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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.

© 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.

An evaluation of the relevance of Ur bias & Ru bias to the postrevolutionary Chinese case. Statistical data are presented on the nature & extent of differentiation in Ru-Ur living standards, the political basis of relevant economic policies is examined, & the relationship between these policies is investigated relative to changes in labor productivity in each sector & to intersectoral savings transfers. Both Ur bias & Ru bias hypotheses illuminate certain dimensions of Chinese development strategy. But the realities of Ru-Ur relations have been complex, & analysis must be supplemented by a focus on the division between state & society, & the question of state bias. 10 Tables, 73 References. HA.

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

Introduction -- Is media bias a serious problem? liberal bias in the media is a serious problem / Richard E. Vatz -- Conservative bias in the media is a serious problem / People for the American Way -- Media bias is not a serious problem / Jon Meacham -- Corporate spending in political campaigns fosters unfair media bias / Sheldon Whitehouse -- Corporate spending in political campaigns does not foster unfair media bias / Shannen W. Coffin -- Which issues reveal media bias? the media are biased in favor of business / David Madland -- The media are biased against business / Nathan Burchfiel -- The media have emphasized a pro-war agenda during the Iraq War / Colman McCarthy -- The media have emphasized bad news in reports about the Iraq War / Rich Noyes ... [et al.] -- The mainstream media exaggerate the threat of global warming / John Fisher -- Conservative news media dismiss the threat of global warming / Karl Frisch -- Should media bias be challenged? media bias should be sanitized and challenged / Fairness and Accuracy in Reporting -- Media bias should not be challenged / Greg Beato -- The Fairness Doctrine should be re-established / Steve Almond -- The Fairness Doctrine should not be re-established / -- Jim Demint -- "Fake news" reports creatively challenge media bias / Patrick McCormick -- "Fake news" reports undermine serious media coverage / Joe Saltzman

We study how electoral systems affect the presence of women in politics using a model in which both voters and parties might have a gender bias. We apply the model to Spanish municipal elections, in which national law mandates that municipalities follow one of two different electoral systems: a closed-list system in which voters pick one party-list, or an open-list system, in which voters pick individual candidates. Using a regression discontinuity design, we find that the closed-list system increases the share of women among candidates and councilors by 2.5 percentage points, and the share of women among mayors by 4.3 percentage points. Our model explains these results as mostly driven by voter bias against women. We provide evidence that supports the mechanism of the model. In particular, we show that, when two councilors almost tied in general-election votes, the one with "one more vote" is substantially more likely to be appointed mayor, but this does not happen when the most voted was female and the second was male, suggesting the presence of some voter bias. We also show that, in a subsample of municipalities with low bias — proxied by having had a female mayor in the past — the difference between the two electoral systems disappears.

Introduction -- American media, a biased history -- Television and blogs -- Bias and perception -- Watergate -- Media, politics, and the (very) personal -- Business and government control of media -- The media and war -- Public broadcasting -- A biased future

AbstractThe handling of potential stakeholder bias distinguishes evaluation theorists from one another and from practitioners.

This paper introduces a socio-technical typology of bias in data-driven machine learning and artificial intelligence systems. The typology is linked to the conceptualisations of legal anti-discrimination regulations, so that the concept of structural inequality-and, therefore, of undesirable bias-is defined accordingly. By analysing the controversial Austrian "AMS algorithm" as a case study as well as examples in the contexts of face detection, risk assessment and health care management, this paper defines the following three types of bias: firstly, purely technical bias as a systematic deviation of the datafied version of a phenomenon from reality; secondly, socio-technical bias as a systematic deviation due to structural inequalities, which must be strictly distinguished from, thirdly, societal bias, which depicts-correctly-the structural inequalities that prevail in society. This paper argues that a clear distinction must be made between different concepts of bias in such systems in order to analytically assess these systems and, subsequently, inform political action.

Introduction. Fairness, politics, and American media -- Point: journalists are out of touch with America -- Counterpoint: journalists' views do not affect their reporting -- Point: the media have a liberal bias -- Counterpoint: claims of "biased media" are exaggerated -- Point: concentrated media ownership harms the public -- Counterpoint: the media are healthier than ever -- Conclusion: reforming the media

We demonstrate that female incumbents are of higher average candidate quality than male incumbents. This quality difference is the result of barriers to entry faced by potential female candidates, although the observed effects of this quality differential on vote share are partially masked by the fact that female incumbents are also more likely to be opposed or to be opposed by high-quality challengers. Using data from House elections for 1984-1992, we estimate that the gender-based differential in candidate quality yields an extra six percentage points of vote share for female incumbents. 7 Tables, 1 Appendix, 32 References. Adapted from the source document.