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People seem to have pure time preferences about trade-offs concerning their own pleasures and pains, and such preferences contribute to estimates of people's individual time discount rate. Do pure time preferences also matter to interpersonal welfare trade-offs, including those concerning the welfare of future generations? Most importantly, should the intergenerational time discount rate include a pure time preference? Descriptivists claim that the intergenerational discount rate should reflect actual people's revealed preferences, and thus it should include a pure time preference. Prescriptivists claim that the intergenerational discount rate should be based on moral analysis, and thus they (often) claim that the rate of pure time preference should be zero. I argue that regardless of which view is correct, a focus on pure time preference is misplaced. First, the most plausible interpretation of descriptive preferences for intergenerational trade-offs is that people are socially biased and not time biased. Second, social bias is superior to time bias as a prescriptive reason to discount the welfare of future people. Third, recent advances in measuring social bias as a social discount rate make social bias a viable replacement for time bias in economic analyses of intergenerational welfare trade-offs.

In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due to excluding observed covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of bias unmasking and shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.

© The Author 2016. Published by Oxford University Press on behalf of the Society for Political Methodology. All rights reserved. In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due to excluding observed covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of bias unmasking and shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.

In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due toexcludingobserved covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of biasunmaskingand shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.

In recent decades, legal scholars have advanced sophisticated models for understanding prejudice and discrimination, drawing on disciplines such as psychology, sociology, and economics. These models explain how inequality is implicit in cognition and seamlessly woven into social structures. And yet, obvious, explicit, and overt forms of bias have not gone away. The law does not need empirical methods to identify bias when it is marching down the street in Nazi regalia, hurling misogynist invective, or trading in anti-Muslim stereotypes. Official acceptance of such prejudices may be uniquely harmful in normalizing discrimination. But surprisingly, many discrimination cases ignore explicit bias. Courts have refused to consider evidence of biased statements by government officials in cases alleging, for example, that facially neutral laws were enacted for the express purpose of singling out Muslims. Courts outright ignore explicit bias when they consider intentional discrimination to be justified by goals such as law enforcement. And courts have developed a "stray remarks doctrine" in employment discrimination cases to prevent juries from hearing evidence of explicit bias. This Article identifies and criticizes legal arguments against consideration of explicit bias, including concerns about the feasibility of inquiries into intent, worry about undermining otherwise legitimate policies, the desire to avoid chilling effects on free speech, and the fear that confronting explicit bias will result in backlash. It argues that discrimination law should dispense with doctrines that shield explicit bias from consideration.

In recent decades, legal scholars have advanced sophisticated models for understanding prejudice and discrimination, drawing on disciplines such as psychology, sociology, and economics. These models explain how inequality is implicit in cognition and seamlessly woven into social structures. And yet, obvious, explicit, and overt forms of bias have not gone away. The law does not need empirical methods to identify bias when it is marching down the street in Nazi regalia, hurling misogynist invective, or trading in anti-Muslim stereotypes. Official acceptance of such prejudices may be uniquely harmful in normalizing discrimination. But surprisingly, many discrimination cases ignore explicit bias. Courts have refused to consider evidence of biased statements by government officials in cases alleging, for example, that facially neutral laws were enacted for the express purpose of singling out Muslims. Courts outright ignore explicit bias when they consider intentional discrimination to be justified by goals such as law enforcement. And courts have developed a "stray remarks doctrine" in employment discrimination cases to prevent juries from hearing evidence of explicit bias. This Article identifies and criticizes legal arguments against consideration of explicit bias, including concern about the feasibility of inquiries into intent, worry about undermining otherwise legitimate policies, the desire to avoid chilling effects on free speech, and fear that confronting explicit bias will result in backlash. It argues that discrimination law should dispense with doctrines that shield explicit bias from consideration.

Bias is everywhere. Politicians are talking about it, corporations are trying to eradicate it and people are dying because of it. In contrast to explicit biases such as obvious racism or sexism, implicit biases exist outside our awareness and influence us despite our best intentions. This session will start with introduction to the concept of implicit bias, and its relevance to science education. Next, Dr. Sukhera describes a framework for recognizing and managing biases that has relevance for individuals and organizations. Through striving for our ideals while accepting our shortcomings we can reflect on our biases, change our behaviour and co-create change within society. At the end of this session participants will be able to: 1. Understand the topic of implicit bias and its relevance to communication in science education. 2. Describe a framework for implicit bias recognition and management for educational professionals 3. Be inspired to apply findings on the science of implicit bias towards organizational and societal change

Selective reading of political online information was examined based on cognitive dissonance, social identity, and news values frameworks. Online reports were displayed to 156 Americans while selective exposure was tracked. The news articles that participants chose from were either conservative or liberal and also either positive or negative regarding American political policies. In addition, information processing styles (cognitive reflection and need-for-cognition) were measured. Results revealed confirmation and negativity biases, per cognitive dissonance and news values, but did not corroborate the hypothesis derived from social identity theory. Greater cognitive reflection, greater need-for-cognition, and worse affective state fostered the confirmation bias; stronger social comparison tendency reduced the negativity bias.

Intro -- Contents -- What is Bias? -- Why Addressing Bias is Essential for Leaders -- Types of Bias -- Cognitive Bias -- Implicit Associations -- Bias is a Habit -- The Beyond Bias Model -- Social Identity -- Stereotypes -- Prejudice -- Discrimination -- Organizational Approaches to Decreasing Stereotypes, Prejudice, and Discrimination -- Challenging the Habit of Bias: The SCRIPt™ Toolkit -- Self-Care -- Contact -- Recognize & Replace -- Information -- Perspective Taking -- SCRIPt™ in Action -- Conclusion -- Bibliography -- About the Center for Creative Leadership