Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned
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When Diving Deep Into Operationalizing the Ethical Development of Artificial Intelligence, One Immediately Runs Into the “Fractal Problem”
When diving deep into operationalizing the ethical development of artificial intelligence, one immediately runs into the “fractal problem”. This may also be called the “infinite onion” problem.1 That is, each problem within development that you pinpoint expands into a vast universe of new complex problems. It can be hard to make any measurable progress as you run in circles among different competing issues, but one of the paths forward is to pause at a specific point and detail what you see there. Here I list some of the complex points that I see at play in the firing of Dr. Timnit Gebru, and why it will remain forever after a really, really, really terrible decision. The Punchline. The firing of Dr. Timnit Gebru is not okay, and the way it was done is not okay. It appears to stem from the same lack of foresight that is at the core of modern technology,2 and so itself serves as an example of the problem. The firing seems to have been fueled by the same underpinnings of racism and sexism that our AI systems, when in the wrong hands, tend to soak up. How Dr. Gebru was fired is not okay, what was said about it is not okay, and the environment leading up to it was -- and is -- not okay. Every moment where Jeff Dean and Megan Kacholia do not take responsibility for their actions is another moment where the company as a whole stands by silently as if to intentionally send the horrifying message that Dr. Gebru deserves to be treated this way. -
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 -
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification∗
Proceedings of Machine Learning Research 81:1{15, 2018 Conference on Fairness, Accountability, and Transparency Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification∗ Joy Buolamwini [email protected] MIT Media Lab 75 Amherst St. Cambridge, MA 02139 Timnit Gebru [email protected] Microsoft Research 641 Avenue of the Americas, New York, NY 10011 Editors: Sorelle A. Friedler and Christo Wilson Abstract who is hired, fired, granted a loan, or how long Recent studies demonstrate that machine an individual spends in prison, decisions that learning algorithms can discriminate based have traditionally been performed by humans are on classes like race and gender. In this rapidly made by algorithms (O'Neil, 2017; Citron work, we present an approach to evaluate and Pasquale, 2014). Even AI-based technologies bias present in automated facial analysis al- that are not specifically trained to perform high- gorithms and datasets with respect to phe- stakes tasks (such as determining how long some- notypic subgroups. Using the dermatolo- one spends in prison) can be used in a pipeline gist approved Fitzpatrick Skin Type clas- that performs such tasks. For example, while sification system, we characterize the gen- face recognition software by itself should not be der and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. trained to determine the fate of an individual in We find that these datasets are overwhelm- the criminal justice system, it is very likely that ingly composed of lighter-skinned subjects such software is used to identify suspects. Thus, (79:6% for IJB-A and 86:2% for Adience) an error in the output of a face recognition algo- and introduce a new facial analysis dataset rithm used as input for other tasks can have se- which is balanced by gender and skin type. -
Zencam: Context-Driven Control of Continuous Vision Body Cameras
UI*OUFSOBUJPOBM$POGFSFODFPO%JTUSJCVUFE$PNQVUJOHJO4FOTPS4ZTUFNT %$044 ZenCam: Context-Driven Control of Continuous Vision Body Cameras Shiwei Fang Ketan Mayer-Patel Shahriar Nirjon UNC Chapel Hill UNC Chapel Hill UNC Chapel Hill [email protected] [email protected] [email protected] Abstract—In this paper, we present — ZenCam, which is an this design suits the purpose in an ideal scenario, in many always-on body camera that exploits readily available information situations, the wearer (e.g., a law-enforcement officer) may in the encoded video stream from the on-chip firmware to not be able to predict the right moment to turn the camera on classify the dynamics of the scene. This scene-context is further combined with simple inertial measurement unit (IMU)-based or may completely forget to do so in the midst of an action. activity level-context of the wearer to optimally control the camera There are some cameras which automatically turns on at an configuration at run-time to keep the device under the desired event (e.g., when a gun is pulled), but they miss the “back- energy budget. We describe the design and implementation of story,” i.e., how the situation had developed. ZenCam and thoroughly evaluate its performance in real-world We advocate that body cameras should be always on so that scenarios. Our evaluation shows a 29.8-35% reduction in energy consumption and 48.1-49.5% reduction in storage usage when they are able to continuously capture the scene for an extended compared to a standard baseline setting of 1920x1080 at 30fps period. -
Devices, the Weak Link in Achieving an Open Internet
Smartphones, tablets, voice assistants... DEVICES, THE WEAK LINK IN ACHIEVING AN OPEN INTERNET Report on their limitations and proposals for corrective measures French République February 2018 Devices, the weak link in achieving an open internet Content 1 Introduction ..................................................................................................................................... 5 2 End-user devices’ possible or probable evolution .......................................................................... 7 2.1 Different development models for the main internet access devices .................................... 7 2.1.1 Increasingly mobile internet access in France, and in Europe, controlled by two main players 7 2.1.2 In China, mobile internet access from the onset, with a larger selection of smartphones .................................................................................................................................. 12 2.2 Features that could prove decisive in users’ choice of an internet access device ................ 14 2.2.1 Artificial intelligence, an additional level of intelligence in devices .............................. 14 2.2.2 Voice assistance, a feature designed to simplify commands ........................................ 15 2.2.3 Mobile payment: an indispensable feature for smartphones? ..................................... 15 2.2.4 Virtual reality and augmented reality, mere goodies or future must-haves for devices? 17 2.2.5 Advent of thin client devices: giving the cloud a bigger role? -
Electronic 3D Models Catalogue (On July 26, 2019)
Electronic 3D models Catalogue (on July 26, 2019) Acer 001 Acer Iconia Tab A510 002 Acer Liquid Z5 003 Acer Liquid S2 Red 004 Acer Liquid S2 Black 005 Acer Iconia Tab A3 White 006 Acer Iconia Tab A1-810 White 007 Acer Iconia W4 008 Acer Liquid E3 Black 009 Acer Liquid E3 Silver 010 Acer Iconia B1-720 Iron Gray 011 Acer Iconia B1-720 Red 012 Acer Iconia B1-720 White 013 Acer Liquid Z3 Rock Black 014 Acer Liquid Z3 Classic White 015 Acer Iconia One 7 B1-730 Black 016 Acer Iconia One 7 B1-730 Red 017 Acer Iconia One 7 B1-730 Yellow 018 Acer Iconia One 7 B1-730 Green 019 Acer Iconia One 7 B1-730 Pink 020 Acer Iconia One 7 B1-730 Orange 021 Acer Iconia One 7 B1-730 Purple 022 Acer Iconia One 7 B1-730 White 023 Acer Iconia One 7 B1-730 Blue 024 Acer Iconia One 7 B1-730 Cyan 025 Acer Aspire Switch 10 026 Acer Iconia Tab A1-810 Red 027 Acer Iconia Tab A1-810 Black 028 Acer Iconia A1-830 White 029 Acer Liquid Z4 White 030 Acer Liquid Z4 Black 031 Acer Liquid Z200 Essential White 032 Acer Liquid Z200 Titanium Black 033 Acer Liquid Z200 Fragrant Pink 034 Acer Liquid Z200 Sky Blue 035 Acer Liquid Z200 Sunshine Yellow 036 Acer Liquid Jade Black 037 Acer Liquid Jade Green 038 Acer Liquid Jade White 039 Acer Liquid Z500 Sandy Silver 040 Acer Liquid Z500 Aquamarine Green 041 Acer Liquid Z500 Titanium Black 042 Acer Iconia Tab 7 (A1-713) 043 Acer Iconia Tab 7 (A1-713HD) 044 Acer Liquid E700 Burgundy Red 045 Acer Liquid E700 Titan Black 046 Acer Iconia Tab 8 047 Acer Liquid X1 Graphite Black 048 Acer Liquid X1 Wine Red 049 Acer Iconia Tab 8 W 050 Acer -
Prof. Feng Liu
Prof. Feng Liu Spring 2021 http://www.cs.pdx.edu/~fliu/courses/cs510/ 03/30/2021 Today Course overview ◼ Admin. Info ◼ Computational Photography 2 People Lecturer: Prof. Feng Liu ◼ Room: email for a Zoom appointment ◼ Office Hours: TR 3:30-4:30pm ◼ [email protected] Grader: Zhan Li ◼ [email protected] 3 Web and Computer Account Course website ◼ http://www.cs.pdx.edu/~fliu/courses/cs510/ ◼ Class mailing list [email protected] [email protected] Everyone needs a Computer Science department computer account ◼ Get account at CAT ◼ http://cat.pdx.edu 4 Recommended Textbooks & Readings Computer Vision: Algorithms and Applications ◼ By R. Szeliski ◼ Available online, free Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library ◼ By Adrian Kaehler and Gary Bradski Or its early version Learning OpenCV: Computer Vision with the OpenCV Library ◼ By Gary Bradski and Adrian Kaehler Papers recommended by the lecturers 5 Grading 30%: Readings 20%: In-class paper presentation 50%: Project ◼ 10%: final project presentation ◼ 40%: project quality 6 Readings About 2 papers every week ◼ Write a brief summary for one of the papers Totally less than 500 words 1. What problem is addressed? 2. How is it solved? 3. The advantages of the presented method? 4. The limitations of the presented method? 5. How to improve this method? Submit to [email protected] by 4:00 pm every Thursday ◼ Write in the plain text format in your email directly ◼ No attached document 7 Paper Presentation One paper one student ◼ -
Manovich.Ai Aestheti
AI Aesthetics Lev Manovich Publication data: Strelka Press, December 21, 2018. ASIN: B07M8ZJMYG Abstract: AI plays a crucial role in the global cultural ecosystem. It recommends what we should see, listen to, read, and buy. It determines how many people will see our shared content. It helps us make aesthetic decisions when we create media. In professional cultural production, AI has already been adapted to produce movie trailers, music albums, fashion items, product and web designs, architecture, etc. In this short book, Lev Manovich offers a systematic framework to help us think about cultural uses of AI today and in the future. He challenges existing ideas and gives us new concepts for understanding media, design, and aesthetics in the AI era. “[The Analytical Engine] might act upon other things besides number...supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent.” - Ada Lovelace, 1842 Итак, кто же я такой? С известными оговорками, я и есть то, что люди прошлого называли «искусственным интеллектом». (Виктор Пелевин, iPhuck 10, 2017.) “So who am I exactly? With known caveats, I am what people of the past have called "artificial intelligence."” (Victor Pelevin, iPhuck 10, 2017.) Writing in 1842, Ada Lovelace imagines that in the future, Babbage’s Analytical Engine ( general purpose programmable computer) will be able to create complex music. In the 2017 novel by famous Russian writer Victor Pelevin, set in the late 21th century, the narrator is an algorithm solving crimes and writing novels about them. -
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 -
Facial Recognition Is Improving — but the Bigger That Had Been Scanned
Feature KELVIN CHAN/AP/SHUTTERSTOCK KELVIN The Metropolitan Police in London used facial-recognition cameras to scan for wanted people in February. Fussey and Murray listed a number of ethical and privacy concerns with the dragnet, and questioned whether it was legal at all. And they queried the accuracy of the system, which is sold by Tokyo-based technology giant NEC. The software flagged 42 people over 6 trials BEATING that the researchers analysed; officers dis- missed 16 matches as ‘non-credible’ but rushed out to stop the others. They lost 4 people in the crowd, but still stopped 22: only 8 turned out to be correct matches. The police saw the issue differently. They BIOMETRIC BIAS said the system’s number of false positives was tiny, considering the many thousands of faces Facial recognition is improving — but the bigger that had been scanned. (They didn’t reply to issue is how it’s used. By Davide Castelvecchi Nature’s requests for comment for this article.) The accuracy of facial recognition has improved drastically since ‘deep learning’ tech- hen London’s Metropolitan extracted key features and compared them niques were introduced into the field about a Police tested real-time to those of suspects from a watch list. “If there decade ago. But whether that means it’s good facial-recognition technology is a match, it pulls an image from the live feed, enough to be used on lower-quality, ‘in the wild’ between 2016 and 2019, they together with the image from the watch list.” images is a hugely controversial issue. -
Distributed Blackness Published Just Last Year (2020, New York University Press), Dr
Transcript The Critical Technology Episode 4: Distributed Blackness Published just last year (2020, New York University Press), Dr. Andre Brock Jr’s Distributed Blackness: African American Cybercultures has already become a highly influential, widely cited, must read text for anyone interested in digital culture and technology. Through a theoretically rich and methodologically innovative analysis, Brock positions Blackness at the centre of Internet technoculture, its technological designs and infrastructures, its political economic underpinnings, its cultural significance and its emergent informational identities. Sara Grimes 0:00 Like many of you, as the fall 2020 semester was wrapping up, my Twitter feed was suddenly flooded with the news that Dr. Timnit Gebru had been fired from Google. A world renowned computer scientist, leading AI ethics researcher, and co-founder of Black in AI. Dr. Gebru had recently completed research raising numerous ethical concerns with the type of large scale AI language models used in many Google products. Her expulsion from Google highlights the discrimination and precarity faced by black people, especially black women, working in the tech industries and in society at large. On the one hand, Dr. Gebru’s treatment by Google execs made visible through screenshots and conversations posted on Twitter was met with an outpouring of public support from fellow Google employees, tech workers, academics and concerned citizens. The #ISupportTimnit was often paired with #BelieveBlackWomen, connecting this event with the systematic anti-black racism and sexism found across technoculture. On the other hand, since December, Dr. Gebru and her supporters have also been targeted by Twitter trolls, and subjected to a bombardment of racist and sexist vitriol online. -
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing Inioluwa Deborah Raji Timnit Gebru Margaret Mitchell University of Toronto Google Research Google Research Canada United States United States [email protected] [email protected] [email protected] Joy Buolamwini Joonseok Lee Emily Denton MIT Media Lab. Google Research Google Research United States United States United States [email protected] [email protected] [email protected] ABSTRACT the positive uses of this technology includes claims of “understand- Although essential to revealing biased performance, well inten- ing users”, “monitoring or detecting human activity”, “indexing and tioned attempts at algorithmic auditing can have effects that may searching digital image libraries”, and verifying and identifying harm the very populations these measures are meant to protect. subjects “in security scenarios” [1, 8, 16, 33]. This concern is even more salient while auditing biometric sys- The reality of FPT deployments, however, reveals that many tems such as facial recognition, where the data is sensitive and of these uses are quite vulnerable to abuse, especially when used the technology is often used in ethically questionable manners. for surveillance and coupled with predatory data collection prac- We demonstrate a set of five ethical concerns in the particular case tices that, intentionally or unintentionally, discriminate against of auditing commercial facial processing technology, highlighting marginalized groups [2]. The stakes are particularly high when additional design considerations and ethical tensions the auditor we consider companies like Amazon and HireVue, who are selling needs to be aware of so as not exacerbate or complement the harms their services to police departments or using the technology to help propagated by the audited system.