Is Bad News Biased? How Poll Reporting Affects Perceptions of Media Bias and Presumed Voter Behavior
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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 -
Studying Political Bias Via Word Embeddings
Studying Political Bias via Word Embeddings Josh Gordon Marzieh Babaeianjelodar Jeanna Matthews Clarkson University jogordo,babaeim,[email protected] ABSTRACT 1 INTRODUCTION Machine Learning systems learn bias in addition to other patterns In 2016, Bolukbasi et al. [1] published an influential paper demon- from input data on which they are trained. Bolukbasi et al. pio- strating a method to quantify and remove gender bias that a ma- neered a method for quantifying gender bias learned from a corpus chine learning (ML) model learned from a corpus of human text. of text. Specifically, they compute a gender subspace into which This is important for revealing bias in human text as well as re- words, represented as word vectors, can be placed and compared ducing the impact biased ML systems can have on hiring, housing, with one another. In this paper, we apply a similar methodology and credit. In this paper, we explore how we might use a similar to a different type of bias, political bias. Unlike with gender bias, methodology to model other kinds of bias such as political bias. it is not obvious how to choose a set of definitional word pairs to As with gender, we begin as Bolukbasi et al. did with attempting compute a political bias subspace. We propose a methodology for to model political bias as simply two binary extremes along a single doing so that could be used for modeling other types of bias as well. axis. Neither gender nor political bias is as simple in the real world We collect and examine a 26 GB corpus of tweets from Republican as two points on a single axis, but we wanted to see how useful and Democratic politicians in the United States (presidential candi- this model could be in the case of political bias. -
The Effects of the Intuitive Prosecutor Mindset on Person Memory
THE EFFECTS OF THE INTUITIVE PROSECUTOR MINDSET ON PERSON MEMORY DISSERTATION Presented in Partial Fulfillment of the Requirements for The Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Richard J. Shakarchi, B.S., M.A. ***** The Ohio State University 2002 Dissertation Committee: Professor Marilynn Brewer, Adviser Approved by Professor Gifford Weary ________________________________ Adviser Professor Neal Johnson Department of Psychology Copyright by Richard J. Shakarchi 2002 ABSTRACT The intuitive prosecutor metaphor of human judgment is a recent development within causal attribution research. The history of causal attribution is briefly reviewed, with an emphasis on explanations of attributional biases. The evolution of such explanations is traced through a purely-cognitive phase to the more modern acceptance of motivational explanations for attributional biases. Two examples are offered of how a motivational explanatory framework of attributional biases can account for broad patterns of information processing biases. The intuitive prosecutor metaphor is presented as a parallel explanatory framework for interpreting attributional biases, whose motivation is based on a threat to social order. The potential implications for person memory are discussed, and three hypotheses are developed: That intuitive prosecutors recall norm- violating information more consistently than non-intuitive prosecutors (H1); that this differential recall may be based on differential (biased) encoding of behavioral information (H2); and that this differential recall may also be based on biased retrieval of information from memory rather than the result of a reporting bias (H3). A first experiment is conducted to test the basic recall hypothesis (H1). A second experiment is conducted that employs accountability in order to test the second and third hypotheses. -
Outcome Reporting Bias in COVID-19 Mrna Vaccine Clinical Trials
medicina Perspective Outcome Reporting Bias in COVID-19 mRNA Vaccine Clinical Trials Ronald B. Brown School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L3G1, Canada; [email protected] Abstract: Relative risk reduction and absolute risk reduction measures in the evaluation of clinical trial data are poorly understood by health professionals and the public. The absence of reported absolute risk reduction in COVID-19 vaccine clinical trials can lead to outcome reporting bias that affects the interpretation of vaccine efficacy. The present article uses clinical epidemiologic tools to critically appraise reports of efficacy in Pfzier/BioNTech and Moderna COVID-19 mRNA vaccine clinical trials. Based on data reported by the manufacturer for Pfzier/BioNTech vaccine BNT162b2, this critical appraisal shows: relative risk reduction, 95.1%; 95% CI, 90.0% to 97.6%; p = 0.016; absolute risk reduction, 0.7%; 95% CI, 0.59% to 0.83%; p < 0.000. For the Moderna vaccine mRNA-1273, the appraisal shows: relative risk reduction, 94.1%; 95% CI, 89.1% to 96.8%; p = 0.004; absolute risk reduction, 1.1%; 95% CI, 0.97% to 1.32%; p < 0.000. Unreported absolute risk reduction measures of 0.7% and 1.1% for the Pfzier/BioNTech and Moderna vaccines, respectively, are very much lower than the reported relative risk reduction measures. Reporting absolute risk reduction measures is essential to prevent outcome reporting bias in evaluation of COVID-19 vaccine efficacy. Keywords: mRNA vaccine; COVID-19 vaccine; vaccine efficacy; relative risk reduction; absolute risk reduction; number needed to vaccinate; outcome reporting bias; clinical epidemiology; critical appraisal; evidence-based medicine Citation: Brown, R.B. -
Working Memory, Cognitive Miserliness and Logic As Predictors of Performance on the Cognitive Reflection Test
Working Memory, Cognitive Miserliness and Logic as Predictors of Performance on the Cognitive Reflection Test Edward J. N. Stupple ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Maggie Gale ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Christopher R. Richmond ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Abstract Most participants respond that the answer is 10 cents; however, a slower and more analytic approach to the The Cognitive Reflection Test (CRT) was devised to measure problem reveals the correct answer to be 5 cents. the inhibition of heuristic responses to favour analytic ones. The CRT has been a spectacular success, attracting more Toplak, West and Stanovich (2011) demonstrated that the than 100 citations in 2012 alone (Scopus). This may be in CRT was a powerful predictor of heuristics and biases task part due to the ease of administration; with only three items performance - proposing it as a metric of the cognitive miserliness central to dual process theories of thinking. This and no requirement for expensive equipment, the practical thesis was examined using reasoning response-times, advantages are considerable. There have, moreover, been normative responses from two reasoning tasks and working numerous correlates of the CRT demonstrated, from a wide memory capacity (WMC) to predict individual differences in range of tasks in the heuristics and biases literature (Toplak performance on the CRT. These data offered limited support et al., 2011) to risk aversion and SAT scores (Frederick, for the view of miserliness as the primary factor in the CRT. -
The Qanon Conspiracy
THE QANON CONSPIRACY: Destroying Families, Dividing Communities, Undermining Democracy THE QANON CONSPIRACY: PRESENTED BY Destroying Families, Dividing Communities, Undermining Democracy NETWORK CONTAGION RESEARCH INSTITUTE POLARIZATION AND EXTREMISM RESEARCH POWERED BY (NCRI) INNOVATION LAB (PERIL) Alex Goldenberg Brian Hughes Lead Intelligence Analyst, The Network Contagion Research Institute Caleb Cain Congressman Denver Riggleman Meili Criezis Jason Baumgartner Kesa White The Network Contagion Research Institute Cynthia Miller-Idriss Lea Marchl Alexander Reid-Ross Joel Finkelstein Director, The Network Contagion Research Institute Senior Research Fellow, Miller Center for Community Protection and Resilience, Rutgers University SPECIAL THANKS TO THE PERIL QANON ADVISORY BOARD Jaclyn Fox Sarah Hightower Douglas Rushkoff Linda Schegel THE QANON CONSPIRACY ● A CONTAGION AND IDEOLOGY REPORT FOREWORD “A lie doesn’t become truth, wrong doesn’t become right, and evil doesn’t become good just because it’s accepted by the majority.” –Booker T. Washington As a GOP Congressman, I have been uniquely positioned to experience a tumultuous two years on Capitol Hill. I voted to end the longest government shut down in history, was on the floor during impeachment, read the Mueller Report, governed during the COVID-19 pandemic, officiated a same-sex wedding (first sitting GOP congressman to do so), and eventually became the only Republican Congressman to speak out on the floor against the encroaching and insidious digital virus of conspiracy theories related to QAnon. Certainly, I can list the various theories that nest under the QAnon banner. Democrats participate in a deep state cabal as Satan worshiping pedophiles and harvesting adrenochrome from children. President-Elect Joe Biden ordered the killing of Seal Team 6. -
Blooming Where They're Planted: Closing Cognitive
BLOOMING WHERE THEY’RE PLANTED: CLOSING COGNITIVE ACHIEVEMENT GAPS WITH NON-COGNITIVE SKILLS Elaina Michele Sabatine A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the School of Social Work Chapel Hill 2019 Approved by: Melissa Lippold Amy Blank Wilson Natasha Bowen Din-Geng Chen Jill Hamm ©2019 Elaina Sabatine ALL RIGHTS RESERVED ii ABSTRACT ELAINA SABATINE: Blooming Where They’re Planted: Closing Cognitive Achievement Gaps With Non-Cognitive Skills (Under the direction of Dr. Melissa Lippold) For the last several decades, education reform has focused on closing achievement gaps between affluent, white students and their less privileged peers. One promising area for addressing achievement gaps is through promoting students’ non-cognitive skills (e.g., self- discipline, persistence). In the area of non-cognitive skills, two interventions – growth mindset and stereotype threat – have been identified as promising strategies for increasing students’ academic achievement and closing achievement gaps. This dissertation explores the use of growth mindset and stereotype threat strategies in the classroom, as a form of academic intervention. Paper 1 examines the extant evidence for growth mindset and stereotype threat interventions. Paper 1 used a systematic review and meta-analysis to identify and analyze 24 randomized controlled trials that tested growth mindset and stereotype threat interventions with middle and high school students over the course of one school year. Results from meta- analysis indicated small, positive effects for each intervention on students’ GPAs. Findings highlight the influence of variation among studies and the need for additional research that more formally evaluates differences in study characteristics and the impact of study characteristics on intervention effects. -
Ssi to Syndicate Chuck Woolery Daily Political Feature/Commentary – Save Us Chuck
PRESS RELEASE ______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ For additional info or assistance please contact: SSI @ (203) 431-0790 or www.SyndicatedSolutions.com SSI TO SYNDICATE CHUCK WOOLERY DAILY POLITICAL FEATURE/COMMENTARY – SAVE US CHUCK (Ridgefield, CT – 8 May 2012) Syndicated Solutions, Inc. - (SSI) announced today that it will nationally syndicate a daily Monday-Friday :60 second political feature/commentary hosted by Chuck Woolery. The program, SAVE US CHUCK, will be available nationwide on Monday, 4 June 2012. A short video detailing the program can be found at - http://www.youtube.com/watch?v=aQ9Og00HvUU. Poking fun at the politically ridiculous, separating fact from fiction, exposing the truths about Washington DC, working to spread common sense and sharing some laughs – Chuck Woolery is creating change in America. A proud conservative with a passion for politics – SAVE US CHUCK is an extension of Chuck’s wildly successful Save Us Chuck Woolery YouTube Channel that has been viewed by millions and his videos shared at CPAC, political events, cable news networks and more. A universally recognized TV legend, Chuck is well known from his years hosting Love Connection, Wheel of Fortune, Scrabble, Lingo and Greed. He’s also hosted television talk shows - The Home and Family Show and The Chuck Woolery Show. Today Chuck is a frequent contributor to Fox News and appears regularly with Sean Hannity, Glenn Beck, Dennis Miller and others. Now via SAVE US CHUCK, he’s bringing his unique style and entertaining political commentary to the radio dial. America loves Chuck Woolery and so will you as SAVE US CHUCK is a winner that’s changing radio forever while helping to save America one daily broadcast at time! Bob Carey, President of SSI, stated “When you think of recognizable personalities in America, Chuck Woolery certainly makes the list. -
Qanon • 75 Years of the Bomb • Vaccine History • Raising
SQANON • K75 YEARS OF ETHE BOMB P• VACCINE HISTORYT • RAISINGI CTHE DEAD? Extraordinary Claims, Revolutionary Ideas & the Promotion of Science—Vol.25Science—Vol.25 No.4No.4 2020 $6.95 USA and Canada www.skeptic.com • WHAT IS QANON? • HOW QANON RECYCLES CENTURIES-OLD CONSPIRACY BELIEFS • HOW QANON HURTS THEIR OWN CAUSE • QANON IN CONSPIRATORIAL CONTEXT watch or listen for free Hear leading scientists, scholars, and thinkers discuss the most important issues of our time. Hosted by Michael Shermer. #146 Dr. DonalD Prothero— # 130 Dr. DeBra Soh—the end # 113 Dave ruBIn— # 106 Dr. DanIel ChIrot— Weird earth: Debunking Strange of Gender: Debunking the Myths Don’t Burn this Book: you Say you Want a revolution? Ideas about our Planet about Sex & Identity in our Society thinking for yourself in an radical Idealism and its tragic age of unreason Consequences #145 GreG lukIanoff—Mighty # 129 Dr. Mona Sue WeISSMark Ira: the aClu’s controversial involve- —the Science of Diversity # 112 ann Druyan—Cosmos: # 105 Dr. DIana PaSulka— ment in the Skokie case of 1977. Possible Worlds. how science and american Cosmic: ufos, # 128 MIChael ShellenBerGer civilization grew up together religion, and technology #144 Dr. aGuStIn fuenteS— —apocalypse never: Why environ- Why We Believe: evolution and the mental alarmism hurts us all human Way of Being # 127 Dr. WIllIaM Perry and #143 Dr. nICholaS ChrIStakIS— toM CollIna—the Button: the apollo’s arrow: the Profound and new nuclear arms race and Presi- enduring Impact of Coronavirus on dential Power from truman to trump the Way We live # 126 Sarah SColeS—they are #142 Dr. -
Stereotypes, Role Models, and the Formation of Beliefs
Stereotypes, Role Models, and the Formation of Beliefs Alex Eble and Feng Hu⇤ September 2017 Abstract Stereotypes can erroneously reduce children’s beliefs about their own ability, negatively affect- ing effort in school and investment in skills. Because of dynamic complementarity in the formation of human capital, this may lead to large negative effects on later life outcomes. In this paper, we show evidence that role models, in the form of female math teachers, can counter the negative effects of gender-based stereotypes on girls’ perceptions of their math ability. A simple model of investment in human capital under uncertainty predicts the greatest effects of role models ac- cruing to girls who perceive themselves to be of low ability. We exploit random assignment of students to classes in Chinese middle schools to test this prediction, examining the effects of teacher-student gender match on students’ perceived ability, aspirations, investment, and aca- demic performance. We find that, for girls who perceive themselves to be of low ability, being assigned a female math teacher causes large gains in all of these outcomes. We see no effects of teacher-student gender match on children who do not perceive themselves to be of low math ability. We show evidence against the potential alternative explanations that differential teacher effort, skill, teaching methods, or differential attention to girls drive these effects. ⇤Eble: Teachers College, Columbia University. Email: [email protected] Hu: School of Economics and Manage- ment, University of Science and Technology Beijing [email protected]. The authors are grateful to Joe Cummins, John Friedman, Morgan Hardy, Asim Khwaja, Ilyana Kuziemko, Bentley MacCleod, Randy Reback, Jonah Rockoff, Judy Scott- Clayton, and Felipe Valencia for generous input, as well as seminar audiences at Columbia, Fordham, the 2017 IZA transat- lantic meeting, and PacDev. -
Mitigating Political Bias in Language Models Through Reinforced Calibration
Mitigating Political Bias in Language Models Through Reinforced Calibration Ruibo Liu,1 Chenyan Jia, 2 Jason Wei, 3 Guangxuan Xu, 1 Lili Wang, 1 Soroush Vosoughi 1 1 Department of Computer Science, Dartmouth College 2 Moody College of Communication, University of Texas at Austin 3 ProtagoLabs [email protected], [email protected] Abstract with particular keywords of the aforementioned attributes, and 2) Direct Bias, which measures bias in texts generated Current large-scale language models can be politically bi- using prompts that have directly ideological triggers (e.g., ased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real- democrat, republican) in addition to keywords of aforemen- world settings. In this paper, we describe metrics for mea- tioned attributes. Table 1 shows four samples of text gen- suring political bias in GPT-2 generation and propose a re- erated by off-the-shelf GPT-2 with different attribute key- inforcement learning (RL) framework for mitigating political words in the prompts—all samples exhibit political bias. biases in generated text. By using rewards from word em- For example, when triggered with a prompt including mar- beddings or a classifier, our RL framework guides debiased ijuana, the generated text tends to present a favorable atti- generation without having access to the training data or re- tude (e.g., “I believe it should be legal and not regulated.”), quiring the model to be retrained. In empirical experiments which is mostly a liberal stance. More interestingly, even on three attributes sensitive to political bias (gender, loca- a prompt including a conservative trigger (republican) re- tion, and topic), our methods reduced bias according to both sults in generation which leans to the liberal side (“vote for our metrics and human evaluation, while maintaining read- ability and semantic coherence. -
1 Does Political Affiliation Trump Outcome Bias? Evan D. Lester
Does Political Affiliation Trump Outcome Bias? 1 Does Political Affiliation Trump Outcome Bias? Evan D. Lester Department of Psychology Hampden-Sydney College Does Political Affiliation Trump Outcome Bias? 2 Abstract Research in the field of judgment and decision making has consistently shown that information pertaining to the outcome of a decision has a significant impact on people’s attitudes of the decision itself. This effect is referred to as outcome bias. Data was collected from approximately equal numbers of Republicans and Democrats. Participants were presented with descriptions and outcomes of decisions made by a hypothetical politician. The decisions concerned public policies in response to the Coronavirus (COVID-19) pandemic. Participants evaluated the quality of the thinking that went into each decision. Results showed that policies that yielded successful outcomes received significantly better evaluations than policies that yielded failures. Democrats exhibited this tendency to a greater extent compared to Republicans. Conversely, Republicans exhibited a greater bias toward their own political party than did Democrats. The findings of this project are discussed within the context of classic and contemporary findings in the field of judgment and decision making. Keywords: Outcome Bias, Hindsight Bias, Political Affiliation Does Political Affiliation Trump Outcome Bias? 3 Does Political Affiliation Trump Outcome Bias? Individuals make countless decisions every day. Some decisions are trivial (i.e. what to eat) while other decisions can impact many people (i.e. public policy decisions). But how do individuals decide what makes a good or bad decision? In explaining how individuals evaluate decision quality, expected utility theory (EUT) suggests that individuals are rational and deliberate in their estimates of the options available.