Advancing AI Ethics Beyond Compliance
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
Artificial Intelligence
TechnoVision The Impact of AI 20 18 CONTENTS Foreword 3 Introduction 4 TechnoVision 2018 and Artificial Intelligence 5 Overview of TechnoVision 2018 14 Design for Digital 17 Invisible Infostructure 26 Applications Unleashed 33 Thriving on Data 40 Process on the Fly 47 You Experience 54 We Collaborate 61 Applying TechnoVision 68 Conclusion 77 2 TechnoVision 2018 The Impact of AI FOREWORD We introduce TechnoVision 2018, now in its eleventh year, with pride in reaching a second decade of providing a proven and relevant source of technology guidance and thought leadership to help enterprises navigate the compelling and yet complex opportunities for business. The longevity of this publication has been achieved through the insight of our colleagues, clients, and partners in applying TechnoVision as a technology guide and coupling that insight with the expert input of our authors and contributors who are highly engaged in monitoring and synthesizing the shift and emergence of technologies and the impact they have on enterprises globally. In this edition, we continue to build on the We believe that with TechnoVision 2018, you can framework that TechnoVision has established further crystalize your plans and bet on the right for several years with updates on last years’ technology disruptions, to continue to map and content, reflecting new insights, use cases, and traverse a successful digital journey. A journey technology solutions. which is not about a single destination, but rather a single mission to thrive in the digital epoch The featured main theme for TechnoVision 2018 through agile cycles of transformation delivering is AI, due to breakthroughs burgeoning the business outcomes. -
The Importance of Generation Order in Language Modeling
The Importance of Generation Order in Language Modeling Nicolas Ford∗ Daniel Duckworth Mohammad Norouzi George E. Dahl Google Brain fnicf,duckworthd,mnorouzi,[email protected] Abstract There has been interest in moving beyond the left-to-right generation order by developing alter- Neural language models are a critical compo- native multi-stage strategies such as syntax-aware nent of state-of-the-art systems for machine neural language models (Bowman et al., 2016) translation, summarization, audio transcrip- and latent variable models of text (Wood et al., tion, and other tasks. These language models are almost universally autoregressive in nature, 2011). Before embarking on a long-term research generating sentences one token at a time from program to find better generation strategies that left to right. This paper studies the influence of improve modern neural networks, one needs ev- token generation order on model quality via a idence that the generation strategy can make a novel two-pass language model that produces large difference. This paper presents one way of partially-filled sentence “templates” and then isolating the generation strategy from the general fills in missing tokens. We compare various neural network design problem. Our key techni- strategies for structuring these two passes and cal contribution involves developing a flexible and observe a surprisingly large variation in model quality. We find the most effective strategy tractable architecture that incorporates different generates function words in the first pass fol- generation orders, while enabling exact computa- lowed by content words in the second. We be- tion of the log-probabilities of a sentence. -
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design Jeffrey Dean Google Research [email protected] Abstract The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore’s Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today. Introduction The past decade has seen a remarkable series of advances in machine learning (ML), and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas [LeCun et al. 2015]. Major areas of significant advances include computer vision [Krizhevsky et al. 2012, Szegedy et al. 2015, He et al. 2016, Real et al. 2017, Tan and Le 2019], speech recognition [Hinton et al. -
Apple Joins Group Devoted to Keeping AI Nice 27 January 2017
Apple joins group devoted to keeping AI nice 27 January 2017 control and end up being detrimental to society. The companies "will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology," according to a statement. Internet giants have been investing heavily in creating software to help machines think more like people, ideally acting as virtual assistants who get to know users and perhaps even anticipate needs. Apple is the latest tech giant to join the non-profit "Partnership on AI," which will have its inaugural board SpaceX founder and Tesla chief executive Elon meeting in San Francisco on February 3 Musk in 2015 took part in creating nonprofit research company OpenAI devoted to developing artificial intelligence that will help people and not hurt them. A technology industry alliance devoted to making sure smart machines don't turn against humanity Musk found himself in the middle of a technology said Friday that Apple has signed on and will have world controversy by holding firm that AI could turn a seat on the board. on humanity and be its ruin instead of a salvation. Microsoft, Amazon, Google, Facebook, IBM, and A concern expressed by Musk was that highly Google-owned British AI firm DeepMind last year advanced artificial intelligence would be left to its established the non-profit organization, called own devices, or in the hands of a few people, to the "Partnership on AI," which will have its inaugural detriment of civilization as a whole. -
Ai & the Sustainable Development Goals
1 AI & THE SUSTAINABLE DEVELOPMENT GOALS: THE STATE OF PLAY Global Goals Technology Forum 2 3 INTRODUCTION In 2015, 193 countries agreed to the United Nations (UN) 2030 Agenda for Sustainable Development, which provides a shared blueprint for peace and prosperity for people and the planet, now and into the future. At its heart are the 17 Sustainable Development Goals (SDGs), which are an urgent call for action by all countries – developed and developing – in a global partnership. Achieving the SDGs is not just a moral imperative, but an economic one. While the world is making progress in some areas, we are falling behind in delivering the SDGs overall. We need all actors – businesses, governments, academia, multilateral institutions, NGOs, and others – to accelerate and scale their efforts to deliver the SDGs, using every tool at their disposal, including artificial intelligence (AI). In December 2017, 2030Vision 2030Vision published its first report, Uniting to “WE ARE AT A PIVOTAL Deliver Technology for the Global ABOUT 2030VISION Goals, which addressed the role MOMENT. AS ARTIFICIAL of digital technology – big data, AI & The Sustainable Development Goals: The State of Play State Goals: The Development Sustainable AI & The Founded in 2017, 2030Vision INTELLIGENCE BECOMES robotics, internet of things, AI, and is a partnership of businesses, MORE WIDELY ADOPTED, other technologies – in achieving NGOs, and academia that aims to the SDGs. transform the use of technology to WE HAVE A TREMENDOUS support the delivery of the SDGs. OPPORTUNITY TO In this paper, we focus on AI for 2030Vision serves as a platform REEVALUATE WHAT WE’VE the SDGs. -
Redesigning AI for Shared Prosperity: an Agenda Executive Summary
Redesigning AI for Shared Prosperity: an Agenda Executive Summary Artificial intelligence is expected to contribute trillions of dollars to the global GDP over the coming decade,1 but these gains may not occur equitably or be shared widely. Today, many communities around the world face persistent underemployment, driven in part by technological advances that have divided workers into cohorts of haves and have nots. If advances in workplace AI continue on their current trajectory, they could accelerate this economic exclusion. Alternatively, as this Agenda outlines, advances in AI could expand access to good jobs for all workers, regardless of their formal education, geographic location, race, ethnicity, gender, or ability. To put AI on this alternate path, Redesigning AI for Shared Prosperity: An Agenda proposes the creation of shared prosperity targets: verifiable criteria the AI industry must meet to support the future of workers. Crucial to the success of these targets will be thoughtful design and proper alignment with the needs of key stakeholders. In service of that, this Agenda compiles critical questions that must be answered for these targets to be implementable and effective. This living document is not a final set of questions, nor a comprehensive literature review of the issue areas covered. Instead, it is an open invitation to contribute answers as well as new questions that will help make AI advancement more likely to support inclusive economic progress and respect human rights. The primary way that people around the world support themselves is through waged work. Consequently, the creation of AI systems that lower labor demand will have a seismic effect, reducing employment and/ or wages while weakening the worker bargaining power that contributes to job quality and security. -
Overview of Artificial Intelligence
Updated October 24, 2017 Overview of Artificial Intelligence The use of artificial intelligence (AI) is growing across a Currently, AI technologies are highly tailored to particular wide range of sectors. Stakeholders and policymakers have applications or tasks, known as narrow (or weak) AI. In called for careful consideration of strategies to influence its contrast, general (or strong) AI refers to intelligent behavior growth, reap predicted benefits, and mitigate potential risks. across a range of cognitive tasks, a capability which is This document provides an overview of AI technologies unlikely to occur for decades, according to most analysts. and applications, recent private and public sector activities, Some researchers also use the term “augmented and selected issues of interest to Congress. intelligence” to capture AI’s various applications in physical and connected systems, such as robotics and the AI Technologies and Applications Internet of Things, and to focus on using AI technologies to Though definitions vary, AI can generally be thought of as enhance human activities rather than to replace them. computerized systems that work and react in ways commonly thought to require intelligence, such as solving In describing the course of AI development, the Defense complex problems in real-world situations. According to Advanced Research Projects Agency (DARPA) has the Association for the Advancement of Artificial identified three “waves.” The first wave focused on Intelligence (AAAI), researchers broadly seek to handcrafted knowledge that allowed for system-enabled understand “the mechanisms underlying thought and reasoning in limited situations, though lacking the ability to intelligent behavior and their embodiment in machines.” learn or address uncertainty, as with many rule-based systems from the 1980s. -
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 -
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims∗ Miles Brundage1†, Shahar Avin3,2†, Jasmine Wang4,29†‡, Haydn Belfield3,2†, Gretchen Krueger1†, Gillian Hadfield1,5,30, Heidy Khlaaf6, Jingying Yang7, Helen Toner8, Ruth Fong9, Tegan Maharaj4,28, Pang Wei Koh10, Sara Hooker11, Jade Leung12, Andrew Trask9, Emma Bluemke9, Jonathan Lebensold4,29, Cullen O’Keefe1, Mark Koren13, Théo Ryffel14, JB Rubinovitz15, Tamay Besiroglu16, Federica Carugati17, Jack Clark1, Peter Eckersley7, Sarah de Haas18, Maritza Johnson18, Ben Laurie18, Alex Ingerman18, Igor Krawczuk19, Amanda Askell1, Rosario Cammarota20, Andrew Lohn21, David Krueger4,27, Charlotte Stix22, Peter Henderson10, Logan Graham9, Carina Prunkl12, Bianca Martin1, Elizabeth Seger16, Noa Zilberman9, Seán Ó hÉigeartaigh2,3, Frens Kroeger23, Girish Sastry1, Rebecca Kagan8, Adrian Weller16,24, Brian Tse12,7, Elizabeth Barnes1, Allan Dafoe12,9, Paul Scharre25, Ariel Herbert-Voss1, Martijn Rasser25, Shagun Sodhani4,27, Carrick Flynn8, Thomas Krendl Gilbert26, Lisa Dyer7, Saif Khan8, Yoshua Bengio4,27, Markus Anderljung12 1OpenAI, 2Leverhulme Centre for the Future of Intelligence, 3Centre for the Study of Existential Risk, 4Mila, 5University of Toronto, 6Adelard, 7Partnership on AI, 8Center for Security and Emerging Technology, 9University of Oxford, 10Stanford University, 11Google Brain, 12Future of Humanity Institute, 13Stanford Centre for AI Safety, 14École Normale Supérieure (Paris), 15Remedy.AI, 16University of Cambridge, 17Center for Advanced Study in the Behavioral Sciences,18Google Research, 19École Polytechnique Fédérale de Lausanne, 20Intel, 21RAND Corporation, 22Eindhoven University of Technology, 23Coventry University, 24Alan Turing Institute, 25Center for a New American Security, 26University of California, Berkeley, 27University of Montreal, 28Montreal Polytechnic, 29McGill University, 30Schwartz Reisman Institute for Technology and Society arXiv:2004.07213v2 [cs.CY] 20 Apr 2020 April 2020 ∗Listed authors are those who contributed substantive ideas and/or work to this report. -
User Experience + Research Scientist Collaboration in a Generative Machine Learning Interface
CHI 2019 Case Study CHI 2019, May 4–9, 2019, Glasgow, Scotland, UK Identifying the Intersections: User Figure 1: Google AI. http://ai.google Experience + Research Scientist Collaboration in a Generative Machine Learning Interface Claire Kayacik, Sherol Chen, Signe Noerly, Jess Holbrook, Adam Roberts, Douglas Eck Google, Inc. Mountain View, CA , 94043 USA (cbalgemann,sherol,noerly,jessh,adarob,deck)@google.com ABSTRACT Creative generative machine learning interfaces are stronger when multiple actors bearing different points of view actively contribute to them. User experience (UX) research and design involvement in the creation of machine learning (ML) models help ML research scientists to more effectively identify human needs that ML models will fulfill. The People and AI Research (PAIR) group within Google developed a novel program method in which UXers are embedded into an ML research group for three Figure 2: Magenta is a project within Google Brain that focuses on training ma- months to provide a human-centered perspective on the creation of ML models. The first full-time chines for creative expressivity, genera- cohort of UXers were embedded in a team of ML research scientists focused on deep generative models tive personalization, and intuitive inter- to assist in music composition. Here, we discuss the structure and goals of the program, challenges we faces. https://g.co/magenta faced during execution, and insights gained as a result of the process. We offer practical suggestions Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. -
Artificial Intelligence the Next Digital Frontier?
ARTIFICIAL INTELLIGENCE THE NEXT DIGITAL FRONTIER? DISCUSSION PAPER JUNE 2017 Jacques Bughin | Brussels Eric Hazan | Paris Sree Ramaswamy | Washington, DC Michael Chui | San Francisco Tera Allas | London Peter Dahlström | London Nicolaus Henke | London Monica Trench | London Since its founding in 1990, the McKinsey Global Institute (MGI) has sought to develop a deeper understanding of the evolving global economy. As the business and economics research arm of McKinsey & Company, MGI aims to provide leaders in the commercial, public, and social sectors with the facts and insights on which to base management and policy decisions. The Lauder Institute at the University of Pennsylvania ranked MGI the world’s number-one private-sector think tank in its 2016 Global Think Tank Index for the second consecutive year. MGI research combines the disciplines of economics and management, employing the analytical tools of economics with the insights of business leaders. Our “micro-to-macro” methodology examines microeconomic industry trends to better understand the broad macroeconomic forces affecting business strategy and public policy. MGI’s in-depth reports have covered more than 20 countries and 30 industries. Current research focuses on six themes: productivity and growth, natural resources, labor markets, the evolution of global financial markets, the economic impact of technology and innovation, and urbanization. Recent reports have assessed the economic benefits of tackling gender inequality, a new era of global competition, Chinese innovation, and digital globalization. MGI is led by four McKinsey and Company senior partners: Jacques Bughin, James Manyika, Jonathan Woetzel, and Frank Mattern, MGI’s chairman. Michael Chui, Susan Lund, Anu Madgavkar, Sree Ramaswamy, and Jaana Remes serve as MGI partners.