are in 2013 dollars using the Consumer Price In- dex for all Urban Consumers (CPI-U). -
Bias and Fairness in NLP
Bias and Fairness in NLP Margaret Mitchell Kai-Wei Chang Vicente Ordóñez Román Google Brain UCLA University of Virginia Vinodkumar Prabhakaran Google Brain Tutorial Outline ● Part 1: Cognitive Biases / Data Biases / Bias laundering ● Part 2: Bias in NLP and Mitigation Approaches ● Part 3: Building Fair and Robust Representations for Vision and Language ● Part 4: Conclusion and Discussion “Bias Laundering” Cognitive Biases, Data Biases, and ML Vinodkumar Prabhakaran Margaret Mitchell Google Brain Google Brain Andrew Emily Simone Parker Lucy Ben Elena Deb Timnit Gebru Zaldivar Denton Wu Barnes Vasserman Hutchinson Spitzer Raji Adrian Brian Dirk Josh Alex Blake Hee Jung Hartwig Blaise Benton Zhang Hovy Lovejoy Beutel Lemoine Ryu Adam Agüera y Arcas What’s in this tutorial ● Motivation for Fairness research in NLP ● How and why NLP models may be unfair ● Various types of NLP fairness issues and mitigation approaches ● What can/should we do? What’s NOT in this tutorial ● Definitive answers to fairness/ethical questions ● Prescriptive solutions to fix ML/NLP (un)fairness What do you see? What do you see? ● Bananas What do you see? ● Bananas ● Stickers What do you see? ● Bananas ● Stickers ● Dole Bananas What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store ● Bananas on shelves What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store ● Bananas on shelves ● Bunches of bananas What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas -
Illinois Rules of the Road 2021 DSD a 112.35 ROR.Qxp Layout 1 5/5/21 9:45 AM Page 1
DSD A 112.32 Cover 2021.qxp_Layout 1 1/6/21 10:58 AM Page 1 DSD A 112.32 Cover 2021.qxp_Layout 1 5/11/21 2:06 PM Page 3 Illinois continues to be a national leader in traffic safety. Over the last decade, traffic fatalities in our state have declined significantly. This is due in large part to innovative efforts to combat drunk and distracted driving, as well as stronger guidelines for new teen drivers. The driving public’s increased awareness and avoidance of hazardous driving behaviors are critical for Illinois to see a further decline in traffic fatalities. Beginning May 3, 2023, the federal government will require your driver’s license or ID card (DL/ID) to be REAL ID compliant for use as identification to board domestic flights. Not every person needs a REAL ID card, which is why we offer you a choice. You decide if you need a REAL ID or standard DL/ID. More information is available on the following pages. The application process for a REAL ID-compliant DL/ID requires enhanced security measures that meet mandated federal guidelines. As a result, you must provide documentation confirming your identity, Social Security number, residency and signature. Please note there is no immediate need to apply for a REAL ID- compliant DL/ID. Current Illinois DL/IDs will be accepted to board domestic flights until May 3, 2023. For more information about the REAL ID program, visit REALID.ilsos.gov or call 833-503-4074. As Secretary of State, I will continue to maintain the highest standards when it comes to traffic safety and public service in Illinois. -
Nonresponse Bias and Trip Generation Models
64 TRANSPORTATION RESEARCH RECORD 1412 Nonresponse Bias and Trip Generation Models PIYUSHIMITA THAKURIAH, As:H1sH SEN, SnM S66T, AND EDWARD CHRISTOPHER There is serious concern over the fact that travel surveys often On the other hand, if the model does not satisfy these con overrepresent smaller households with higher incomes and better ditions, bias will occur even if there is a 100 percent response education levels and, in general, that nonresponse is nonrandom. rate. These conditions are satisfied by the model if the func However, when the data are used to build linear models, such as trip generation models, and the model is correctly specified, tional form of the model is correct and all important explan estimates of parameters are unbiased regardless of the nature of atory variables are included. the respondents, and the issues of how response rates and non Because categorical models do not have any problems with response bias are ameliorated. The more important task then is their functional form, and weighting and related issues are the complete specification of the model, without leaving out var taken care of, the authors prefer categorical trip generation iables that have some effect on the variable to be predicted. The models. This preference is discussed in a later section. There theoretical basis for this reasoning is given along with an example fore, the issue that remains when assessing bias in estimates of how bias may be assessed in estimates of trip generation model parameters. Some of the methods used are quite standard, but from categorical trip generation models is whether the model the manner in which these and other more nonstandard methods includes all the relevant independent variables or at least all have been systematically put together to assess bias in estimates important predictors. -
Equally Flexible and Optimal Response Bias in Older Compared to Younger Adults
AGING AND RESPONSE BIAS Equally Flexible and Optimal Response Bias in Older Compared to Younger Adults Roderick Garton, Angus Reynolds, Mark R. Hinder, Andrew Heathcote Department of Psychology, University of Tasmania Accepted for publication in Psychology and Aging, 8 February 2019 © 2019, American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors’ permission. The final article will be available, upon publication, via its DOI: 10.1037/pag0000339 Author Note Roderick Garton, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia; Angus Reynolds, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia; Mark R. Hinder, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia; Andrew Heathcote, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia. Correspondence concerning this article should be addressed to Roderick Garton, University of Tasmania Private Bag 30, Hobart, Tasmania, Australia, 7001. Email: [email protected] This study was supported by Australian Research Council Discovery Project DP160101891 (Andrew Heathcote) and Future Fellowship FT150100406 (Mark R. Hinder), and by Australian Government Research Training Program Scholarships (Angus Reynolds and Roderick Garton). The authors would like to thank Matthew Gretton for help with data acquisition, Luke Strickland and Yi-Shin Lin for help with data analysis, and Claire Byrne for help with study administration. The trial-level data for the experiment reported in this manuscript are available on the Open Science Framework (https://osf.io/9hwu2/). 1 AGING AND RESPONSE BIAS Abstract Base-rate neglect is a failure to sufficiently bias decisions toward a priori more likely options. -
LOST the Official Show Auction
LOST | The Auction 156 1-310-859-7701 Profiles in History | August 21 & 22, 2010 572. JACK’S COSTUME FROM THE EPISODE, “THERE’S NO 574. JACK’S COSTUME FROM PLACE LIKE HOME, PARTS 2 THE EPISODE, “EGGTOWN.” & 3.” Jack’s distressed beige Jack’s black leather jack- linen shirt and brown pants et, gray check-pattern worn in the episode, “There’s long-sleeve shirt and blue No Place Like Home, Parts 2 jeans worn in the episode, & 3.” Seen on the raft when “Eggtown.” $200 – $300 the Oceanic Six are rescued. $200 – $300 573. JACK’S SUIT FROM THE EPISODE, “THERE’S NO PLACE 575. JACK’S SEASON FOUR LIKE HOME, PART 1.” Jack’s COSTUME. Jack’s gray pants, black suit (jacket and pants), striped blue button down shirt white dress shirt and black and gray sport jacket worn in tie from the episode, “There’s Season Four. $200 – $300 No Place Like Home, Part 1.” $200 – $300 157 www.liveauctioneers.com LOST | The Auction 578. KATE’S COSTUME FROM THE EPISODE, “THERE’S NO PLACE LIKE HOME, PART 1.” Kate’s jeans and green but- ton down shirt worn at the press conference in the episode, “There’s No Place Like Home, Part 1.” $200 – $300 576. JACK’S SEASON FOUR DOCTOR’S COSTUME. Jack’s white lab coat embroidered “J. Shephard M.D.,” Yves St. Laurent suit (jacket and pants), white striped shirt, gray tie, black shoes and belt. Includes medical stetho- scope and pair of knee reflex hammers used by Jack Shephard throughout the series. -
Why So Confident? the Influence of Outcome Desirability on Selective Exposure and Likelihood Judgment
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by The University of North Carolina at Greensboro Archived version from NCDOCKS Institutional Repository http://libres.uncg.edu/ir/asu/ Why so confident? The influence of outcome desirability on selective exposure and likelihood judgment Authors Paul D. Windschitl , Aaron M. Scherer a, Andrew R. Smith , Jason P. Rose Abstract Previous studies that have directly manipulated outcome desirability have often found little effect on likelihood judgments (i.e., no desirability bias or wishful thinking). The present studies tested whether selections of new information about outcomes would be impacted by outcome desirability, thereby biasing likelihood judgments. In Study 1, participants made predictions about novel outcomes and then selected additional information to read from a buffet. They favored information supporting their prediction, and this fueled an increase in confidence. Studies 2 and 3 directly manipulated outcome desirability through monetary means. If a target outcome (randomly preselected) was made especially desirable, then participants tended to select information that supported the outcome. If made undesirable, less supporting information was selected. Selection bias was again linked to subsequent likelihood judgments. These results constitute novel evidence for the role of selective exposure in cases of overconfidence and desirability bias in likelihood judgments. Paul D. Windschitl , Aaron M. Scherer a, Andrew R. Smith , Jason P. Rose (2013) "Why so confident? The influence of outcome desirability on selective exposure and likelihood judgment" Organizational Behavior and Human Decision Processes 120 (2013) 73–86 (ISSN: 0749-5978) Version of Record available @ DOI: (http://dx.doi.org/10.1016/j.obhdp.2012.10.002) Why so confident? The influence of outcome desirability on selective exposure and likelihood judgment q a,⇑ a c b Paul D. -
Guidance for Health Care Worker (HCW) Surveys in Humanitarian
Analytics for Operations & COVID-19 Research Roadmap Social Science working groups GUIDANCE BRIEF Guidance for Health Care Worker (HCW) Surveys in humanitarian contexts in LMICs Developed by the Analytics for Operations & COVID-19 Research Roadmap Social Science working groups to support those working with communities and healthcare workers in humanitarian and emergency contexts. This document has been developed for response actors working in humanitarian contexts who seek rapid approaches to gathering evidence about the experience of healthcare workers, and the communities of which they are a part. Understanding healthcare worker experience is critical to inform and guide humanitarian programming and effective strategies to promote IPC, identify psychosocial support needs. This evidence also informs humanitarian programming that interacts with HCWs and facilities such as nutrition, health reinforcement, communication, SGBV and gender. In low- and middle-income countries (LMIC), healthcare workers (HCW) are often faced with limited resources, equipment, performance support and even formal training to provide the life-saving work expected of them. In humanitarian contexts1, where human resources are also scarce, HCWs may comprise formally trained doctors, nurses, pharmacists, dentists, allied health professionals etc. as well as community members who perform formal health worker related duties with little or no trainingi. These HCWs frequently work in contexts of multiple public health crises, including COVID-19. Their work will be affected -
Straight Until Proven Gay: a Systematic Bias Toward Straight Categorizations in Sexual Orientation Judgments
ATTITUDES AND SOCIAL COGNITION Straight Until Proven Gay: A Systematic Bias Toward Straight Categorizations in Sexual Orientation Judgments David J. Lick Kerri L. Johnson New York University University of California, Los Angeles Perceivers achieve above chance accuracy judging others’ sexual orientations, but they also exhibit a notable response bias by categorizing most targets as straight rather than gay. Although a straight categorization bias is evident in many published reports, it has never been the focus of systematic inquiry. The current studies therefore document this bias and test the mechanisms that produce it. Studies 1–3 revealed the straight categorization bias cannot be explained entirely by perceivers’ attempts to match categorizations to the number of gay targets in a stimulus set. Although perceivers were somewhat sensitive to base rate information, their tendency to categorize targets as straight persisted when they believed each target had a 50% chance of being gay (Study 1), received explicit information about the base rate of gay targets in a stimulus set (Study 2), and encountered stimulus sets with varying base rates of gay targets (Study 3). The remaining studies tested an alternate mechanism for the bias based upon perceivers’ use of gender heuristics when judging sexual orientation. Specifically, Study 4 revealed the range of gendered cues compelling gay judgments is smaller than the range of gendered cues compelling straight judgments despite participants’ acknowledgment of equal base rates for gay and straight targets. Study 5 highlighted perceptual experience as a cause of this imbalance: Exposing perceivers to hyper-gendered faces (e.g., masculine men) expanded the range of gendered cues compelling gay categorizations.