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S U M M I T
AI for Social Good - Fairness, Ethics, Accountability, and Transparency
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S U M M I T
Opinions expressed are solely our own and do
not express the views or opinions of Amazon or AWS
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S U M M I T
Machine Learning
1980s
A technique that allows computer to perform task without being
explicitly programmed
AI, Machine Learning, and Deep Learning
Artificial Intelligence
1950s
Any techniques that allows computer to
mimic human intelligence
Turing Test Perceptron
Deep Learning
2010s
A subfield of machine learning that
uses neural network to learning
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S U M M I T
3 Types of Machine Learning
Supervised Machine Learning
Task driven
• Training Data: (X,Y) (Features, Labels)
• Predict: Y, minimizing some loss
• Classification, Regression
Unsupervised Machine Learning
Data driven
• Training Data: X (features only)
• Find similar points in high-dim X-space
• Clustering
Reinforcement Learning
Decision making
• Training data: (State, Action, Reward)
• Maximize long term rewards
• Robotics, games
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S U M M I T
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R
S U M M I T
AI for Social Good (Collaboration: AWS, NSF and University of Nevada)
https://www.unr.edu/nevada-today/news/2019/big-data-wildfires
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S U M M I T
Source: Weather Channel
Machine Learning for Improving Disaster Management and Response
Session ID: 301069 - Artificial Intelligence and Machine Learning in Research
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S U M M I T
AI for Social Good
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S U M M I T
Evolving AI Capabilities
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S U M M I T
Survey of Judges: Case for Human Decision making
William Austin and Thomas A. Williams III, ‘A survey of judges’ responses
to simulated legal cases: research note on sentencing disparity’,
Journal of Criminal Law and Criminology , vol. 68, no. 2, 1977, pp. 306–310.
Algorithmic Learning:
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S U M M I T
Applicability in various areas:
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S U M M I T
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S U M M I T
Fairness
• Protected Attributes : like race, gender, age, religion and their proxies
• Classification parity, meaning that common measures of
predictive performance (e.g., false positive and false negative rates)
• Outcomes are independent of protected attributes.
Transparency
Accountability
Explainability
Example: DARPA’s program on explainable AI.
https://www.darpa.mil/program/explainable-artificial-intelligence
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S U M M I T
Prem Natarajan is a Vice President in Alexa and leads a multidisciplinary
science, engineering, and product organization which improves
customer experience worldwide through advances in natural language
understanding, entity linking and resolution, and related machine
learning technologies. Before joining Amazon, he was senior vice dean
of engineering at the University of Southern California where he led
nationally influential DARPA and IARPA sponsored research efforts in
biometrics/face recognition, OCR, natural language processing,
media forensics, and forecasting. Prior to that, he served as executive
vice president and principal scientist for speech, language, and
multimedia at Raytheon BBN Technologies.
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R
S U M M I T
AI for Social Good
AMAZON.COM INC. 2019. ALL RIGHTS RESERVED.
AI for Social Good
 Social “good” comes in many forms
 Better education
 Faster, cheaper drug discovery
 More effective policy making
 Predicting and responding to natural disasters, epidemics
 And much more ….
 Commonly accepted attributes of “AI for Social Good”
 Collaboration of multiple disciplines, especially social sciences and AI
 Public-private partnership + nonprofits-academia-industry collaboration
 Open access to technological resources
 Considerations of bias, fairness, and accountability of ML algorithms
AMAZON.COM INC. 2019. ALL RIGHTS RESERVED.
Current State
 Growing investments across academia, industry, and government
 Several academia-based centers of “AI for Social Good” or “AI in Society” have emerged in
recent years with diverse themes ranging from algorithms to policy
 Industry initiatives span in-house efforts and extramural community creation efforts such as
the NSF-Amazon Fairness Program for funding fairness research in academia
 Substantial Government investments – e.g. DARPA LORELEI, Memex, World Modelers, XAI and
many other programs
 Emergence of conferences and workshops
 FATML – Fairness, Accountability and Transparency in ML
 AI for Social Good workshop at NeurIPS
AMAZON.COM INC. 2019. ALL RIGHTS RESERVED.
Current State – AI Stack View
Apps
Toolkits
ML Dev
Environments
Algorithms
Compute / Storage
Mostly Open source (e.g. MXNet) but includes
dev environments like Alexa Skills Kit
Requires targeted funding for fairness,
transparency, etc.
Requires Funding
Cost-effective models of access
AMAZON.COM INC. 2019. ALL RIGHTS RESERVED.
Alexa Skills for Social Good
 Organized contest in 2018 to encourage creation of Alexa skills for social good
 Red Cross skills: hurricane alerts, scheduling blood donations, and first aid
 Environmental consciousness skills: recycle Game, EVIE assistant, compost tracking, bike sharing
 Access skills: My Talking Newspaper, Safe and Well (check on status of relatives)
 Language Preservation (with the Alexa Cleo Skill)
 Cleo skill harnesses the expertise of multilingual Alexa users to teach Alexa new languages or
dialects. Through a crowdsourcing model, users can help expand Alexa to new locales and
languages, bringing the technology to more people around the world.
 Users have taught Alexa languages such as Hindi, Korean, Russian, Klingon and many more.
 We are conducting an internal pilot to evaluate programs to support language preservation
with Indigenous languages such as Lakota and Ojibwe.
AMAZON.COM INC. 2019. ALL RIGHTS RESERVED.
Doing Well by Doing Good*
“How People with Disabilities Are Using AI to Improve Their Lives”
“It was the first time since he was a toddler playing with a rattler that he was able to interact with
something all by himself,” James says. “This Echo device goes way beyond ordering groceries or looking
up a recipe for us."
--- NPR Nova 30 January 2019
“How the Alexa Robot brought internet-based learning to a remote village school in Maharashtra”
“….. people on ground zero have emerged as change-makers themselves with a little help from Amazon
devices. Here’s one such story that is nothing but a triumph of human imagination.”
“In the hot, dry, and dusty village of Warud in Maharashtra’s Amravati district, a 31-year-old
schoolteacher is using Alexa to impart lessons to kids of farmers and labourers employed in the vicinity.”
--- Yourstory.com and The Hindu newspaper, 4 Feb 2019
*Prof. Andrew Lo at re:MARS 2019
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S U M M I T
Jim Kurose
Assistant Director, CISE, National Science Foundation
Dr. Jim Kurose is an Assistant Director of the National Science Foundation, where he
leads the Directorate for Computer and Information Science and Engineering
(CISE). With an annual budget of nearly $1B, CISE’s mission is to uphold the
nation's leadership in scientific discovery and engineering innovation through its
support of fundamental research in computer and information science and
engineering, transformative advances in cyberinfrastructure, and preparation of a
diverse computing-capable workforce. Jim also co-chairs the Networking and
Information Technology Research and Development (NITRD) Program, the
Subcommittee on Machine Learning and AI, and the Subcommittee on Open Science
of the National Science and Technology Council (NSTC), facilitating the coordination
of these research and development efforts across Federal agencies. Recently, Jim
also served as the Assistant Director for Artificial Intelligence in the US Office of
Science and Technology Policy (OSTP). Jim is on leave from the University of
Massachusetts, Amherst, where he is Distinguished University Professor of Computer
Science
Information &
Intelligent Systems
Computing &
Communication Foundations
Computer & Network
Systems
Advanced
Cyberinfrastructure
Panel: AI for Social Good - Fairness, Ethics, Accountability, and Transparency
AWS Public Sector Summit
Jim Kurose
Assistant Director, NSF
Computer & Information Science & Engineering
Federal AI R&D Activities: a view from NSF
AI: ongoing US government activities
AI Executive Order
(Feb 2019)
HSST AI Roundtable (May 2019)
Congress
Senate, House
legislative
activities
AI Convening @ NSF (May 2019)
 Envisioning National AI R&D Institutes
 Policy and principles
 Objectives
 Roles and responsibilities
 Federal Investment in AI R&D
 Data, Computing for AI R&D
 Guidance for Regulation of AI
Applications
 AI and the American workforce
 Action Plan for Protection of the United
States Advantage in AI
AI principles
Principles for responsible
stewardship of trustworthy AI
 Inclusive growth, sustainable
development and well-being
 Human-centred values and
fairness
 Transparency and explainability
 Robustness, security and safety
 Accountability
National policies and
international co-operation for
trustworthy
 Investing in AI R&D
 Fostering a digital ecosystem for AI
 Building human capacity,
preparing for labour market
transformation
 International cooperation for
trustworthy AI
OECD Principles on AI, May 22, 2019
Fairness in the AI System Lifecycle
Artificial Intelligence in Society, June 12, 2019
NSF Leadership in AI
NSF invested nearly $450M
in AI research (core,
applications, systems,
infrastructure) in FY18
$
Thought Leadership Across USG
Innovative Programmatics
NSTC Select Committee on AI
NSTC Subcommittee on ML & AI
NSTC AI Interagency Working Group (under
NITRD): 2016, 2019 AI R&D Strategic Plans
OSTP Assistant Director(s) for AI
International: OECD, G7
Research Funding
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S U M M I T
Patricia Flatley Brennan, RN, PhD
Director, National Library of Medicine
National Institutes of Health
Prem Natarajan is a Vice President in Alexa and leads a multidisciplinary
science, engineering, and product organization which improves
customer experience worldwide through advances in natural language
understanding, entity linking and resolution, and related machine
learning technologies. Before joining Amazon, he was senior vice dean
of engineering at the University of Southern California where he led
nationally influential DARPA and IARPA sponsored research efforts in
biometrics/face recognition, OCR, natural language processing,
media forensics, and forecasting. Prior to that, he served as executive
vice president and principal scientist for speech, language, and
multimedia at Raytheon BBN Technologies.
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R
S U M M I T
The National Institutes of Health
NEI
NCI
NHLBI
NLM
NINDS
NIMH
NIAMS
NINR
NCCIH
NHGRI
NIA
NIAAA
NIAID
NICHD
NIDCD
NIDCR
NIDDK
NIDA
NIEHS
OD
NIBIB
NIMHD
NCATS
CIT
CC
CSR
FICNIGMS
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S U M M I T
BIOMEDICAL CHALLENGES
-Cardiovascular Health
- Gene Therapy
- Alzheimer’s Disease
- Lifespan Development
- BRAIN
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S U M M I T
and many others
Open Science
Open Data at NIH
STRIDE
S
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S U M M I T 31
NIH’s Longstanding Commitment to Data &
Resource Sharing
20042003 2007 20142008
NIH Model
Organism
Policy
NIH Genome-wide
Association
(GWAS) Policy
2012
NIH Public
Access Policy
(Publications)
Big Data to
Knowledge
(BD2K)
Initiative
NIH Genomic
Data Sharing
(GDS) Policy
White House
Initiative
(“OSTP Memo”)
Increasing Access
Results Fed-
Funded Sci
Research
2015 2017
NIH Public
Access Plan
NIH Data
Sharing
Policy
Modernization of
NIH Clinical Trials
Request for
Information
(RFI) on Data
Sharing
NIH Data
Commons
Pilot
2016
Cancer
Moonshot
2013
NLM
Strategic
Plan
NIH Data
Science
Strategic Plan
2018
RFI on Policy
Provisions for Data
Management
and Sharing
HHS Rule and NIH
Policy on Clinical Trial
Results Dissemination
NIH New Models for
Data Stewardship and
STRIDES Initiative
21st Century
Cures Act
NIH All of Us
Research
Program
Precision
Medicine
Initiative
Adapted from NIH Office of Science Policy
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S U M M I T
NLM as a platform for
biomedical discovery
& data-powered health
New ways
to reach users with
new information and
new tools
A workforce prepared
to advance
data-driven discovery
& data-powered health
NLM
Transforming Information into Discovery
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S U M M I T
Fostering a
sphere of discovery:
digital research objects
Protocols
Funding
Code
Models
Clinical Data
Literature
Study Data
People
Pathways
Instruments
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S U M M I T
Critical Issues in the Development and Use of AI for Biomedical
Discovery
Rigor and Reproducibility
Protection of participant privacy
Discoverability of data sets
Data Stewardship: Preservation, Sustainability, Sharing etc.
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S U M M I T
Tomas Diaz de la Rubia
Vice President for Discovery Park, Purdue University
Tomás Díaz de la Rubia is Purdue University's vice president for Discovery
Park. In this position, his responsibilities include building upon Discovery Park's
foundation of excellence, which has enabled high-impact research that crosses
traditional academic boundaries. Prior to Purdue, Tomás served as chief
research officer and deputy laboratory director for science and technology at the
Lawrence Livermore National Laboratory (LLNL) in California, where he was
responsible for the science and technology foundations of the laboratory’s $1.6
billion research program. In this capacity, he oversaw a $300M program of basic
and applied research, and was responsible for the Laboratory’s industrial
partnerships and technology commercialization. Tomás has published more
than 150 peer-reviewed articles and has co-edited several books and
conference proceedings. He is a fellow of the American Physical Society and of
the American Association for the Advancement of Science and served as an
elected member of the board of directors of the Materials Research Society, and
vice-chair of the division of computational physics of the American Physical
Society. He holds a bachelor's degree and a doctorate in physics from The State
University of New York, Albany.
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S U M M I T
Azer Bestavros
Director, Hariri Institute for Computing, Boston University
Azer Bestavros is Warren Distinguished Professor of CS
and Founding Director of the Hariri Institute for Computing
at BU, an incubator for high-risk, high-reward cross-
disciplinary projects. His current research is on the design
and implementation of scalable secure multiparty
computation platforms to enable analytics over private data.
Funded by over $30M from government and industry, his
research yielded 18 PhD theses, 8 patents, 2 startups, and
hundreds of papers with over 20,000 citations.
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S U M M I T
Sharing Knowledge without Sharing Data
Towards Fairness, Ethics, Accountability, and Transparency
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S U M M I T
ML: Actionable Knowledge from Data
𝑓 𝑥1, 𝑥2, 𝑥3, …
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S U M M I T
ML: The Bad News…
 Repeated queries to model leak inputs.
 Adversaries can pollute input to reveal data.
 Adversaries can steal the learned models.
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S U M M I T
Differential Privacy: The Promise
Credit: https://tinyurl.com/y6x9kdyx
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S U M M I T
ML & DP: The Good News
CloakwithDP
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S U M M I T
ML: Actionable Knowledge from Data
g 𝑥1, 𝑥2, 𝑥3, …
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S U M M I T
Multi-Party Computation: The Promise
K = 𝑓 𝑥1, 𝑥2, 𝑥3, …
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S U M M I T
Dec 11 2013
GOAL 3: Evaluating Success
Employers agree to … contribute data
to a report compiled by a third party ...
Employer-level data would not be
identified in the report.
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S U M M I T
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S U M M I T
November, 2017 ++
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S U M M I T
The congresswoman, who
had signed onto a bill
addressing income disparity
between men and women,
was impressed by the
relevance he outlined. “It’s
linking it back for the members
of Congress,” Clark said.
“Nobody would think, oh, the
Paycheck Fairness Act, how is
that tied into NSF funding?”
2014  2018
2017
2015
“[MPC] has never been used for public good. Here, we’re
beginning to show how to use this sophisticated computer
science research for public programs.”
BWWC co-chair Evelyn Murphy
2014
“This [is] the first time actual wage data has been
reported both anonymously and voluntarily. This is a
groundbreaking moment in tackling the gender gap.”
Mayor Marty Walsh
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S U M M I T
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R
S U M M I T
NSF Program on Fairness in Artificial Intelligence in Collaboration
with Amazon (FAI)
https://www.nsf.gov/pubs/2019/nsf19571/nsf19571.htm
Thank you!
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S U M M I T
Sanjay Padhi: sanpadhi@amazon.com
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S U M M I T
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S U M M I T

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Panel: AI for Social Good - Fairness, Ethics, Accountability, and Transparency

  • 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AI for Social Good - Fairness, Ethics, Accountability, and Transparency
  • 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Opinions expressed are solely our own and do not express the views or opinions of Amazon or AWS
  • 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Machine Learning 1980s A technique that allows computer to perform task without being explicitly programmed AI, Machine Learning, and Deep Learning Artificial Intelligence 1950s Any techniques that allows computer to mimic human intelligence Turing Test Perceptron Deep Learning 2010s A subfield of machine learning that uses neural network to learning
  • 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T 3 Types of Machine Learning Supervised Machine Learning Task driven • Training Data: (X,Y) (Features, Labels) • Predict: Y, minimizing some loss • Classification, Regression Unsupervised Machine Learning Data driven • Training Data: X (features only) • Find similar points in high-dim X-space • Clustering Reinforcement Learning Decision making • Training data: (State, Action, Reward) • Maximize long term rewards • Robotics, games
  • 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T
  • 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AI for Social Good (Collaboration: AWS, NSF and University of Nevada) https://www.unr.edu/nevada-today/news/2019/big-data-wildfires
  • 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Source: Weather Channel Machine Learning for Improving Disaster Management and Response Session ID: 301069 - Artificial Intelligence and Machine Learning in Research
  • 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AI for Social Good
  • 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Evolving AI Capabilities
  • 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Survey of Judges: Case for Human Decision making William Austin and Thomas A. Williams III, ‘A survey of judges’ responses to simulated legal cases: research note on sentencing disparity’, Journal of Criminal Law and Criminology , vol. 68, no. 2, 1977, pp. 306–310. Algorithmic Learning:
  • 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Applicability in various areas:
  • 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T
  • 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Fairness • Protected Attributes : like race, gender, age, religion and their proxies • Classification parity, meaning that common measures of predictive performance (e.g., false positive and false negative rates) • Outcomes are independent of protected attributes. Transparency Accountability Explainability Example: DARPA’s program on explainable AI. https://www.darpa.mil/program/explainable-artificial-intelligence
  • 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Prem Natarajan is a Vice President in Alexa and leads a multidisciplinary science, engineering, and product organization which improves customer experience worldwide through advances in natural language understanding, entity linking and resolution, and related machine learning technologies. Before joining Amazon, he was senior vice dean of engineering at the University of Southern California where he led nationally influential DARPA and IARPA sponsored research efforts in biometrics/face recognition, OCR, natural language processing, media forensics, and forecasting. Prior to that, he served as executive vice president and principal scientist for speech, language, and multimedia at Raytheon BBN Technologies.
  • 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T AI for Social Good
  • 16. AMAZON.COM INC. 2019. ALL RIGHTS RESERVED. AI for Social Good  Social “good” comes in many forms  Better education  Faster, cheaper drug discovery  More effective policy making  Predicting and responding to natural disasters, epidemics  And much more ….  Commonly accepted attributes of “AI for Social Good”  Collaboration of multiple disciplines, especially social sciences and AI  Public-private partnership + nonprofits-academia-industry collaboration  Open access to technological resources  Considerations of bias, fairness, and accountability of ML algorithms
  • 17. AMAZON.COM INC. 2019. ALL RIGHTS RESERVED. Current State  Growing investments across academia, industry, and government  Several academia-based centers of “AI for Social Good” or “AI in Society” have emerged in recent years with diverse themes ranging from algorithms to policy  Industry initiatives span in-house efforts and extramural community creation efforts such as the NSF-Amazon Fairness Program for funding fairness research in academia  Substantial Government investments – e.g. DARPA LORELEI, Memex, World Modelers, XAI and many other programs  Emergence of conferences and workshops  FATML – Fairness, Accountability and Transparency in ML  AI for Social Good workshop at NeurIPS
  • 18. AMAZON.COM INC. 2019. ALL RIGHTS RESERVED. Current State – AI Stack View Apps Toolkits ML Dev Environments Algorithms Compute / Storage Mostly Open source (e.g. MXNet) but includes dev environments like Alexa Skills Kit Requires targeted funding for fairness, transparency, etc. Requires Funding Cost-effective models of access
  • 19. AMAZON.COM INC. 2019. ALL RIGHTS RESERVED. Alexa Skills for Social Good  Organized contest in 2018 to encourage creation of Alexa skills for social good  Red Cross skills: hurricane alerts, scheduling blood donations, and first aid  Environmental consciousness skills: recycle Game, EVIE assistant, compost tracking, bike sharing  Access skills: My Talking Newspaper, Safe and Well (check on status of relatives)  Language Preservation (with the Alexa Cleo Skill)  Cleo skill harnesses the expertise of multilingual Alexa users to teach Alexa new languages or dialects. Through a crowdsourcing model, users can help expand Alexa to new locales and languages, bringing the technology to more people around the world.  Users have taught Alexa languages such as Hindi, Korean, Russian, Klingon and many more.  We are conducting an internal pilot to evaluate programs to support language preservation with Indigenous languages such as Lakota and Ojibwe.
  • 20. AMAZON.COM INC. 2019. ALL RIGHTS RESERVED. Doing Well by Doing Good* “How People with Disabilities Are Using AI to Improve Their Lives” “It was the first time since he was a toddler playing with a rattler that he was able to interact with something all by himself,” James says. “This Echo device goes way beyond ordering groceries or looking up a recipe for us." --- NPR Nova 30 January 2019 “How the Alexa Robot brought internet-based learning to a remote village school in Maharashtra” “….. people on ground zero have emerged as change-makers themselves with a little help from Amazon devices. Here’s one such story that is nothing but a triumph of human imagination.” “In the hot, dry, and dusty village of Warud in Maharashtra’s Amravati district, a 31-year-old schoolteacher is using Alexa to impart lessons to kids of farmers and labourers employed in the vicinity.” --- Yourstory.com and The Hindu newspaper, 4 Feb 2019 *Prof. Andrew Lo at re:MARS 2019
  • 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Jim Kurose Assistant Director, CISE, National Science Foundation Dr. Jim Kurose is an Assistant Director of the National Science Foundation, where he leads the Directorate for Computer and Information Science and Engineering (CISE). With an annual budget of nearly $1B, CISE’s mission is to uphold the nation's leadership in scientific discovery and engineering innovation through its support of fundamental research in computer and information science and engineering, transformative advances in cyberinfrastructure, and preparation of a diverse computing-capable workforce. Jim also co-chairs the Networking and Information Technology Research and Development (NITRD) Program, the Subcommittee on Machine Learning and AI, and the Subcommittee on Open Science of the National Science and Technology Council (NSTC), facilitating the coordination of these research and development efforts across Federal agencies. Recently, Jim also served as the Assistant Director for Artificial Intelligence in the US Office of Science and Technology Policy (OSTP). Jim is on leave from the University of Massachusetts, Amherst, where he is Distinguished University Professor of Computer Science
  • 22. Information & Intelligent Systems Computing & Communication Foundations Computer & Network Systems Advanced Cyberinfrastructure Panel: AI for Social Good - Fairness, Ethics, Accountability, and Transparency AWS Public Sector Summit Jim Kurose Assistant Director, NSF Computer & Information Science & Engineering Federal AI R&D Activities: a view from NSF
  • 23. AI: ongoing US government activities AI Executive Order (Feb 2019) HSST AI Roundtable (May 2019) Congress Senate, House legislative activities AI Convening @ NSF (May 2019)  Envisioning National AI R&D Institutes  Policy and principles  Objectives  Roles and responsibilities  Federal Investment in AI R&D  Data, Computing for AI R&D  Guidance for Regulation of AI Applications  AI and the American workforce  Action Plan for Protection of the United States Advantage in AI
  • 24. AI principles Principles for responsible stewardship of trustworthy AI  Inclusive growth, sustainable development and well-being  Human-centred values and fairness  Transparency and explainability  Robustness, security and safety  Accountability National policies and international co-operation for trustworthy  Investing in AI R&D  Fostering a digital ecosystem for AI  Building human capacity, preparing for labour market transformation  International cooperation for trustworthy AI OECD Principles on AI, May 22, 2019
  • 25. Fairness in the AI System Lifecycle Artificial Intelligence in Society, June 12, 2019
  • 26. NSF Leadership in AI NSF invested nearly $450M in AI research (core, applications, systems, infrastructure) in FY18 $ Thought Leadership Across USG Innovative Programmatics NSTC Select Committee on AI NSTC Subcommittee on ML & AI NSTC AI Interagency Working Group (under NITRD): 2016, 2019 AI R&D Strategic Plans OSTP Assistant Director(s) for AI International: OECD, G7 Research Funding
  • 27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Patricia Flatley Brennan, RN, PhD Director, National Library of Medicine National Institutes of Health Prem Natarajan is a Vice President in Alexa and leads a multidisciplinary science, engineering, and product organization which improves customer experience worldwide through advances in natural language understanding, entity linking and resolution, and related machine learning technologies. Before joining Amazon, he was senior vice dean of engineering at the University of Southern California where he led nationally influential DARPA and IARPA sponsored research efforts in biometrics/face recognition, OCR, natural language processing, media forensics, and forecasting. Prior to that, he served as executive vice president and principal scientist for speech, language, and multimedia at Raytheon BBN Technologies.
  • 28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T The National Institutes of Health NEI NCI NHLBI NLM NINDS NIMH NIAMS NINR NCCIH NHGRI NIA NIAAA NIAID NICHD NIDCD NIDCR NIDDK NIDA NIEHS OD NIBIB NIMHD NCATS CIT CC CSR FICNIGMS
  • 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T BIOMEDICAL CHALLENGES -Cardiovascular Health - Gene Therapy - Alzheimer’s Disease - Lifespan Development - BRAIN
  • 30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T and many others Open Science Open Data at NIH STRIDE S
  • 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T 31 NIH’s Longstanding Commitment to Data & Resource Sharing 20042003 2007 20142008 NIH Model Organism Policy NIH Genome-wide Association (GWAS) Policy 2012 NIH Public Access Policy (Publications) Big Data to Knowledge (BD2K) Initiative NIH Genomic Data Sharing (GDS) Policy White House Initiative (“OSTP Memo”) Increasing Access Results Fed- Funded Sci Research 2015 2017 NIH Public Access Plan NIH Data Sharing Policy Modernization of NIH Clinical Trials Request for Information (RFI) on Data Sharing NIH Data Commons Pilot 2016 Cancer Moonshot 2013 NLM Strategic Plan NIH Data Science Strategic Plan 2018 RFI on Policy Provisions for Data Management and Sharing HHS Rule and NIH Policy on Clinical Trial Results Dissemination NIH New Models for Data Stewardship and STRIDES Initiative 21st Century Cures Act NIH All of Us Research Program Precision Medicine Initiative Adapted from NIH Office of Science Policy
  • 32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T NLM as a platform for biomedical discovery & data-powered health New ways to reach users with new information and new tools A workforce prepared to advance data-driven discovery & data-powered health NLM Transforming Information into Discovery
  • 33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Fostering a sphere of discovery: digital research objects Protocols Funding Code Models Clinical Data Literature Study Data People Pathways Instruments
  • 34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Critical Issues in the Development and Use of AI for Biomedical Discovery Rigor and Reproducibility Protection of participant privacy Discoverability of data sets Data Stewardship: Preservation, Sustainability, Sharing etc.
  • 35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Tomas Diaz de la Rubia Vice President for Discovery Park, Purdue University Tomás Díaz de la Rubia is Purdue University's vice president for Discovery Park. In this position, his responsibilities include building upon Discovery Park's foundation of excellence, which has enabled high-impact research that crosses traditional academic boundaries. Prior to Purdue, Tomás served as chief research officer and deputy laboratory director for science and technology at the Lawrence Livermore National Laboratory (LLNL) in California, where he was responsible for the science and technology foundations of the laboratory’s $1.6 billion research program. In this capacity, he oversaw a $300M program of basic and applied research, and was responsible for the Laboratory’s industrial partnerships and technology commercialization. Tomás has published more than 150 peer-reviewed articles and has co-edited several books and conference proceedings. He is a fellow of the American Physical Society and of the American Association for the Advancement of Science and served as an elected member of the board of directors of the Materials Research Society, and vice-chair of the division of computational physics of the American Physical Society. He holds a bachelor's degree and a doctorate in physics from The State University of New York, Albany.
  • 36. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Azer Bestavros Director, Hariri Institute for Computing, Boston University Azer Bestavros is Warren Distinguished Professor of CS and Founding Director of the Hariri Institute for Computing at BU, an incubator for high-risk, high-reward cross- disciplinary projects. His current research is on the design and implementation of scalable secure multiparty computation platforms to enable analytics over private data. Funded by over $30M from government and industry, his research yielded 18 PhD theses, 8 patents, 2 startups, and hundreds of papers with over 20,000 citations.
  • 37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Sharing Knowledge without Sharing Data Towards Fairness, Ethics, Accountability, and Transparency
  • 38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T ML: Actionable Knowledge from Data 𝑓 𝑥1, 𝑥2, 𝑥3, …
  • 39. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T ML: The Bad News…  Repeated queries to model leak inputs.  Adversaries can pollute input to reveal data.  Adversaries can steal the learned models.
  • 40. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Differential Privacy: The Promise Credit: https://tinyurl.com/y6x9kdyx
  • 41. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T ML & DP: The Good News CloakwithDP
  • 42. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T ML: Actionable Knowledge from Data g 𝑥1, 𝑥2, 𝑥3, …
  • 43. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Multi-Party Computation: The Promise K = 𝑓 𝑥1, 𝑥2, 𝑥3, …
  • 44. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Dec 11 2013 GOAL 3: Evaluating Success Employers agree to … contribute data to a report compiled by a third party ... Employer-level data would not be identified in the report.
  • 45. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T
  • 46. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T November, 2017 ++
  • 47. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T The congresswoman, who had signed onto a bill addressing income disparity between men and women, was impressed by the relevance he outlined. “It’s linking it back for the members of Congress,” Clark said. “Nobody would think, oh, the Paycheck Fairness Act, how is that tied into NSF funding?” 2014  2018 2017 2015 “[MPC] has never been used for public good. Here, we’re beginning to show how to use this sophisticated computer science research for public programs.” BWWC co-chair Evelyn Murphy 2014 “This [is] the first time actual wage data has been reported both anonymously and voluntarily. This is a groundbreaking moment in tackling the gender gap.” Mayor Marty Walsh
  • 48. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T
  • 49. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon (FAI) https://www.nsf.gov/pubs/2019/nsf19571/nsf19571.htm
  • 50. Thank you! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Sanjay Padhi: sanpadhi@amazon.com
  • 51. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T