Coded Bias: Race, Technology, And
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Facial Recognition: the Discrepancies with People of Color
Journal of Business Cases and Applications Volume 30 Facial Recognition: The Discrepancies with People of Color Damon E. Martin, J.D. National University ABSTRACT Studies have shown the existing racial bias in facial recognition technology, the ethical manner in how creators and users of this software stem from their ability to deduce accuracy and societal influence. The discriminatory practices implemented in procuring photo and video data for facial recognition technology, however, by default, is a failing. Living in a multicultural society without the proper lens to view its citizens only creates more contention. Understanding the making of the algorithm and its implications shed light on the biases that exist. When inclusivity is not considered, and the collection of data is disproportionate, there will be negligence in the improper use of technology, thus failing to protect citizens of color. This piece explains how human influence produces bias through the dissection of facial recognition software and, therefore, results in the domino effects it has on social accountability. Keywords: algorithmic bias, discriminatory practice, social accountability, facial recognition technology Copyright statement: Authors retain the copyright to the manuscripts published in AABRI journals. Please see the AABRI Copyright Policy at http://www.aabri.com/copyright.html Facial recognition, Page 1 Journal of Business Cases and Applications Volume 30 Introduction When addressing the flaws in the relatively new technology that is facial recognition software, the discrepancies between technical error and human error go hand-in-hand. Improper use of technology both in its manufacturing and its mishandling is coming into question. Algorithmic bias and discriminatory practices are being held under scrutiny for its misuse and ethical competency. -
AI Principles 2020 Progress Update
AI Principles 2020 Progress update AI Principles 2020 Progress update Table of contents Overview ........................................................................................................................................... 2 Culture, education, and participation ....................................................................4 Technical progress ................................................................................................................... 5 Internal processes ..................................................................................................................... 8 Community outreach and exchange .....................................................................12 Conclusion .....................................................................................................................................17 Appendix: Research publications and tools ....................................................18 Endnotes .........................................................................................................................................20 1 AI Principles 2020 Progress update Overview Google’s AI Principles were published in June 2018 as a charter to guide how we develop AI responsibly and the types of applications we will pursue. This report highlights recent progress in AI Principles implementation across Google, including technical tools, educational programs and governance processes. Of particular note in 2020, the AI Principles have supported our ongoing work to address -
United States House Committee on Science, Space and Technology
United States House Committee on Science, Space and Technology June 26, 2019 Hearing on Artificial Intelligence: Societal and Ethical Implications Written Testimony of Joy Buolamwini Founder, Algorithmic Justice League Masters in Media Arts and Sciences, 2017, Massachusetts Institute of Technology MSc Education (Learning & Technology), 2014, Distinction, University of Oxford BS Computer Science, 2012, Highest Honors, Georgia Institute of Technology PhD Pending, MIT Media Lab Made Possible By Critical Input from Dr. Sasha Costanza-Chock Injoluwa Deborah Raji For additional information, please contact Joy Buolamwini at [email protected] Dear Chairwoman Johnson, Ranking Member Lucas, and Members of the Committee, Thank you for the opportunity to testify on the societal and ethical implications of artificial intelligence (AI). My name is Joy Buolamwini, and I am the founder of the Algorithmic Justice League (AJL), based in Cambridge, Massachusetts. I established AJL to create a world with more ethical and inclusive technology after experiencing facial analysis software failing to detect my dark-skinned face until I put on a white mask. I’ve shared this experience of algorithmic bias in op-eds for Time Magazine and the New York Times as well as a TED featured talk with over 1 million views.1 My MIT thesis and subsequent research studies uncovered substantial skin type and gender bias in AI services from companies like Microsoft, IBM, and Amazon.2 This research has been covered in over 40 countries and has been featured in the mainstream media including FOX News, MSNBC, CNN, PBS, Bloomberg, Fortune, BBC, and even the Daily Show with Trevor Noah.3 Figure 1. -
A Feminist Critique of Algorithmic Fairness
Redistribution and Rekognition: A Feminist Critique of Algorithmic Fairness Sarah Myers West AI Now Institute at New York University [email protected] Abstract Computer scientists, and artificial intelligence researchers in particular, have a predisposition for adopting precise, fixed definitions to serve as classifiers (Agre, 1997; Broussard, 2018). But classification is an enactment of power; it orders human interaction in ways that produce advantage and suffering (Bowker & Star, 1999). In so doing, it attempts to create order out of the messiness of human life, masking the work of the people involved in training machine learning systems, and hiding the uneven distribution of its impacts on communities (A. Taylor, 2018; Gray, 2019; Roberts, 2019). Feminist scholars, and particularly feminist scholars of color, have made powerful critiques of the ways in which artificial intelligence systems formalize, classify, and amplify historical forms of discrimination and act to reify and amplify existing forms of social inequality (Eubanks, 2017; Benjamin, 2019; Noble, 2018). In response, the machine learning community has begun to address claims of algorithmic bias under the rubric of fairness, accountability, and transparency. But it has dealt with these claims largely using computational approaches that obscure difference. Inequality is reflected and amplified in algorithmic systems in ways that exceed the capacity of statistical methods alone. This article examines how patterns of exclusion and erasure in algorithmic systems recapitulate and magnify a history of discrimination and erasure in the field of artificial intelligence, and in society more broadly. Shifting from individualized notions of fairness to more situated modeling of algorithmic remediation might create spaces of possibility for new forms of solidarity and West, Sarah Myers (2020). -
Pondering Responsible AI Podcast Series Transcript Season 1 Episode 6
KIMBERLY Welcome to Pondering AI. My name is Kimberly Nevala. I'm a Strategic Advisor at SAS and your host to this NEVALA: season as we contemplate the imperative for responsible AI. Each episode, we're joined by an expert to explore a different facet of the ongoing quest to ensure artificial intelligence is deployed fairly, safely, and justly for all. Today, we are joined by Shalini Kantayya. Shalini is a storyteller, a passionate social activist, and a filmmaker who documents some of the most challenging topics of our time. I've also been blown away by her amazing ability to tackle sensitive topics with serious humor. Shalini, your short, Doctor In Law, made me laugh out loud. More recently, Shalini directed the film, Coded Bias, which debunks the myth that AI algorithms are objective by nature. Welcome, Shalini. SHALINI Thanks so much for having me. It's an honor to be here. KANTAYYA: KIMBERLY So have you always been a storyteller? NEVALA: SHALINI I have. [LAUGHTER] I think all of us are storytellers. And we all have stories. KANTAYYA: I think there's a saying that says, the universe is not made of molecules. It's made of stories. I'm a scientist, so I believe both are true. But I think that stories are something that we've had and passed on since we had fire. It's one of our oldest human traditions. And it's how we engage with issues that are difficult. And I think it enables us to cross boundaries and to translate our values. -
Bias in Artificial Intelligence
Bias in Artificial Intelligence …inequity and bias are not to be found in a single place, like a bug that can be located and fixed. These issues are systemic. 1(p9) Recent news stories deliver apparently contradictory messages about artificial intelligence (AI): future prospects leading to lucrative business deals on the one hand and disappointing performance prompting protests and lawsuits on the other.2-4 Expectations remain high that AI will continue to transform many aspects of daily life, including health care — thus the business deals, such as Microsoft’s commitment to acquire Nuance Communications for $19.7 billion.5 At the same time, AI algorithms have produced results biased against women and people of color, prompting disillusionment and reassessment. Racism and Gender Inequities Troubling examples demonstrate how AI can reflect and even exacerbate existing racism and gender inequities. In 2015, Black software developer Jacky Alciné was shocked to find that Google Photos had automatically sorted selfies he’d taken with a friend, who is also Black, into a default folder labeled “gorillas.”6,7 And in 2017, Joy Buolamwini, a Ghanaian-American computer scientist at the MIT Media Lab could only get a facial recognition program to see her by putting on a white mask.8,9 Other examples abound. The source of algorithmic bias is more implicit than explicit. It is inherent in the environment within which the technology was developed. Both of the above examples involve facial recognition, but the underlying problem of faulty, biased and offensive results occurs across many algorithms and applications of AI. Machine learning, the process through which algorithms learn to identify patterns in data, relies on vast digital archives of tagged images, records, and other data for continual training and refinement. -
Wanda Muñoz Autonomous Weapons
Autonomous weapons systems: an analysis from human rights, humanitarian and ethical artificial intelligence perspectives This paper is based on Wanda Muñoz’ intervention at the briefing for diplomats on “The Normative and Operational Framework on Autonomous Weapon Systems” held on 28/05/2021. Today I will share with you two angles of analysis on the issue of autonomous weapons. Firstly, I will share some perspectives of human rights activists and humanitarian workers. Secondly, I will talk about how the discussion on autonomous weapons could be informed by international discussions on ethical approaches to artificial intelligence (AI). A/ Humanitarian and Human Rights Concerns The topic of autonomous weapons should not be seen only as a military and diplomatic issue, because it is much more than that; it is also a social and humanitarian issue. Unfortunately, what we see in the world today is that thousands of civilians have to deal with the consequences of violence and weapons daily, and that humanitarian organizations are at the forefront of the response. From this perspective, I would like to highlight four key reasons why autonomous weapon should be banned. 1. Human dignity. We believe that no one ever should face any risk of harm or death by autonomous weapons, because this would go against human dignity. This has already been said but we will keep insisting on it until autonomous weapons that target humans are banned. What message do you think we would send to those populations where autonomous weapons would be deployed? In my view, we would be telling them: “To us, your value as a human being is so little, that we don’t care about the risks, and even a machine can do the job of killing you”. -
Mx. Joy Buolamwini, Founder, Algorithmic Justice League
United States House Committee on Science, Space and Technology June 26, 2019 Hearing on Artificial Intelligence: Societal and Ethical Implications Written Testimony of Joy Buolamwini Founder, Algorithmic Justice League Masters in Media Arts and Sciences, 2017, Massachusetts Institute of Technology MSc Education (Learning & Technology), 2014, Distinction, University of Oxford BS Computer Science, 2012, Highest Honors, Georgia Institute of Technology PhD Pending, MIT Media Lab Made Possible By Critical Input from Dr. Sasha Costanza-Chock Injoluwa Deborah Raji For additional information, please contact Joy Buolamwini at [email protected] Dear Chairwoman Johnson, Ranking Member Lucas, and Members of the Committee, Thank you for the opportunity to testify on the societal and ethical implications of artificial intelligence (AI). My name is Joy Buolamwini, and I am the founder of the Algorithmic Justice League (AJL), based in Cambridge, Massachusetts. I established AJL to create a world with more ethical and inclusive technology after experiencing facial analysis software failing to detect my dark-skinned face until I put on a white mask. I’ve shared this experience of algorithmic bias in op-eds for Time Magazine and the New York Times as well as a TED featured talk with over 1 million views.1 My MIT thesis and subsequent research studies uncovered substantial skin type and gender bias in AI services from companies like Microsoft, IBM, and Amazon.2 This research has been covered in over 40 countries and has been featured in the mainstream media including FOX News, MSNBC, CNN, PBS, Bloomberg, Fortune, BBC, and even the Daily Show with Trevor Noah.3 Figure 1. -
Facial Recognition: the Discrepancies with People of Color
S21VC011 Facial Recognition: The Discrepancies with People of Color Damon E. Martin, J.D. National University ABSTRACT Studies have shown the existing racial bias in facial recognition technology, the ethical manner in how creators and users of this software stem from their ability to deduce accuracy and societal influence. The discriminatory practices implemented in procuring photo and video data for facial recognition technology, however, by default, is a failing. Living in a multicultural society without the proper lens to view its citizens only creates more contention. Understanding the making of the algorithm and its implications shed light on the biases that exist. When inclusivity is not considered, and the collection of data is disproportionate, there will be negligence in the improper use of technology, thus failing to protect citizens of color. This piece explains how human influence produces bias through the dissection of facial recognition software and, therefore, results in the domino effects it has on social accountability. Keywords: algorithmic bias, discriminatory practice, social accountability, facial recognition technology S21VC011 Introduction When addressing the flaws in the relatively new technology that is facial recognition software, the discrepancies between technical error and human error go hand-in-hand. Improper use of technology both in its manufacturing and its mishandling is coming into question. Algorithmic bias and discriminatory practices are being held under scrutiny for its misuse and ethical competency. In light of recent events such as the death of Breonna Taylor, Jacob Blake, and the Black Lives Matter protests, the proceedings in using facial recognition software can put more black lives at risk if these biases are not corrected. -
Tools to Create and Democratize Conversational Arti Cial Intelligence
Tools to Create and Democratize Conversational Articial Intelligence by Jessica Van Brummelen B.ASc., The University of British Columbia (2017) Submitted to the Department of Electrical Engineering and Computer Science in partial fulllment of the requirements for the degree of Master of Science in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2019 © Jessica Van Brummelen, MMXIX. All rights reserved. The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Author............................................................. Department of Electrical Engineering and Computer Science May 23, 2019 Certied by . Harold Abelson Class of 1922 Professor of Computer Science and Engineering Thesis Supervisor Accepted by . Leslie A. Kolodziejski Professor of Electrical Engineering and Computer Science Chair, Department Committee on Graduate Students Tools to Create and Democratize Conversational Articial Intelligence by Jessica Van Brummelen Submitted to the Department of Electrical Engineering and Computer Science on May 23, 2019, in partial fulllment of the requirements for the degree of Master of Science in Electrical Engineering and Computer Science Abstract The world is becoming increasingly saturated with voice-rst technology, such as Ama- zon Alexa and Google Home devices. As this technology becomes more complex, the skill set needed to develop conversational AI applications increases as well. This work bridges the gap, democratizes AI technology, and empowers technology consumers to become technology developers. In this thesis, I develop block-based Alexa Skill programming tools, enabling anyone — even elementary school students — to create complex conver- sational AI applications. -
Artificial Intelligence and Gender Equality And
United Nations Educational, Scientific and Cultural Organization ARTIFICIAL INTELLIGENCE and GENDER EQUALITY Key findings of UNESCO’s Global Dialogue ARTIFICIAL INTELLIGENCE and GENDER EQUALITY Key findings of UNESCO’s Global Dialogue Prepared in August 2020 by the United Nations Educational, Scientific and Cultural Organization, 7, place de Fontenoy, 75352 Paris 07 SP, France © UNESCO 2020 This report is available in Open Access under the Attribution-ShareAlike 3.0 IGO (CC-BY-SA 3.0 IGO) license (http://creativecommons.org/licenses/by-sa/3.0/igo/). The present license applies exclusively to the text content of this report and to images whose copyright belongs to UNESCO. By using the content of this report, the users accept to be bound by the terms of use of the UNESCO Open Access Repository (http://www.unesco.org/open-access/terms-use-ccbysa-en). The designations employed and the presentation of material throughout this report do not imply the expression of any opinion whatsoever on the part of UNESCO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The ideas and opinions expressed in this report are those of the authors; they are not necessarily those of UNESCO and do not commit the Organization. This Report was prepared by the Division for Gender Equality, UNESCO Graphic design: Anna Mortreux Printed by UNESCO The printer is certified Imprim’Vert®, the French printing industry’s environmental initiative CONTENTS Foreword ................................................................................................................... 2 Introduction ............................................................................................................... 4 FRAMING THE LANDSCAPE OF GENDER EQUALITY AND AI ................................... 6 AI and Social Good 6 The Imperatives of Gender Equality 7 GENDER EQUALITY AND AI PRINCIPLES ................................................................. -
Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers
Mitigating dataset harms requires stewardship: Lessons from 1000 papers Kenny Peng, Arunesh Mathur, Arvind Narayanan Princeton University Draft: August 9, 2021 Abstract Concerns about privacy, bias, and harmful applications have shone a light on the ethics of machine learning datasets, even leading to the retraction of prominent datasets including DukeMTMC, MS-Celeb-1M, TinyImages, and VGGFace2. In response, the machine learning community has called for higher ethical standards, transparency efforts, and technical fixes in the dataset creation process. The premise of our work is that these efforts can be more effective if informed by an understanding of how datasets are used in practice in the research community. We study three influential face and person recognition datasets—DukeMTMC, MS-Celeb-1M, and Labeled Faces in the Wild (LFW)—by analyzing nearly 1000 papers that cite them. We found that the creation of derivative datasets and models, broader technological and social change, the lack of clarity of licenses, and dataset management practices can introduce a wide range of ethical concerns. We conclude by suggesting a distributed approach that can mitigate these harms, making recommendations to dataset creators, conference program committees, dataset users, and the broader research community. 1 Introduction Datasets play an essential role in machine learning research, but are also the source of ethical concerns. These include concerns about the privacy of individuals included in datasets [36, 62], representational harms introduced by annotations [20, 35], effects of biases in datasets on downstream use [16, 17, 15], and the use of datasets for ethically dubious purposes [36, 67, 59]. These concerns have led to the retractions of several prominent research datasets including Tiny Images, VGGFace2, DukeMTMC, and MS-Celeb-1M.