DOCSLIB.ORG

Puja Kapai Associate Professor of Law Convenor, Women's Studies

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Puja Kapai Associate Professor of Law Convenor, Women's Studies Puja Kapai Associate Professor of Law Convenor, Women’s Studies Research Centre Centre for Comparative and Public Law University of Hong Kong September 2019 1 TABLE OF CONTENTS ACKNOWLEDGEMENTS ........................................................................................................................................................................ 4 ABOUT THE AUTHOR ............................................................................................................................................................................ 6 LIST OF ABBREVIATIONS .................................................................................................................................................................... 7 INTRODUCTION ....................................................................................................................................................................................... 8 LITERATURE REVIEW OF UNCONSCIOUS BIAS RESEARCH AND INTERVENTIONS .......................................................... 16 Rewiring the Brain – Do Interventions Work ...................................................................................................................... 17 The Role and Limitations of Law and Policy ........................................................................................................................ 20 The Significance of Cultural Context in Doing Equality Consciously .......................................................................... 33 OBJECTIVES ............................................................................................................................................................................................ 43 Expected Outcomes and Social Impact ................................................................................................................................... 44 METHODOLOGY .................................................................................................................................................................................... 45 Research Design ............................................................................................................................................................................... 48 Research Sampling .......................................................................................................................................................................... 49 Research Instruments .................................................................................................................................................................... 52 Research Procedures ...................................................................................................................................................................... 68 Scoring ............................................................................................................................................................................................. 69 Limitations ..................................................................................................................................................................................... 70 RESULTS AND DATA ANALYSIS ..................................................................................................................................................... 73 Scoring .................................................................................................................................................................................................. 73 Detailed Research Findings ......................................................................................................................................................... 73 Comparing Differentials Between IAT1 and IAT2 ......................................................................................................... 74 Comparing Differentials Between Control and Intervention Groups.................................................................... 74 Comparing Differentials Between Males and Females as a Group ......................................................................... 81 Comparing IAT Differentials By Social Groups ............................................................................................................... 87 Contextualising IAT Data Against the Questionnaire Data ........................................................................................ 91 Correlating data with Post-IAT Behavioural Tasks .................................................................................................... 105 DISCUSSION .......................................................................................................................................................................................... 106 © 2019 Puja Kapai | The University of Hong Kong 2 CONCLUSION ........................................................................................................................................................................................ 108 RECOMMENDATIONS ....................................................................................................................................................................... 111 REFERENCES ........................................................................................................................................................................................ 118 APPENDICES ......................................................................................................................................................................................... 136 Appendix I .................................................................................................................................................................................... 136 Appendix II .................................................................................................................................................................................. 118 Appendix III ................................................................................................................................................................................. 118 © 2019 Puja Kapai | The University of Hong Kong 3 Published in 2019 Women’s Studies Research Centre & Centre for Comparative and Public Law Faculty of Law, The University of Hong Kong All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written consent of the author or publisher, except for brief quotations accordingly attributed. © Puja Kapai © 2019 Puja Kapai | The University of Hong Kong 4 ACKNOWLEDGEMENTS Doing Equality Consciously: Understanding Unconscious Bias and its Role and Implications in the Achievement of Equality in Hong Kong and Asia is a project funded by the Hong Kong Equal Opportunities Commission (EOC). The project is housed at the University of Hong Kong’s Women’s Studies Research Centre (WSRC) and the Faculty of Law’s Centre for Comparative and Public Law (CCPL), both of which provided extensive administrative support for the project. WSRC is the only HKU-based entity which brings researchers, policy makers, leaders from different sectors within the broader community together around issues of gender, sexuality and diversity across disciplines, cultures, and contexts. WSRC has historically been and continues to be uniquely placed due to its cross- university and cross-community connections to drive research, conversations and discourses on gender, diversity and equity and to do capacity-building work as a form of applied research. WSRC has served as a hub where people come to to collaborate and share in research and knowledge-exchange initiatives, expertise about the latest theoretical and intellectual developments in the fields of women’s/gender/sexuality/queer/intersectionality studies. For further information about the WSRC, please visit: https://www.wsrcweb.hku.hk/. CCPL was established in 1995 as a non-profit virtual research centre in the Faculty of Law. Its goals are to (1) advance knowledge on public law and human rights issues primarily from the perspectives of international and comparative law and practice; (2) encourage and facilitate collaborative work within the Faculty of Law, the University of Hong Kong, and the broader community in the fields of comparative and public law; and (3) make the law more accessible to the community and more effective as an agent of social change. The Centre’s projects and events generally come within one of the following themes: Comparative Public Policy; Comparative Human Rights; Constitutional Societies; and International Law in the Domestic Order. For further information about CCPL, please visit: https://www.law.hku.hk/ccpl/. Both Centres have, over the years, led numerous research studies and made contributions on various aspects of human rights, including submissions to the Hong Kong Legislative Council, the Government of the Hong Kong Special Administrative Region and United Nations treaty bodies. Centre colleagues regularly collaborate with academics and researchers at local, regional and international universities, research institues and civil society organisations in furtherance of the aims and objectives of the Centres. I would like to
Load more

Recommended publications
  • Playing Prejudice: the Impact of Game-Play on Attributions of Gender and Racial Bias
    Playing Prejudice: The Impact of Game-Play on Attributions of Gender and Racial Bias Jessica Hammer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Jessica Hammer All rights reserved ABSTRACT Playing Prejudice: The Impact of Game-Play on Attributions of Gender and Racial Bias Jessica Hammer This dissertation explores new possibilities for changing Americans' theories about racism and sexism. Popular American rhetorics of discrimination, and learners' naïve models, are focused on individual agents' role in creating bias. These theories do not encompass the systemic and structural aspects of discrimination in American society. When learners can think systemically as well as agentically about bias, they become more likely to support systemic as well as individual remedies. However, shifting from an agentic to a systemic model of discrimination is both cognitively and emotionally challenging. To tackle this difficult task, this dissertation brings together the literature on prejudice reduction and conceptual change to propose using games as an entertainment-based intervention to change players' attribution styles around sexism and racism, as well as their attitudes about the same issues. “Playable model – anomalous data” theory proposes that games can model complex systems of bias, while instantiating learning mechanics that help players confront the limits of their existing models. The web-based
    [Show full text]
  • Request Letter to Government Office
    Request Letter To Government Office laurelledIronic and Godard bathetic thermostats Vibhu doubles almost her evangelically, immaterialness though annunciated Merwin complicateor quarters hisignobly. dodecasyllabic Esthonian and recolonisedmiscues. Lucius so reductively. geometrised his pattle buddings southward, but well-known Avraham never Of journalism certificate from the author, to request government letter is genuine, and why you You requested to government offices are requesting financial officer at missouri state bank account or governments do you for their request a starting point but in. Learn more supportive of your profile today and using this? You need the requirements you are actually read a polite and child health and paste this strategy reviews from manual rates is. Research shows the office management refuses to get the country sends to train someone to the locality. All elements in earning a banana and maximize geoarbitrage before taking surveys! Getting into other product to make it for your current implemented case, sending to hear about my commercial interest because it! Sample letter to even local Minister. You implement this office of government offices are the usg in question is very useful active voice. Certificates may request government offices may not understand. Afsac online fundraising goal and polite, or governments and by far from ucla is a loan, seeking assistance from you should quickly. Use the blue letter provided against you on each Urgent Action level a guide Salutations. Sample Texas Public Information Act of Letter Note Wording does quality need get exactly thought this gorgeous letter. How To propagate Free Money 14 Effortless Ways Clever Girl Finance. Sample invitation letter if a government official.
    [Show full text]
  • Making Sense of a Complex Artistry: a Narrative Inquiry of TIE Actors’ Practice in Two Issue-Based, Interactive Theatre-In-Education Works
    Making Sense of a Complex Artistry: A Narrative Inquiry of TIE Actor's Practice in Two Issue-based, Interactive Theatre- in-Education Works Author Chan, Yuk-Lan Published 2017-03 Thesis Type Thesis (PhD Doctorate) School School Educ & Professional St DOI https://doi.org/10.25904/1912/3738 Copyright Statement The author owns the copyright in this thesis, unless stated otherwise. Downloaded from http://hdl.handle.net/10072/373967 Griffith Research Online https://research-repository.griffith.edu.au Making Sense of a Complex Artistry: A Narrative Inquiry of TIE Actors’ Practice in Two Issue-based, Interactive Theatre-in-Education Works Chan, Yuk-Lan Phoebe BBA (Hong Kong) MA in Drama-in-Education (Birmingham) School of Education and Professional Studies Griffith University This thesis is submitted in fulfilment of the requirement of the degree of Doctor of Philosophy March 2017 Statement of Originality This work has not been previously submitted for a degree or diploma in any university. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made in the thesis itself. 31 March 2017 i Abstract Despite growing research focusing on the application of Theatre-in-Education (TIE) in various educational and community settings, limited exploration has been completed on the practice of actors who engage in TIE works. In particular, insufficient attention has been paid to the complex demands this form places on TIE actors and as such, the artistry required of them. This thesis addresses these gaps by exploring the experiences of nine TIE actors engaged in two issue-based, interactive TIE works presented in Hong Kong.
    [Show full text]
  • ALGORITHMIC BIAS EXPLAINED How Automated Decision-Making Becomes Automated Discrimination
    ALGORITHMIC BIAS EXPLAINED How Automated Decision-Making Becomes Automated Discrimination Algorithmic Bias Explained | 1 Table of Contents Introduction 3 • What Are Algorithms and How Do They Work? • What Is Algorithmic Bias and Why Does it Matter? • Is Algorithmic Bias Illegal? • Where Does Algorithmic Bias Come From? Algorithmic Bias in Healthcare 10 Algorithmic Bias in Employment 12 Algorithmic Bias in Government Programs 14 Algorithmic BIas in Education 16 Algorithmic Bias in Credit and Finance 18 Algorithmic Bias in Housing and Development 21 Algorithmic BIas in Everything Else: Price Optimization Algorithms 23 Recommendations for Fixing Algorithmic Bias 26 • Algorithmic Transparency and Accountability • Race-Conscious Algorithms • Algorithmic Greenlining Conclusion 32 Introduction Over the last decade, algorithms have replaced decision-makers at all levels of society. Judges, doctors and hiring managers are shifting their responsibilities onto powerful algorithms that promise more data-driven, efficient, accurate and fairer decision-making. However, poorly designed algorithms threaten to amplify systemic racism by reproducing patterns of discrimination and bias that are found in the data algorithms use to learn and make decisions. “We find it important to state that the benefits of any technology should be felt by all of us. Too often, the challenges presented by new technology spell out yet another tale of racism, sexism, gender inequality, ableism and lack of consent within digital 1 culture.” —Mimi Onuoha and Mother Cyborg, authors, “A People’s Guide to A.I.” The goal of this report is to help advocates and policymakers develop a baseline understanding of algorithmic bias and its impact as it relates to socioeconomic opportunity across multiple sectors.
    [Show full text]
  • Chevron's Abusive Litigation in Ecuador
    Rainforest Chernobyl Revisited† The Clash of Human Rights and BIT Investor Claims: Chevron’s Abusive Litigation in Ecuador’s Amazon by Steven Donziger,* Laura Garr & Aaron Marr Page** a marathon environmental litigation: Seventeen yearS anD Counting he last time the environmental lawsuit Aguinda v. ChevronTexaco was discussed in these pages, the defen- Tdant Chevron Corporation1 had just won a forum non conveniens dismissal of the case from a U.S. federal court to Ecuador after nine years of litigation. Filed in 1993, the lawsuit alleged that Chevron’s predecessor company, Texaco, while it exclusively operated several oil fields in Ecuador’s Amazon from 1964 to 1990, deliberately dumped billions of gallons of toxic waste into the rainforest to cut costs and abandoned more than 900 large unlined waste pits that leach toxins into soils and groundwater. The suit contended that the contamination poisoned an area the size of Rhode Island, created a cancer epi- demic, and decimated indigenous groups. During the U.S. stage of the litigation, Chevron submitted fourteen sworn affidavits attesting to the fairness and adequacy of Ecuador’s courts. The company also drafted a letter that was By Lou Dematteis/Redux. Steven Donziger, attorney for the affected communities, speaks with signed by Ecuador’s then ambassador to the United States, a Huaorani women outside the Superior Court at the start of the Chevron former Chevron lawyer, asking the U.S. court to send the case trial on October 21, 2003 in Lago Agrio in the Ecuadoran Amazon. to Ecuador.2 Representative of Chevron’s position was the sworn statement from Dr.
    [Show full text]
  • Accountability As a Debiasing Strategy: Does Race Matter?
    Accountability as a Debiasing Strategy: Does Race Matter? Jamillah Bowman Williams, J.D., Ph.D. Georgetown University Law Center Paper Presented at CULP Colloquium Duke University May 19th, 2016 1 Introduction Congress passed Title VII of the Civil Rights Act of 1964 with the primary goal of integrating the workforce and eliminating arbitrary bias against minorities and other groups who had been historically excluded. Yet substantial research reveals that racial bias persists and continues to limit opportunities and outcomes for racial minorities in the workplace. It has been argued that having a sense of accountability, or “the implicit or explicit expectation that one may be called on to justify one’s beliefs, feelings, and actions to others,” can decrease the influence of bias.1 This empirical study seeks to clarify the conditions under which accountability to a committee of peers influences bias and behavior. This project builds on research by Sommers et al. (2006; 2008) that found whites assigned to racially diverse groups generated a wider range of perspectives, processed facts more thoroughly, and were more likely to discuss polarizing social issues than all-white groups.2 This project extends this line of inquiry to the employment discrimination context to empirically examine how a committee’s racial composition influences the decision making process. For example, when making a hiring or promotion decision, does accountability to a racially diverse committee lead to more positive outcomes than accountability to a homogeneous committee? More specifically, how does race of the committee members influence complex thinking, diversity beliefs, acknowledgement of structural discrimination, and inclusive promotion decisions? Implications for antidiscrimination law and EEO policy will be discussed.
    [Show full text]
  • OFFICIAL RECORD of PROCEEDINGS Thursday, 22 June
    LEGISLATIVE COUNCIL ― 22 June 2017 10405 OFFICIAL RECORD OF PROCEEDINGS Thursday, 22 June 2017 The Council continued to meet at Nine o'clock MEMBERS PRESENT: THE PRESIDENT THE HONOURABLE ANDREW LEUNG KWAN-YUEN, G.B.S., J.P. THE HONOURABLE LEUNG YIU-CHUNG THE HONOURABLE ABRAHAM SHEK LAI-HIM, G.B.S., J.P. THE HONOURABLE TOMMY CHEUNG YU-YAN, G.B.S., J.P. PROF THE HONOURABLE JOSEPH LEE KOK-LONG, S.B.S., J.P. THE HONOURABLE JEFFREY LAM KIN-FUNG, G.B.S., J.P. THE HONOURABLE WONG TING-KWONG, S.B.S., J.P. THE HONOURABLE STARRY LEE WAI-KING, S.B.S., J.P. THE HONOURABLE CHAN HAK-KAN, B.B.S., J.P. THE HONOURABLE CHAN KIN-POR, B.B.S., J.P. DR THE HONOURABLE PRISCILLA LEUNG MEI-FUN, S.B.S., J.P. THE HONOURABLE WONG KWOK-KIN, S.B.S., J.P. THE HONOURABLE MRS REGINA IP LAU SUK-YEE, G.B.S., J.P. 10406 LEGISLATIVE COUNCIL ― 22 June 2017 THE HONOURABLE PAUL TSE WAI-CHUN, J.P. THE HONOURABLE LEUNG KWOK-HUNG# THE HONOURABLE CLAUDIA MO THE HONOURABLE STEVEN HO CHUN-YIN, B.B.S. THE HONOURABLE FRANKIE YICK CHI-MING, J.P. THE HONOURABLE WU CHI-WAI, M.H. THE HONOURABLE YIU SI-WING, B.B.S. THE HONOURABLE MA FUNG-KWOK, S.B.S., J.P. THE HONOURABLE CHARLES PETER MOK, J.P. THE HONOURABLE CHAN CHI-CHUEN THE HONOURABLE CHAN HAN-PAN, J.P. THE HONOURABLE LEUNG CHE-CHEUNG, B.B.S., M.H., J.P.
    [Show full text]
  • 7. the Names Entry in People When Reporting to the IRD Cause
    7. The Names Entry in People when Reporting to the IRD Cause: According to the IRD, surnames made up of two Chinese characters (for example, AU YEUNG, SZE TO) should be reported in either one of the following two formats:- a. No space between the 2 characters, for example, AUYEUNG, SZETO b. A hyphen (-) between the 2 characters, for example, AU-YEUNG, SZE-TO For a married female, her maiden name should be used as the employee’s surname. Solutions:- (A) Last Names made up of 2 Chinese characters should be entered as per the example below – in the "Last" name field of the People form: “AU YEUNG” should be entered as either “AUYEUNG” or “AU-YEUNG”. (B) For married female employees, the Last Name is taken from the "Previous Last Name” field in the "Further Name" tab (People form), if entered. Otherwise, it is obtained from the "Last" name field of the People form. Case 1: Without changing the name that appears on the HKID Card There is no need to modify any name fields to fulfill IRD requirements. Case 2: With husband surname added in HKID Card Example: Husband surname: Au Yeung; Maiden name: LEE Siu Ching Field name Before After System navigation Hong Kong Name Lee Siu Ching Au-Yeung Lee Siu People, Ching Employment tab, or Information Auyeung Lee Siu Ching Last Lee Au-Yeung Lee People or Auyeung Lee Previous Last Name (may be empty) Lee People, Further Name tab First Siu Ching Siu Ching People Full name to be Lee, Siu Ching Au-Yeung Lee, Siu Find People searched Ching or Auyeung Lee, Siu Ching Case 3: Maiden name changed to husband surname
    [Show full text]
  • How to Prevent Discriminatory Outcomes in Machine Learning
    White Paper How to Prevent Discriminatory Outcomes in Machine Learning Global Future Council on Human Rights 2016-2018 March 2018 Contents 3 Foreword 4 Executive Summary 6 Introduction 7 Section 1: The Challenges 8 Issues Around Data 8 What data are used to train machine learning applications? 8 What are the sources of risk around training data for machine learning applications? 9 What-if use case: Unequal access to loans for rural farmers in Kenya 9 What-if use case: Unequal access to education in Indonesia 9 Concerns Around Algorithm Design 9 Where is the risk for discrimination in algorithm design and deployment? 10 What-if use case: Exclusionary health insurance systems in Mexico 10 What-if scenario: China and social credit scores 11 Section 2: The Responsibilities of Businesses 11 Principles for Combating Discrimination in Machine Learning 13 Bringing principles of non-discrimination to life: Human rights due diligence for machine learning 14 Making human rights due diligence in machine learning effective 15 Conclusion 16 Appendix 1: Glossary/Definitions 17 Appendix 2: The Challenges – What Can Companies Do? 23 Appendix 3: Principles on the Ethical Design and Use of AI and Autonomous Systems 22 Appendix 4: Areas of Action Matrix for Human Rights in Machine Learning 29 Acknowledgements World Economic Forum® This paper has been written by the World Economic Forum Global Future Council on Human Rights 2016-18. The findings, interpretations and conclusions expressed herein are a result of a © 2018 – All rights reserved. collaborative process facilitated and endorsed by the World Economic Forum, but whose results No part of this publication may be reproduced or Transmitted in any form or by any means, including do not necessarily represent the views of the World Economic Forum, nor the entirety of its Photocopying and recording, or by any information Storage Members, Partners or other stakeholders, nor the individual Global Future Council members listed and retrieval system.
    [Show full text]
  • I Appreciate the Spirit of the Rule Change, and I Agree That We All
    From: Javed Abbas To: stevens, cheryl Subject: Comment On Proposed Rule Change - 250.2 Date: Saturday, March 27, 2021 8:42:43 PM Attachments: image002.png I appreciate the spirit of the rule change, and I agree that we all likely benefit by understanding the fundamental ways in which we are all connected, and that our society will either succeed or fail primarily based on our ability to cooperate with each other and treat each other fairly. However, I do not support the rule change because I do not think it will have the effect of making us more inclusive because of the ways humans tend to react when forced to do things. There will inevitably be push back from a group of people who do not support the spirit of the rule, that push back will probably be persuasive to people who don’t have strong feelings about the ideas one way or the other but will feel compelled to oppose the ideas because it will be portrayed as part of a political movement that they are inclined to oppose, and ultimately the Rule that is supposed to help us become more inclusive will actually help drive us further apart. I do think it’s a great idea to offer these CLEs and let them compete on equal footing in the marketplace of ideas. Rule Change: "Rule 250.2. CLE Requirements (1) CLE Credit Requirement. Every registered lawyer and every judge must complete 45 credit hours of continuing legal education during each applicable CLE compliance period as provided in these rules.
    [Show full text]
  • Language Identification for Person Names Based on Statistical Information
    Proceedings of PACLIC 19, the 19th Asia-Pacific Conference on Language, Information and Computation. Language Identification for Person Names Based on Statistical Information Shiho Nobesawa Ikuo Tahara Department of Information Sciences Department of Information Sciences Tokyo University of Science Tokyo University of Science 2641 Yamazaki, Noda 2641 Yamazaki, Noda Chiba, 278-8510, Japan Chiba, 278-8510, Japan [email protected] [email protected] Abstract Language identification has been an interesting and fascinating issue in natural language processing for decades, and there have been many researches on it. However, most of the researches are for documents, and though the possibility of high accuracy for shorter strings of characters, language identification for words or phrases has not been discussed much. In this paper we propose a statistical method of language identification for phrases, and show the empirical results for person names of 9 languages (12 areas). Our simple method based on n-gram and phrase length obtained more than 90% of accuracy for Japanese, Korean and Russian, and fair results for other languages except English. This result indicated the possibility of language identification for person names based on statistics, which is useful in multi-language person name detection and also let us expect the possibility of language identification for phrases with simple statistics-based methods. 1. Introduction The technology of language identification has become more important with the growth of the WWW. As Grefenstette reported in their paper (Grefenstette 2000) non-English languages are growing in recent years on the WWW, and the need for automatic language identification for both documents and phrases are increasing.
    [Show full text]
  • Bibliography: Bias in Artificial Intelligence
    NIST A.I. Reference Library Bibliography: Bias in Artificial Intelligence Abdollahpouri, H., Mansoury, M., Burke, R., & Mobasher, B. (2019). The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286. Abebe, R., Barocas, S., Kleinberg, J., Levy, K., Raghavan, M., & Robinson, D. G. (2020, January). Roles for computing in social change. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 252-260. Aggarwal, A., Shaikh, S., Hans, S., Haldar, S., Ananthanarayanan, R., & Saha, D. (2021). Testing framework for black-box AI models. arXiv preprint. Retrieved from https://arxiv.org/pdf/2102.06166.pdf Ahmed, N., & Wahed, M. (2020). The De-democratization of AI: Deep learning and the compute divide in artificial intelligence research. arXiv preprint arXiv:2010.15581. Retrieved from https://arxiv.org/ftp/arxiv/papers/2010/2010.15581.pdf. AI Now Institute. Algorithmic Accountability Policy Toolkit. (2018). Retrieved from: https://ainowinstitute.org/aap-toolkit.html. Aitken, M., Toreini, E., Charmichael, P., Coopamootoo, K., Elliott, K., & van Moorsel, A. (2020, January). Establishing a social licence for Financial Technology: Reflections on the role of the private sector in pursuing ethical data practices. Big Data & Society. doi:10.1177/2053951720908892 Ajunwa, I. (2016). Hiring by Algorithm. SSRN Electronic Journal. doi:10.2139/ssrn.2746078 Ajunwa, I. (2020, Forthcoming). The Paradox of Automation as Anti-Bias Intervention, 41 Cardozo, L. Rev. Amini, A., Soleimany, A. P., Schwarting, W., Bhatia, S. N., & Rus, D. (2019, January). Uncovering and mitigating algorithmic bias through learned latent structure. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 289-295. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D.
    [Show full text]