Face recognition R&D has made great progress in recent years and has been prominent in the news. In public policy many are calling for a reversal of the trajectory for FR systems and products. In the hands of people of good will – using products designed for safety and training systems with appropriate data – society and individuals could have a better life. The Vergereports China’s use of unique facial markings of pandas to identify individual animals. FR research includes work to mitigate negative outcomes, as with the Adobe and UC Berkeley work on Detecting Facial Manipulations in Adobe Photoshop: automatic detect when images of faces have been manipulated by use of splicing, cloning, and removing an object.
Intentional and unintentional application of systems that are not designed and trained for ethical use are a threat to society. Screening for terrorists could be good, but FR lie and fraud detection systems may not work properly. The safety of FR is currently an important issue for policymakers, but regulations could have negative consequences for AI researchers. As with many contemporary issues, conflicts arise because of conflicting policies in different countries.
Recent and current legislation is attempting to restrict FR the use and possibly research. * San Francisco, CA and Somerville, MA, and Oakland, CA, are the first three cities to limit use of FR to identify people. * “Facial recognition may be banned from public housing thanks to proposed law” – CNET reports that a bill will be introduced to address the issue that “… landlords across the country continue to install smart home technology and tenants worry about unchecked surveillance, there’s been growing concern about facial recognition arriving at people’s doorsteps.” * The major social media companies are being pressed on “how they plan to handle the threat of deepfake images and videos on their platforms ahead of the 2020 elections.” * A call for a more comprehensive ban on FR has been launched by the digital rights group Fight for the Future, seeking a complete Federal ban on government use of facial recognition surveillance.
Beyond legislation against FR research and banning certain products, work is in progress to enable safe and ethical use of FR. A more general example that could be applied to FR is the MITRE work The Ethical Framework for the Use of Consumer-Generated Data in Health Care, which “establishes ethical values, principles, and guidelines to guide the use of Consumer-Generated Data for health care purposes.”
The past few weeks have been
busy with government events and announcements on AI Policy.
The G20 on AI
Ministers from the Group of 20 major economies conducted meetings on trade and the digital economy. They produced guiding principles for using artificial intelligence based on principles adopted last month by the 36-member OECD and an additional six countries. The G20 guidelines call for users and developers of AI to be fair and accountable, with transparent decision-making processes and to respect the rule of law and values including privacy, equality, diversity and internationally recognized labor rights. Meanwhile, the principles also urge governments to ensure a fair transition for workers through training programs and access to new job opportunities.
Bipartisan Group of Legislators Act on “Deepfake” Videos
The senators introduced legislation Friday intended to lessen the threat posed by “deepfake” videos — those created with AI technologies to manipulate original videos and produce misleading information. With this legislation, the Department of Homeland Security would conduct an annual study of deepfakes and related content and require the department to assess the AI technologies used to create deepfakes. This could lead to changes to regulations or new regulations impacting the use of AI.
Hearing on Societal and Ethical Implications of AI
The House Science, Space and Technology Committee held a hearing. June 26th on the societal and ethical implications of artificial intelligence, now available on video.
The National Artificial Intelligence Research and Development Strategic Plan, released in June, is an update of the report by the Select Committee on Artificial Intelligence of The National Science & Technology Council.
February 11, 2019, the President signed Executive Order 13859 Maintaining
American Leadership in Artificial Intelligence. According to Michael Kratsios, Deputy Assistant to the President for
Technology Policy, this order “launched the American AI Initiative, which is a
concerted effort to promote and protect AI technology and innovation in the
United States. The Initiative implements a whole-of-government strategy in
collaboration and engagement with the private sector, academia, the public, and
likeminded international partners. Among other actions, key directives in the
Initiative call for Federal agencies to prioritize AI research and development
(R&D) investments, enhance access to high-quality cyberinfrastructure and
data, ensure that the Nation leads in the development of technical standards
for AI, and provide education and training opportunities to prepare the
American workforce for the new era of AI.
“The first seven strategies
continue from the 2016 Plan, reflecting the reaffirmation of the importance of
these strategies by multiple respondents from the public and government, with
no calls to remove any of the strategies. The eighth strategy is new and
focuses on the increasing importance of effective partnerships between the
Federal Government and academia, industry, other non-Federal entities, and international
allies to generate technological breakthroughs in AI and to rapidly transition
those breakthroughs into capabilities.”
Strategy 8: Expand
Public–Private Partnerships to Accelerate Advances in AI is new in the June, 2019,
plan and “reflects the growing importance of public-private partnerships
enabling AI R&D an expands public-private partnerships to accelerate
advances in AI. Promote opportunities for sustained investment in AI R&D
and for transitioning advances into practical capabilities, in collaboration
with academia, industry, international partners, and other non-Federal
Continued points from the seven
Strategies in the previous Executive Order in February include
1. support for the
development of instructional materials and teacher professional
development in computer science at all levels, with emphasis at the K–12 levels
2. consideration of
AI as a priority area within existing Federal fellowship and service
3. development AI techniques
for human augmentation
4. emphasis on achieving
trust: AI system designers need to create accurate, reliable systems with
informative, user-friendly interfaces.
The National Science and Technology Council (NSTC) is functioning again. NSTC is the principal means by which the Executive Branch coordinates science and technology policy across the diverse entities that make up the Federal research and development enterprise. A primary objective of the NSTC is to ensure that science and technology policy decisions and programs are consistent with the President’s stated goals. The NSTC prepares research and development strategies that are coordinated across Federal agencies aimed at accomplishing multiple national goals. The work of the NSTC is organized under committees that oversee subcommittees and working groups focused on different aspects of science and technology. More information is available at https://www.whitehouse.gov/ostp/nstc.
The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office of the President with advice on the scientific, engineering, and technological aspects of the economy, national security, homeland security, health, foreign relations, the environment, and the technological recovery and use of resources, among other topics. OSTP leads interagency science and technology policy coordination efforts, assists the Office of Management and Budget with an annual review and analysis of Federal research and development (R&D) in budgets, and serves as a source of scientific and technological analysis and judgment for the President with respect to major policies, plans, and programs of the Federal Government. More information is available at https://www.whitehouse.gov/ostp.
that advise and assist the NSTC on AI include
The Select Committee on Artificial Intelligence (AI) addresses Federal AI
R&D activities, including those related to autonomous systems, biometric
identification, computer vision, human computer interactions, machine learning,
natural language processing, and robotics. The committee supports policy on
technical, national AI workforce issues.
The Subcommittee on Machine Learning and Artificial Intelligence monitors the
state of the art in machine learning (ML) and artificial intelligence within
the Federal Government, in the private sector, and internationally.
The Artificial Intelligence Research & Development Interagency Working
Group coordinates Federal R&D in AI and supports and coordinates activities
tasked by the Select Committee on AI and the NSTC Subcommittee on Machine
Learning and Artificial Intelligence.
With AI in the news so much over the past year, the public awareness of potential problems arising from the proliferation of AI systems and products has led to increasing calls for regulation. The popular media, and even technical media, contain misinformation and misplaced fears, but plenty of legitimate issues exist even if their relative importance is sometimes misunderstood. Policymakers, researchers, and developers need to be in dialog about the true needs and potential dangers of regulation.
“Google top lawyer pushes back against one-size-fits-all rules for AI” by Janosch Delcker at POLITICO is an example of corporate reaction to the calls for regulation. “Understanding exactly the applications that we see for AI, and how those should be regulated, that’s an important next chapter,” Kent Walker, Google’s senior vice president for global affairs and the company’s chief legal officer, told POLITICO during a recent visit to Germany. “But you generally don’t want one-size-fits-all regulation, especially for a tool that is going to be used in a lot of different ways,” he added.
From our policy perspective, the significant risks from AI systems include misuse and faulty unsafe designs that can create bias, non-transparency of use, and loss of privacy. AI systems are known to discriminate against minorities, unintentionally and not. An important discussion we should be having is if governments, international organizations, and big corporations, which have already released dozens of non-binding guidelines for the responsible development and use of AI, are the best entities for writing and enforcing regulations. Non-binding principles will not make some companies developing and applying AI products accountable. An important point in this regard is to hold companies responsible for the product design process itself, not just for testing products after they are in use.
Introduction of new government regulations is a long process and subject to pressure from lobbyists, and the current US administration is generally inclined against regulations anyway. We should discuss alternatives like clearinghouses and consumer groups endorsing AI products designed for safety and ethical use. If well publicized, the endorsements of respected non-partisan groups including professional societies might be more effective and timely than government regulations. The European Union has released its Ethics Guidelines for Trustworthy AI, and a second document with recommendations on how to boost investment in Europe’s AI industry is to be published. In May, 2019, the Organization for Economic Cooperation and Development (OECD) issued their first set of international OECD Principles on Artificial Intelligence, which are embraced by the United State and leading AI companies.
AAAI has established a new mailing list on US Policy that will
focus exclusively on the discussion of US policy matters related to artificial
intelligence. All members and affiliates are invited to join the list at https://aaai.org/Organization/mailing-lists.php
Participants will have the opportunity to subscribe or unsubscribe at any time. The mailing list will be moderated, and all posts will be approved before dissemination. This is a great opportunity for another productive partnership between AAAI and SIGAI policy work.
EPIC Panel on June 5th
A panel on AI, Human Rights, and US policy, will be hosted by the Electronic Privacy Information Center (EPIC) at their annual meeting (and celebration of 25th anniversary) on June 5, 2019, at the National Press Club in DC. Our Lorraine Kisselburgh will join Harry Lewis (Harvard), Sherry Turkle (MIT), Lynne Parker (UTenn and White House OSTP director for AI), Sarah Box (OECD), and Bilyana Petkova (EPIC and Maastricht) to discuss AI policy directions for the US. The event is free and open to the public. You can register at https://epic.org/events/June5AIpanel/
2019 ACM SIGAI
Please remember to vote and to review the information on http://www.acm.org/elections/sigs/voting-page. Please note that 16:00 UTC, 14 June 2019 is the deadline for submitting your vote. To access the secure voting site, you will enter your email
address (the one associated with your ACM/SIG member record) to reach the menu
of active SIG elections for which you are eligible. In the online menu, select your
Special Interest Group and enter the 10-digit Unique Pin.
We are pleased to announce that the recipients of the 2018 ACM A.M. Turing Award are AI researchers Yoshua Bengio, Professor at the University of Montreal and Scientific Director at Mila; Geoffrey Hinton, Professor at the University of Toronto and Chief Scientific Advisor at the Vector Institute; and Yann LeCun, Professor at New York University and Chief AI Scientist at Facebook.
Their citation reads as follows:
For conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
Bengio, Hinton, and LeCun will be presented with the Turing Award at the June 15, 2019 ACM Awards Banquet in San Francisco.
As employers increasingly adopt automation technology, many workforce analysts look to jobs and career paths in new disciplines, especially data science and applications of AI, to absorb workers who are displaced by automation. By some accounts, data science is in first place for technology career opportunities. Estimating current and near-term numbers of data scientists and AI professionals is difficult because of different job titles and position descriptions used by organizations and job recruiters. Likewise, many employees in positions with traditional titles have transitioned to data science and AI work. Better estimates, and at least upper limits, are necessary for evidence-based predictions of unemployment rates due to automation over the next decade. McKinsey&Company estimates 375 million jobs will be lost globally due to AI and other automation technologies by 2030, and one school of thought in today’s public discourse is that at least that number of new jobs will be created. An issue for the AI community and policy makers is the nature, quality, and number of the new jobs – and how many data science and AI technology jobs will contribute to meeting the shortfall.
An article in KDnuggets by Gregory Piatetsky points out that a “Search for data scientist (without quotes) finds about 30,000 jobs, but we are not sure how many of those jobs are for scientists in other areas … a person employed to analyze and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision-making … titles include Data Scientist, Data Analyst , Statistician, Bioinformatician, Neuroscientist, Marketing executive, Computer scientist, etc…” Data on this issue could clarify the net number of future jobs in AI, data science, and related areas. Computer science had a similar history with the boom in the new field followed by migration of computing into many other disciplines. Another factor is that “long-term, however, automation will be replacing many jobs in the industry, and Data Scientist job will not be an exception. Already today companies like DataRobot and H2O offer automated solutions to Data Science problems. Respondents to KDnuggets 2015 Poll expected that most expert-level Predictive Analytics/Data Science tasks will be automated by 2025. To stay employed, Data Scientists should focus on developing skills that are harder to automate, like business understanding, explanation, and story telling.” This issue is also important in estimating the number of new jobs by 2030 for displaced workers.
Kiran Garimella in his Forbes article “Job
Loss From AI? There’s More To Fear!” examines
the scenario of not enough new jobs to replace ones lost through automation.
His interesting perspective turns to economists, sociologists, and insightful
re-examine and re-formulate their models of human interaction and organization
and … re-think incentives and agency relationships.”
A recent controversy erupted over OpenAI’s new version of
their language model for generating well-written next words of text based on
unsupervised analysis of large samples of writing. Their announcement and
decision not to follow open-source practices raises interesting policy issues
about regulation and self-regulation of AI products. OpenAI, a non-profit AI
research company founded by Elon Musk and others, announced on
February 14, 2019, that “We’ve trained a large-scale unsupervised language
model which generates coherent paragraphs of text, achieves state-of-the-art
performance on many language modeling benchmarks, and performs rudimentary
reading comprehension, machine translation, question answering, and
summarization—all without task-specific training.”
The reactions to the announcement followed from the decision behind the following statement in the release: “Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper.”
Examples of the many reactions are TechCrunch.com and Wired. The Electronic Frontier Foundation has an analysis of the manner of the release (letting journalists know first) and concludes, “when an otherwise respected research entity like OpenAI makes a unilateral decision to go against the trend of full release, it endangers the open publication norms that currently prevail in language understanding research.”
This issue is an example of previous ideas in our Public Policy
blog about who, if anyone, should regulate AI developments and products that
have potential negative impacts on society. Do we rely on self-regulation or
require governmental regulations? What if the U.S. has regulations and other
countries do not? Would a clearinghouse approach put profit-based pressure on
developers and corporations? Can the open source movement be successful without
Welcome to the eighth interview in our se- ries profiling senior AI researchers. This month we are especially happy to interview our SIGAI advisory board member, Thomas Dietterich, Director of Intelligent Systems at the Institute for Collaborative Robotics and In- telligence Systems (CoRIS) at Oregon State University.
Dr. Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stan- ford University 1984) is Professor Emeritus in the School of Electrical Engineering and Computer Science at Oregon State University, where he joined the faculty in 1985. Dietterich is one of the pioneers of the field of Machine Learning and has authored more than 200 refereed publications and two books. His research is motivated by challenging real world problems with a special focus on ecological science, ecosystem management, and sustainable development. He is best known for his work on ensemble methods in machine learning including the development of error- correcting output coding. Dietterich has also invented important reinforcement learning algorithms including the MAXQ method for hierarchical reinforcement learning. Dietterich has devoted many years of service to the research community. He served as President of the Association for the Advancement of Artificial Intelligence (2014-2016) and as the founding president of the International Machine Learning Society (2001-2008). Other major roles include Executive Editor of the journal Machine Learning, co-founder of the Journal for Machine Learning Research, and Program Chair of AAAI 1990 and NIPS 2000. Dietterich is a Fellow of the ACM, AAAI, and AAAS.
Getting to Know Tom Dietterich
When and how did you become interested in CS and AI?
I learned to program in Basic in my early teens; I had an uncle who worked for GE on their time-sharing system. I learned Fortran in high school. I tried to build my own adding machine out of TTL chips around that time too. However, despite this interest, I didn’t really know what CS was until I reached graduate school at the University of Illinois. I first engaged with AI when I took a graduate assistant position with Ryszard Michalski on what became machine learning, and I took an AI class from Dave Waltz. I had also studied phi- losophy of science in college, so I had already thought a bit about how we acquire knowledge from data and experiment.
What would you have chosen as your career if you hadn’t gone into CS?
I had considered going into foreign service, and I have always been interested in policy issues. I might also have gone into technical management. Both of my brothers have been successful technical managers.
What do you wish you had known as a Ph.D. student or early researcher?
I wish I had understood the importance of strong math skills for CS research. I was a software engineer before I was a computer science researcher, and it took me a while to understand the difference. I still struggle with the difference between making an incremental advance within an existing paradigm versus asking fundamental questions that lead to new research paradigms.
What professional achievement are you most proud of?
Developing the MAXQ formalism for hierarchical reinforcement learning.
What is the most interesting project you are currently involved with?
I’m fascinated by the question of how machine learning predictors can have models of their own competence. This is important for mak- ing safe and robust AI systems. Today, we have ML methods that give accurate predictions in aggregate, but we struggle to provide point-wise quantification of uncertainty. Related to these questions are algorithms for anomaly detection and open category detection. In general, we need AI systems that can work well even in the presence of “unknown unknowns”.
Recent advances in AI led to many success stories of AI technology undertaking real-world problems. What are the challenges of deploying AI systems?
AI systems are software systems, so the main challenges are the same as with any soft- ware system. First, are we building the right system? Do we correctly understand the users’ needs? Have we correctly expressed user preferences in our reward functions, constraints, and loss functions? Have we done so in a way that respects ethical standards? Second, have we built the system we intended to build? How can we test software com- ponents created using machine learning? If the system is adapting online, how can we achieve continuous testing and quality assurance? Third, when ML is employed, the re- sulting software components (classifiers and similar predictive models) will fail if the input data distribution changes. So we must mon- itor the data distribution and model the pro- cess by which the data are being generated. This is sometimes known as the problem of “model management”. Fourth, how is the deployed system affecting the surrounding social and technical system? Are there unintended side-effects? Is user or institutional behavior changing as a result of the deployment?
One promising approach is combining humans and AI into a collaborative team. How can we design such a system to successfully tackle challenging high-risk applications? Who should be in charge, the human or the AI?
I have addressed this in a recent short paper (Robust Artificial Intelligence and Robust Human Organizations. Frontiers of Computer Science, 13(1): 1-3). To work well in high- risk applications, human teams must function as so-called “High reliability organizations” or HROs. When we add AI technology to such teams, we must ensure that it contributes to their high reliability rather than disrupting and degrading it. According to organizational researchers, HROs share five main practices: (a) continuous attention to anomalous and near-miss events, (b) seeking diverse explanations for such events, (c) maintaining continuous situational awareness, (d) practicing improvisational problem solving, and (e) delegating decision making authority to the team member who has the most expertise about the specific decision regardless of rank. AI systems in HROs must implement these five practices as well. They must be constantly watch- ing for anomalies and near misses. They must seek multiple explanations for such events (e.g., via ensemble methods). They must maintain situational awareness. They must support joint human-machine improvisational problem solving, such as mixed-initiative plan- ning. And they must build models of the expertise of each team member (including them- selves) to know which team member should make the final decision in any situation.
You ask “Who is in charge?” I’m not sure that is the right question. Our goal is to create human-machine teams that are highly reliable as a team. In an important sense, this means every member of the team has responsibil- ity for robust team performance. However, from an ethical standpoint, I think the human team leader should have ultimate responsibil- ity. That task of taking action in a specific situation could be delegated to the AI system, but the team leader has the moral responsibility for that action.
Moving towards transforming AI systems into high-reliable organizations, how can diversity help to achieve this goal?
Diversity is important for generating multiple hypotheses to explain anomalies and near misses. Experience in hospital operating rooms is that often it is the nurses who first detect a problem or have the right solution. The same has been noted in nuclear power plant operations. Conversely, teams often fail when the engage in “group think” and fixate on an incorrect explanation for a problem.
How do you balance being involved in so many different aspects of the AI community?
I try to stay very organized and manage my time carefully. I use a machine learning system called TAPE (Tagging Assistant for Productive Email) developed by my collaborator and student Michael Slater to automatically tag and organize my email. I also take copi- ous notes in OneNote. Oh, and I work long hours…
What was your most difficult professional decision and why?
The most difficult decision is to tell a PhD student that they are not going to succeed in completing their degree. All teachers and mentors are optimistic people. When we meet a new student, we hope they will be very successful. But when it is clear that a student isn’t going to succeed, that is a deep disappointment for the student (of course) but also for the professor.
What is your favorite AI-related movie or book and why?
I really don’t know much of the science fiction literature (in books or films). My favorite is 2001: A Space Odyssey because I think it depicts most accurately how AI could lead to bad outcomes. Unlike in many other stories, HAL doesn’t “go rogue”. Instead, HAL creatively achieves the objective programmed by its creators, unfortunately as a side effect, it kills the crew.
Nominations, including self nominations, are invited for a three-year term
as TALLIP EiC, beginning on June 1, 2019. The EiC appointment may be renewed at most one
time. This is an entirely voluntary position, but ACM will provide appropriate
Appointed by the ACM Publications Board,
Editors-in-Chief (EiCs) of ACM journals are delegated full responsibility for
the editorial management of the journal consistent with the journal’s charter
and general ACM policies. The Board relies on EiCs to ensure that the content
of the journal is of high quality and that the editorial review process is both
timely and fair. He/she has final say on acceptance of papers, size of
the Editorial Board, and appointment of Associate Editors. A complete list of
responsibilities is found in the ACM
Volunteer Editors Position Descriptions. Additional information can be
found in the following documents:
Nominations should include a vita along with a brief statement of why the
nominee should be considered. Self-nominations are encouraged, and should
include a statement of the candidate’s vision for the future development of TALLIP.
The deadline for submitting nominations is April 15, 2019, although nominations
will continue to be accepted until the position is filled.
A recent item in Science|Business “Artificial intelligence nowhere near the real thing, says German AI chief”, by Éanna Kelly, gives policy-worthy warnings and ideas. “In his 20 years as head of Germany’s biggest AI research lab Wolfgang Wahlster has seen the tech hype machine splutter three times. As he hands over to a new CEO, he warns colleagues: ‘Don’t over-promise’. … the computer scientist who has just ended a 20 year stint as CEO of the German Research Centre for Artificial Intelligence says that [warning] greatly underestimates the distance between AI and its human counterpart: ‘We’re years away from a game changer in the field. I always warn people, one should be a bit careful with what they claim. Every day you work on AI, you see the big gap between human intelligence and AI’, Wahlster told Science|Business.”
For AI policy, we should remember to look out for over promising, but we also need to be mindful of the time frame for making effective policy and be fully engaged now. Our effort importantly informs policymakers about the real opportunities to make AI successful. A recent article in The Conversation by Ben Shneiderman “What alchemy and astrology can teach artificial intelligence researchers,” gives insightful information and advice on how to avoid being distracted away “… from where the real progress is already happening: in systems that enhance – rather than replace – human capabilities.” Shneiderman recommends that technology designers shift “from trying to replace or simulate human behavior in machines to building wildly successful applications that people love to use.”