DOCSLIB.ORG

Confirmation Bias

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

A Thesis Submitted for the Degree of PhD at the University of Warwick Permanent WRAP URL: http://wrap.warwick.ac.uk/95233/ Copyright and reuse: This thesis is made available online and is protected by original copyright. Please scroll down to view the document itself. Please refer to the repository record for this item for information to help you to cite it. Our policy information is available from the repository home page. For more information, please contact the WRAP Team at: [email protected] warwick.ac.uk/lib-publications UNIVERSITY OF WARWICK The importance of making assumptions: why confirmation is not necessarily a bias by Jess Whittlestone A thesis submitted in partial fulfillment for the degree of Doctor of Philosophy in the Behavioural Science Group Warwick Business School July 2017 Contents List of Figures vi List of Tables vii Acknowledgements viii Declaration of Authorship x Abstract xii 1 Introduction 1 2 When is confirmation a bias? 7 2.1 Introduction . .7 2.2 Different types of confirmation bias: a review . .9 2.2.1 Bias in search . 12 2.2.1.1 Bias in hypothesis testing - Wason's original experiments 14 2.2.1.2 Subsequent research on hypothesis testing . 15 2.2.1.3 Selective exposure . 17 2.2.1.4 Bias in amount of time spent searching . 19 2.2.1.5 `Myside bias' in producing arguments . 20 2.2.1.6 Bias in search: discussion . 21 2.2.2 Bias in inference . 24 2.2.2.1 Biased interpretation of evidence . 26 2.2.2.2 Biased evaluation of evidence . 30 2.2.2.3 Bias in inference: discussion . 31 2.2.3 The relationship between bias in search and inference . 34 2.2.4 Biased beliefs: belief persistence and overconfidence . 35 2.2.4.1 Belief persistence . 36 2.2.4.2 Overconfidence . 39 2.2.4.3 Belief persistence and overconfidence: discussion . 41 2.2.4.4 Biased beliefs independent of search and inference? . 42 2.2.5 Summary: the evidence for confirmation bias . 44 2.3 The challenges to confirmation bias . 46 i CONTENTS ii 2.3.1 Strategies that look ‘confirmatory’ do not necessarily lead to a confirmation bias in all circumstances . 47 2.3.2 Strategies that look like they produce `bias' may in fact be accurate on average . 48 2.3.3 Problems with experimental design . 50 2.3.4 Lack of normative standards . 51 2.3.5 Bias in search and inference cannot be studied in isolation . 54 2.4 What remains of confirmation bias? . 56 2.5 Conclusion . 64 3 The mixed evidence for selective exposure 65 3.1 Introduction . 65 3.2 Background . 67 3.3 Past research on selective exposure . 69 3.3.1 A broad overview . 70 3.3.2 A narrower look at selective exposure - social and political attitudes 71 3.3.2.1 Strength of opinions and dissonance . 72 3.3.2.2 Goals and motivations . 74 3.3.2.3 Personality differences . 75 3.3.2.4 Other factors influencing information choice . 76 3.3.3 Naturalistic and field experiments . 78 3.3.4 Summary: the state of selective exposure research . 80 3.4 The present research . 82 3.4.1 Experimental paradigm . 82 3.4.2 Experiment 1: setting up a selective exposure paradigm . 83 3.4.2.1 Background . 83 3.4.2.2 Design and procedure . 84 3.4.2.3 Handling of moderates . 86 3.4.2.4 Results . 87 3.4.2.5 Analysis of moderates . 89 3.4.3 Experiments 2 and 3: how robust is the evidence for selective exposure? . 90 3.4.3.1 Background . 90 3.4.3.2 Design and procedure . 91 3.4.3.3 Results . 92 3.4.4 Experiment 4: a replication of Taber and Lodge, 2006 . 95 3.4.4.1 Background . 95 3.4.4.2 Design and procedure . 95 3.4.4.3 Results . 96 3.4.5 Experiments 5 and 6 . 97 3.4.6 Experiment 5: information source manipulation . 99 3.4.6.1 Design and procedure . 99 3.4.6.2 Results . 99 3.4.6.3 Participant's reported reasons for choices . 101 3.4.7 Experiment 6: information source manipulation 2 . 102 3.4.7.1 Design and procedure . 103 3.4.7.2 Results . 103 CONTENTS iii 3.5 Summary and discussion . 105 3.5.1 Summary . 105 3.5.2 Discussion: just another failed replication? . 106 3.6 General discussion . 110 3.6.1 Design issues in selective exposure research . 110 3.6.2 Different ways of measuring `selective exposure' . 111 3.6.3 Selective exposure and bias . 113 3.6.4 Selective exposure in the `real world' . 115 3.6.5 Final thoughts and implications . 116 4 Bias, rationality and improving human reasoning 118 4.1 Introduction . 118 4.2 What does it mean to be biased? . 120 4.2.1 `Bias' in different areas of research . 120 4.2.2 `Bias' in the confirmation bias literature . 124 4.3 Normative models . 127 4.3.1 The use of normative models in psychology . 127 4.3.2 Normative models in the study of confirmation bias . 129 4.4 What does it mean to be `rational'? . 132 4.4.1 The relationship between bias and rationality . 132 4.4.2 Different types of rationality . 134 4.4.2.1 Summary of different types of rationality . 135 4.4.2.2 Links and overlaps between different types of rationality 138 4.4.3 Disagreements about rationality: summary . 140 4.4.3.1 Substantive disagreement or terminological confusion? . 141 4.4.3.2 Disagreements about rationality in the confirmation bias literature . 144 4.4.4 Why does this matter? Rationality and improving human reasoning147 4.5 Summary . 150 4.5.1 What does it mean to be biased? . 150 4.5.2 Normative models in psychology . 151 4.5.3 Different types of rationality . 151 4.5.4 Bias, rationality, and improving reasoning . 153 4.6 Implications for confirmation bias . 153 5 Open-mindedness 157 5.1 Introduction . 157 5.2 What is open-mindedness? . 159 5.2.1 An intuitive picture of open-mindedness . 159 5.2.2 Open-mindedness in psychology . 161 5.2.2.1 Early accounts of open-mindedness . 161 5.2.2.2 Openness as a personality dimension in the five-factor model . 161 5.2.2.3 Open-mindedness as behaviour . 164 5.2.2.4 Open-mindedness in psychology: summary . 165 5.2.3 Open-mindedness in philosophy . 166 5.2.3.1 Open-mindedness as uncertainty . 166 CONTENTS iv 5.2.3.2 Open-mindedness as intellectual humility . 167 5.2.3.3 Open-mindedness as detachment or engagement . 169 5.2.3.4 Open-mindedness in philosophy: summary . 171 5.2.4 Summary: what is open-mindedness? . 172 5.3 Should we be more open-minded? . 173 5.3.1 The costs of open-mindedness . 174 5.3.2 Why open-mindedness is not a normative concept . 175 5.3.3 Why open-mindedness does not need to be a normative concept . 177 5.3.4 Open-mindedness as an explore-exploit tradeoff . 179 5.3.5 Open-mindedness and science . 180 5.3.6 Summary: would more open-mindedness be better? . 183 5.4 Implications . 184 5.4.1 Implications for the psychological study of open-mindedness . 185 5.4.2 Implications for promoting open-mindedness . 188 5.5 Conclusion . 190 6 Summary and discussion 192 6.1 Summary . 192 6.1.1 When is confirmation a bias? . 192 6.1.2 The mixed evidence for selective exposure . 193 6.1.3 Bias, rationality, and confirmation . 194 6.1.4 Open-mindedness . 195 6.1.5 Confirmation bias, open-mindedness, and Bayes' rule . 196 6.2 Discussion . 198 6.2.1 Why is the belief in confirmation bias so pervasive? . 198 6.2.1.1 A theoretical case for confirmation bias? . 199 6.2.1.2 Confirmatory reasoning as a (not necessarily irrational) tendency . 200 6.2.1.3 Confirmation bias as a convenient explanation . 201 6.2.1.4 Confusing confirmation bias for something else . 201 6.2.1.5 The role of emotions . 202 6.2.1.6 The role of trust in sources . 203 6.2.2 Confirmation bias and real-world problems . 205 6.2.2.1 Confirmation bias and politics . 208 6.2.2.2 Improving forecasting accuracy . 209 6.2.2.3 A new way of thinking about bias . 210 6.2.3 Implications for future research . 211 6.2.3.1 More attention and clarity around.
Recommended publications
  • (HCW) Surveys in Humanitarian Contexts in Lmics

    (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.
  • Lecture Misinformation

    Lecture Misinformation

    Quote of the Day: “A lie will go round the world while truth is pulling its boots on.” -- Baptist preacher Charles H. Spurgeon, 1859 Please fill out the course evaluations: https://uw.iasystem.org/survey/233006 Questions on the final paper Readings for next time Today’s class: misinformation and conspiracy theories Some definitions of fake news: • any piece of information Donald Trump dislikes more seriously: • “a type of yellow journalism or propaganda that consists of deliberate disinformation or hoaxes spread via traditional news media (print and broadcast) or online social media.” disinformation: “false information which is intended to mislead, especially propaganda issued by a government organization to a rival power or the media” misinformation: “false or inaccurate information, especially that which is deliberately intended to deceive” Some findings of recent research on fake news, disinformation, and misinformation • False news stories are 70% more likely to be retweeted than true news stories. The false ones get people’s attention (by design). • Some people inadvertently spread fake news by saying it’s false and linking to it. • Much of the fake news from the 2016 election originated in small-time operators in Macedonia trying to make money (get clicks, sell advertising). • However, Russian intelligence agencies were also active (Kate Starbird’s research). The agencies created fake Black Lives Matter activists and Blue Lives Matter activists, among other profiles. A quick guide to spotting fake news, from the Freedom Forum Institute: https://www.freedomforuminstitute.org/first-amendment- center/primers/fake-news-primer/ Fact checking sites are also essential for identifying fake news.
  • Incorporating Prior Domain Knowledge Into Inductive Machine Learning Its Implementation in Contemporary Capital Markets

    Incorporating Prior Domain Knowledge Into Inductive Machine Learning Its Implementation in Contemporary Capital Markets

    University of Technology Sydney Incorporating Prior Domain Knowledge into Inductive Machine Learning Its implementation in contemporary capital markets A dissertation submitted for the degree of Doctor of Philosophy in Computing Sciences by Ting Yu Sydney, Australia 2007 °c Copyright by Ting Yu 2007 CERTIFICATE OF AUTHORSHIP/ORIGINALITY I certify that the work in this thesis has not previously been submitted for a degree nor has it been submitted as a part of requirements for a degree except as fully acknowledged within the text. I also certify that the thesis has been written by me. Any help that I have received in my research work and the preparation of the thesis itself has been acknowledged. In addition, I certify that all information sources and literature used are indicated in the thesis. Signature of Candidate ii Table of Contents 1 Introduction :::::::::::::::::::::::::::::::: 1 1.1 Overview of Incorporating Prior Domain Knowledge into Inductive Machine Learning . 2 1.2 Machine Learning and Prior Domain Knowledge . 3 1.2.1 What is Machine Learning? . 3 1.2.2 What is prior domain knowledge? . 6 1.3 Motivation: Why is Domain Knowledge needed to enhance Induc- tive Machine Learning? . 9 1.3.1 Open Areas . 12 1.4 Proposal and Structure of the Thesis . 13 2 Inductive Machine Learning and Prior Domain Knowledge :: 15 2.1 Overview of Inductive Machine Learning . 15 2.1.1 Consistency and Inductive Bias . 17 2.2 Statistical Learning Theory Overview . 22 2.2.1 Maximal Margin Hyperplane . 30 2.3 Linear Learning Machines and Kernel Methods . 31 2.3.1 Support Vector Machines .
  • What Is the Function of Confirmation Bias?

    What Is the Function of Confirmation Bias?

    Erkenntnis https://doi.org/10.1007/s10670-020-00252-1 ORIGINAL RESEARCH What Is the Function of Confrmation Bias? Uwe Peters1,2 Received: 7 May 2019 / Accepted: 27 March 2020 © The Author(s) 2020 Abstract Confrmation bias is one of the most widely discussed epistemically problematic cognitions, challenging reliable belief formation and the correction of inaccurate views. Given its problematic nature, it remains unclear why the bias evolved and is still with us today. To ofer an explanation, several philosophers and scientists have argued that the bias is in fact adaptive. I critically discuss three recent proposals of this kind before developing a novel alternative, what I call the ‘reality-matching account’. According to the account, confrmation bias evolved because it helps us infuence people and social structures so that they come to match our beliefs about them. This can result in signifcant developmental and epistemic benefts for us and other people, ensuring that over time we don’t become epistemically disconnected from social reality but can navigate it more easily. While that might not be the only evolved function of confrmation bias, it is an important one that has so far been neglected in the theorizing on the bias. In recent years, confrmation bias (or ‘myside bias’),1 that is, people’s tendency to search for information that supports their beliefs and ignore or distort data contra- dicting them (Nickerson 1998; Myers and DeWall 2015: 357), has frequently been discussed in the media, the sciences, and philosophy. The bias has, for example, been mentioned in debates on the spread of “fake news” (Stibel 2018), on the “rep- lication crisis” in the sciences (Ball 2017; Lilienfeld 2017), the impact of cognitive diversity in philosophy (Peters 2019a; Peters et al.
  • Nonresponse Bias and Trip Generation Models

    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

    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.
  • Your Journey Is Loading. Scroll Ahead to Continue

    Your Journey Is Loading. Scroll Ahead to Continue

    Your journey is loading. Scroll ahead to continue. amizade.org • @AmizadeGSL Misinformation and Disinformation in the time of COVID-19 | VSL powered by Amizade | amizade.org ​2 Welcome! It is with great pleasure that Amizade welcomes you to what will be a week of learning around the abundance of misinformation and disinformation in both the traditional media as well as social media. We are so excited to share this unique opportunity with you during what is a challenging time in human history. We have more access to information and knowledge today than at any point in human history. However, in our increasingly hyper-partisan world, it has become more difficult to find useful and accurate information and distinguish between what is true and false. There are several reasons for this. Social media has given everyone in the world, if they so desire, a platform to spread information throughout their social networks. Many websites, claiming to be valid sources of news, use salacious headlines in order to get clicks and advertising dollars. Many “legitimate” news outlets skew research and data to fit their audiences’ political beliefs. Finally, there are truly bad actors, intentionally spreading false information, in order to sow unrest and further divide people. It seems that as we practice social distancing, connecting with the world has become more important than ever before. At the same time, it is exceedingly important to be aware of the information that you are consuming and sharing so that you are a part of the solution to the ongoing flood of false information. This program’s goal is to do just that—to connect you with the tools and resources you need to push back when you come across incorrect or intentionally misleading information, to investigate your own beliefs and biases, and to provide you with the tools to become a steward of good information.
  • Guidance for Health Care Worker (HCW) Surveys in Humanitarian

    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

    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.
  • Running Head: EXTRAVERSION PREDICTS REWARD SENSITIVITY

    Running Head: EXTRAVERSION PREDICTS REWARD SENSITIVITY

    Running head: EXTRAVERSION PREDICTS REWARD SENSITIVITY Extraversion but not Depression Predicts Reward Sensitivity: Revisiting the Measurement of Anhedonic Phenotypes Submitted: 6/xx/2020 EXTRAVERSION PREDICTS REWARD SENSITIVITY 2 Abstract RecentLy, increasing efforts have been made to define and measure dimensionaL phenotypes associated with psychiatric disorders. One example is a probabiListic reward task deveLoped by PizzagaLLi et aL. (2005) to assess anhedonia, by measuring participants’ responses to a differentiaL reinforcement schedule. This task has been used in many studies, which have connected blunted reward response in the task to depressive symptoms, across cLinicaL groups and in the generaL population. The current study attempted to replicate these findings in a large community sample and aLso investigated possible associations with Extraversion, a personaLity trait Linked theoreticaLLy and empiricaLLy to reward sensitivity. Participants (N = 299) completed the probabiListic reward task, as weLL as the Beck Depression Inventory, PersonaLity Inventory for the DSM-5, Big Five Inventory, and Big Five Aspect ScaLes. Our direct replication attempts used bivariate anaLyses of observed variables and ANOVA modeLs. FolLow-up and extension anaLyses used structuraL equation modeLs to assess reLations among Latent reward sensitivity, depression, Extraversion, and Neuroticism. No significant associations were found between reward sensitivity (i.e., response bias) and depression, thus faiLing to replicate previous findings. Reward sensitivity (both modeLed as response bias aggregated across blocks and as response bias controlLing for baseLine) showed positive associations with Extraversion, but not Neuroticism. Findings suggest reward sensitivity as measured by this probabiListic reward task may be reLated primariLy to Extraversion and its pathologicaL manifestations, rather than to depression per se, consistent with existing modeLs that conceptuaLize depressive symptoms as combining features of Neuroticism and low Extraversion.
  • Efficient Machine Learning Models of Inductive Biases Combined with the Strengths of Program Synthesis (PBE) to Combat Implicit Biases of the Brain

    Efficient Machine Learning Models of Inductive Biases Combined with the Strengths of Program Synthesis (PBE) to Combat Implicit Biases of the Brain

    The Dangers of Algorithmic Autonomy: Efficient Machine Learning Models of Inductive Biases Combined With the Strengths of Program Synthesis (PBE) to Combat Implicit Biases of the Brain The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Halder, Sumona. 2020. The Dangers of Algorithmic Autonomy: Efficient Machine Learning Models of Inductive Biases Combined With the Strengths of Program Synthesis (PBE) to Combat Implicit Biases of the Brain. Bachelor's thesis, Harvard College. Citable link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37364669 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA The Dangers of Algorithmic Autonomy: Efficient Machine Learning Models of Inductive Biases combined with the Strengths of Program Synthesis (PBE) to Combat Implicit Biases of the Brain A Thesis Presented By Sumona Halder To The Department of Computer Science and Mind Brain Behavior In Partial Fulfillment of the Requirements for the Degree of Bachelor of Arts in the Subject of Computer Science on the Mind Brain Behavior Track Harvard University April 10th, 2020 2 Table of Contents ABSTRACT 3 INTRODUCTION 5 WHAT IS IMPLICIT BIAS? 11 Introduction and study of the underlying cognitive and social mechanisms 11 Methods to combat Implicit Bias 16 The Importance of Reinforced Learning 19 INDUCTIVE BIAS and MACHINE LEARNING ALGORITHMS 22 What is Inductive Bias and why is it useful? 22 How is Inductive Reasoning translated in Machine Learning Algorithms? 25 A Brief History of Inductive Bias Research 28 Current Roadblocks on Research with Inductive Biases in Machines 30 How Inductive Biases have been incorporated into Reinforced Learning 32 I.
  • Confirmation Bias

    Confirmation Bias

    CONFIRMATION BIAS PATRICK BARRY* ABSTRACT Supreme Court confirmation hearings are vapid. Supreme Court confirmation hearings are pointless. Supreme Court confirmation hearings are harmful to a citizenry already cynical about government. Sentiments like these have been around for decades and are bound to resurface each time a new nomination is made. This essay, however, takes a different view. It argues that Supreme Court confirmation hearings are a valuable form of cultural expression, one that provides a unique record of, as the theater critic Martin Esslin might say, a nation thinking about itself in public. The theatre is a place where a nation thinks in public in front of itself. —Martin Esslin1 The Supreme Court confirmation process—once a largely behind-the- scenes affair—has lately moved front-and-center onto the public stage. —Laurence H. Tribe2 INTRODUCTION That Supreme Court confirmation hearings are televised unsettles some legal commentators. Constitutional law scholar Geoffrey Stone, for example, worries that publicly performed hearings encourage grandstanding; knowing their constituents will be watching, senators unhelpfully repeat questions they think the nominee will try to evade—the goal being to make the nominee look bad and themselves look good.3 Stone even suggests the country might be * Clinical Assistant Professor, University of Michigan Law School. © 2017. I thank for their helpful comments and edits Enoch Brater, Martha Jones, Eva Foti-Pagan, Sidonie Smith, and James Boyd White. I am also indebted to Alexis Bailey and Hannah Hoffman for their wonderful work as research assistants. 1. MARTIN ESSLIN, AN ANATOMY OF DRAMA 101 (1977). 2. Laurence H. Tribe, Foreword to PAUL SIMON, ADVICE AND CONSENT: CLARENCE THOMAS, ROBERT BORK AND THE INTRIGUING HISTORY OF THE SUPREME COURT’S NOMINATION BATTLES 13 (1992).