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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

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.