USTPC in the News


The ACM’s US Technology Policy Committee (USTPC) has been very active in July already! The contributions and visibility of USTPC as a group and as individual members are very welcome and impressive. The following list has links to highly-recommended reading.

Amicus Brief: USTPC Urges Narrower Definition of Computer Fraud and Abuse Act

ACM’s USTPC filed an amicus curiae (“friend of the court”) brief with the United States Supreme Court in the landmark case of Van Buren v. United States. “Van Buren marks the first time that the US Supreme Court has reviewed the Computer Fraud and Abuse Act (CFAA), a 1986 law that was originally intended to punish hacking. In recent years, however, the CFAA has been used to criminally prosecute both those who access a computer system without permission, as well as those who have permission but exceed their authority to use a database once logged in.”

USTPC Statement on Face Recognition

(USTPC) has assessed the present state of facial recognition (FR) technology as applied by government and the private sector. The Committee concludes that, “when rigorously evaluated, the technology too often produces results demonstrating clear bias based on ethnic, racial, gender, and other human characteristics recognizable by computer systems. The consequences of such bias, USTPC notes, frequently can and do extend well beyond inconvenience to profound injury, particularly to the lives, livelihoods and fundamental rights of individuals in specific demographic groups, including some of the most vulnerable populations in our society.”
See the NBC news article.

Barbara Simons recipient of the 2019 ACM Policy Award

USTPC’s Barbara Simons, founder of USTPC predecessor USACM, is the recipient of the 2019 ACM Policy Award for “long-standing, high-impact leadership as ACM President and founding Chair of ACM’s US Public Policy Committee (USACM), while making influential contributions to improve the reliability of and public confidence in election technology. Over several decades, Simons has advanced technology policy by founding and leading organizations, authoring influential publications, and effecting change through lobbying and public education.”
Congratulations, Barbara!

Potential New Issues

ACM Urges Preservation of Temporary Visa Exemptions for Nonimmigrant Students. Harvard filing is a complaint for declaratory and injunctive relief.

This issue may have dramatic impacts on university research and teaching this fall.

Thank you USTPC for your hard work and representation of ACM to policymakers!

AI and Facial Recognition

AI in Congress

Politico reports on two separate bills introduced Thursday, June 2. (See the section entitled “Artificial Intelligence: Let’s Do the Thing”.)

The National AI Research Resource Task Force Act. “The bipartisan, bicameral bill introduced by Reps. Anna Eshoo, (D-Calif.), Anthony Gonzalez (R-Ohio), and Mikie Sherrill (D-N.J.), along with companion legislation by Sens. Rob Portman (R-Ohio) and Martin Heinrich(D-N.M.), would form a committee to figure out how to launch and best use a national AI research cloud. Public and private researchers and developers from across the country would share this cloud to combine their data, computing power and other resources on AI. The panel would include experts from government, academia and the private sector.”

The Advancing Artificial Intelligence Research Act. “The bipartisan bill introduced by Senate Commerce Chairman Roger Wicker (R-Miss.), Sen. Cory Gardner (R-Colo.) and Gary Peters (D-Mich.), a founding member of the Senate AI Caucus, would create a program to accelerate research and development of guidance around AI at the National Institute of Standards and Technology. It would also create at least a half-dozen AI research institutes to examine the benefits and challenges of the emerging technology and how it can be deployed; provide funding to universities and nonprofits researching AI; and launch a pilot at the National Science Foundation for AI research grants.”

Concerns About Facial Recognition (FR): Discrimination, Privacy, and Democratic Freedom

While including ethical and moral issues, a broader list of issues is concerning to citizens and policymakers about face recognition technology and AI. Areas of concerns include accuracy; surveillance; data storage, permissions, and access; discrimination, fairness, and bias; privacy and video recording without consent; democratic freedoms, including right to choose, gather, and speak; and abuse of technology such as non-intended uses, hacking, and deep fakes. Used responsibly and ethically, face recognition can be valuable for finding missing people, responsible policing and law enforcement, medical uses, healthcare, virus tracking, legal system and court uses, and advertising. Various guidelines by organizations such as the AMA and legislation like S.3284 – Ethical Use of Facial Recognition Act are being developed to encourage the proper use of AI and face recognition.

Some of the above issues do specifically require ethical analysis as in the following by Yaroslav Kuflinski:

Accuracy — FR systems naturally discriminate against non-whites, women, and children, presenting errors of up to 35% for non-white women.

Surveillance issues — concerns about “big brother” watching society.

Data storage — use of images for future purposes stored alongside genuine criminals.

Finding missing people — breaches of the right to a private life.

Advertising — invasion of privacy by displaying information and preferences that a buyer would prefer to keep secret.

Studies of commercial systems are increasingly available, for example an analysis of Amazon Rekognition.

Biases deriving from sources of unfairness and discrimination in machine learning have been identified in two areas: the data and the algorithms.  Biases in data skew what is learned in machine learning methods, and flaws in algorithms can lead to unfair decisions even when the data is unbiased. Intentional or unintentional biases can exist in the data used to train FR systems.

New human-centered design approaches seek to provide intentional system development steps and processes in collecting data and creating high quality databases, including the elimination of naturally occurring bias reflected in data about real people.

Bias That Pertains Especially to Facial Recognition (Mehrabi, et al. and Barocas, et al.)

Direct Discrimination: “Direct discrimination happens when protected attributes of individuals explicitly result in non-favorable outcomes toward them”.  Some traits like race, color, national origin, religion, sex, family status, disability, exercised rights under CCPA , marital status, receipt of public assistance, and age are identified as sensitive attributes or protected attributes in the machine learning world.                       

Indirect Discrimination: Even if sensitive or protected attributes are not used against an individual, indirect discrimination can still happen. For example, residential zip code is not categorized as a protected attribute, but from the zip code one might infer race, which is a protected attribute. So, “protected groups or individuals still can get treated unjustly as a result of implicit effects from their protected attributes”.

Systemic Discrimination: “policies, customs, or behaviors that are a part of the culture or structure of an organization that may perpetuate discrimination against certain subgroups of the population”.

Statistical Discrimination: In law enforcement, racial profiling is an example of statistical discrimination. In this case, minority drivers are pulled over more than compared to white drivers — “statistical discrimination is a phenomenon where decision-makers use average group statistics to judge an individual belonging to that group.”

Explainable Discrimination: In some cases, discrimination can be explained using attributes like working hours and education, which is legal and acceptable. In “the UCI Adult dataset [6], a widely-used dataset in the fairness domain, males on average have a higher annual income than females; however, this is because on average females work fewer hours than males per week. Work hours per week is an attribute that can be used to explain low income. If we make decisions without considering working hours such that males and females end up averaging the same income, we could lead to reverse discrimination since we would cause male employees to get lower salary than females.                             

Unexplainable Discrimination: This type of discrimination is not legal as explainable discrimination because “the discrimination toward a group is unjustified”.

How to Discuss Facial Recognition

Recent controversies about FR mix technology issues with ethical imperatives and ignore that people can disagree on which are the “correct” ethical principles. A recent ACM tweet on FR and face masks was interpreted in different ways and ACM issued an official clarification. A question that emerges is if AI and other technologies should be, and can be, banned rather than controlled and regulated.

In early June, 2020, IBM CEO Arvind Krishna said in a letter to Congress that IBM is exiting the facial recognition business and asking for reforms to combat racism: “IBM no longer offers general purpose IBM facial recognition or analysis software. IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency,” Krishna said in his letter to members of congress, “We believe now is the time to begin a national dialogue on whether and how facial recognition technology should be employed by domestic law enforcement agencies.”

The guest co-author of this series of blog posts on AI and bias is Farhana Faruqe, doctoral student in the George Washington University Human-Technology Collaboration program.


AI is in the news and in policy discussions regarding COVID-19, both about ways to help fight the pandemic and in terms of ethical issues that policymakers should address. Michael Corkery and David Gelles in the NY Times article “Robots Welcome to Take Over, as Pandemic Accelerates Automation”, suggest that “social-distancing directives, which are likely to continue in some form after the crisis subsides, could prompt more industries to accelerate their use of automation.” An MIT Technology Review article by Genevieve Bell, “We need mass surveillance to fight covid-19—but it doesn’t have to be creepy” looks at the pros and cons of AI technology and if we now have the chance to “reinvent the way we collect and share personal data while protecting individual privacy.”

Public Health and Privacy Issues

Liza Lin and Timothy W. Martin in “How Coronavirus Is Eroding Privacy” write about how technology is being developed to track and monitor individuals for slowing the pandemic, but that this “raises concerns about government overreach.” Here is an excerpt from that WSJ article: “Governments worldwide are using digital surveillance technologies to track the spread of the coronavirus pandemic, raising concerns about the erosion of privacy. Many Asian governments are tracking people through their cellphones to identify those suspected of being infected with COVID-19, without prior consent. European countries are tracking citizens’ movements via telecommunications data that they claim conceals individuals’ identities; American officials are drawing cellphone location data from mobile advertising firms to monitor crowds, but not individuals. The biggest privacy debate concerns involuntary use of smartphones and other digital data to identify everyone with whom the infected had recent contact, then testing and quarantining at-risk individuals to halt the further spread of the disease. Public health officials say surveillance will be necessary in the months ahead, as quarantines are relaxed and the virus remains a threat while a vaccine is developed.

“In South Korea, investigators scan smartphone data to find within 10 minutes people who might have caught the coronavirus from someone they met. Israel has tapped its Shin Bet intelligence unit, usually focused on terrorism, to track down potential coronavirus patients through telecom data. One U.K. police force uses drones to monitor public areas, shaming residents who go out for a stroll.

“The Covid-19 pandemic is ushering in a new era of digital surveillance and rewiring the world’s sensibilities about data privacy. Governments are imposing new digital surveillance tools to track and monitor individuals. Many citizens have welcomed tracking technology intended to bolster defenses against the novel coronavirus. Yet some privacy advocates are wary, concerned that governments might not be inclined to unwind such practices after the health emergency has passed.

“Authorities in Asia, where the virus first emerged, have led the way. Many governments didn’t seek permission from individuals before tracking their cellphones to identify suspected coronavirus patients. South Korea, China and Taiwan, after initial outbreaks, chalked up early successes in flattening infection curves to their use of tracking programs.

“In Europe and the U.S., where privacy laws and expectations are more stringent, governments and companies are taking different approaches. European nations monitor citizen movement by tapping telecommunications data that they say conceals individuals’ identities.

American officials are drawing cellphone location data from mobile advertising firms to track the presence of crowds—but not individuals. Apple Inc. and Alphabet Inc.’s Google recently announced plans to launch a voluntary app that health officials can use to reverse-engineer sickened patients’ recent whereabouts—provided they agree to provide such information.”

Germany Changes Course on Contact Tracing App

Politico reports that “the German government announced today” (4/26) “that Berlin would adopt a ‘decentralized’ approach to a coronavirus contact-tracing app — now backing an approach championed by U.S. tech giants Apple and Google. ‘We will promote the use of a consistently decentralized software architecture for use in Germany,’ the country’s Federal Health Minister Jens Spahn said on Twitter, echoing an interview in the Welt am Sonntag newspaper. Earlier this month, Google and Apple announced they would team up to unlock their smartphones’ Bluetooth capabilities to allow developers to build interoperable contact tracing apps. Germany is now abandoning a centralized approach spearheaded by the German-led Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) project. Berlin’s U-turn comes after a group of six organizations on Friday urged Angela Merkel’s government to reassess plans for a smartphone app that traces potential coronavirus infections, warning that it does not do enough to protect user data.”

NSF Program on Fairness in Artificial Intelligence (FAI) in Collaboration with Amazon

A new National Science Foundation solicitation NSF 20-566 has been announced by the Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems, Directorate for Social, Behavioral and Economic Sciences, and Division of Behavioral and Cognitive Sciences.

Bias, Ethics, and Policy

We are planning a series of posts on Bias, starting with the background and context of bias in general and then focusing on specific instances of bias in current and emerging areas of AI. Ultimately, this information is intended to inform ideas on public policy. We look forward to your comments and suggestions for a robust discussion.

Extensive work “A Survey on Bias and Fairness in Machine Learning” by Ninareh Mehrabi et al. will be useful for the conversation. The guest co-author of the ACM SIGAI Public Policy blog posts on Bias will be Farhana Faruqe, doctoral student in the George Washington University Human-Technology Collaboration program.

A related announcement is about the new section on AI and Ethics in the Springer Nature Computer Science journal. “The AI & Ethics section focuses on how AI techniques, tools, and technologies are developing, including consideration of where these developments may lead in the future. It seeks to promote informed debate and discussion of the current and future developments in AI, and the ethical, moral, regulatory, and policy implications that arise from these developments.” As a Co-Editor of the new section, I welcome you to submit a manuscript and contact me with any questions and suggestions.

PCAST and AI Plan

Executive Order on The President’s Council of Advisors on Science and Technology (PCAST)

President Trump issued an executive order on October 22 re-establishing the President’s Council of Advisors on Science and Technology (PCAST), an advisory body that consists of science and technology leaders from the private and academic sectors. PCAST is to be chaired by Kelvin Droegemeier, director of the Office of Science and Technology Policy, and Edward McGinnis, formerly with DOE, is to serve as the executive director. The majority of the 16 members are from key industry sectors. The executive order says that the council is expected to address “strengthening American leadership in science and technology, building the Workforce of the Future, and supporting foundational research and development across the country.” For more information, see the Inside Education article about the first appointments.

Schumer AI Plan

Jeffrey Mervis has a November 11, 2019, article in AAAS News from Science on a recommendation for the government to create a new agency funded with $100 billion over 5 years for basic AI research. “Senator Charles Schumer (D–NY) says the initiative would enable the United States to keep pace with China and Russia in a critical research arena and plug gaps in what U.S. companies are unwilling to finance.”

Schumer gave his ideas publicly in a speech in early November to senior national security and research policymakers following a recent presidential executive order. He wants to create a new national science tech fund looking into “fundamental research related to AI and some other cutting-edge areas” such as quantum computing, 5G networks, robotics, cybersecurity, and biotechnology. Funds would encourage research at U.S. universities, companies, and other federal agencies and support incubators for moving research into commercial products. An additional article can be found in Defense News.

National AI Strategy

The National Artificial Intelligence Research and Development Strategic Plan – an update of the report by the Select Committee on Artificial Intelligence of The National Science & Technology Council – was released in June, 2019, and the President’s, Executive Order 13859 Maintaining American Leadership in Artificial Intelligence was released on February 11. The Computing Community Consortium (CCC) recently released the AI Roadmap Website, and an interesting industry response is “Intel Gets Specific on a National Strategy for AI, “How to Propel the US into a Sustainable Leadership Position on the Global Artificial Intelligence Stage” By Naveen Rao and David Hoffman. Excerpts follow and the accompanying links provide the details:

“AI is more than a matter of making good technology; it is also a matter of making good policy. And that’s what a robust national AI strategy will do: continue to unlock the potential of AI, prepare for AI’s many ramifications, and keep the U.S. among leading AI countries. At least 20 other countries have published, and often funded, their national AI strategies. Last month, the administration signaled its commitment to U.S. leadership in AI by issuing an executive order to launch the American AI Initiative, focusing federal government resources to develop AI. Now it’s time to take the next step and bring industry and government together to develop a fully realized U.S. national strategy to continue leading AI innovation.

“… But to sustain leadership and effectively manage the broad social implications of AI, the U.S. needs coordination across government, academia, industry and civil society. This challenge is too big for silos, and it requires that technologists and policymakers work together and understand each other’s worlds.” Their call to action was released in May 2018.

Four Key Pillars

“Our recommendation for a national AI strategy lays out four key responsibilities for government. Within each of these areas we propose actionable steps. We provide some highlights here, and we encourage you to read the full white paper or scan the shorter fact sheet.

Sustainable and funded government AI research and development can help to advance the capabilities of AI in areas such as healthcare, cybersecurity, national security and education, but there need to be clear ethical guidelines.

Create new employment opportunities and protect people’s welfare given that AI has the potential to automate certain work activities.

Liberate and share data responsibly, as the more data that is available, the more “intelligent” an AI system can become. But we need guardrails.

Remove barriers and create a legal and policy environment that supports AI so that the responsible development and use of AI is not inadvertently derailed.”

AI Race Matters

China, the European Union, and the United States have been in the news about strategic plans and policies on the future of AI. The July 2 AI Matters policy blog post was on the U.S. National Artificial Intelligence Research and Development Strategic Plan, released in June, as an update of the report by the Select Committee on Artificial Intelligence of The National Science & Technology Council. The Computing Community Consortium (CCC) recently released the AI Roadmap Website.
Now, a Center for Data Innovation Report compares the current standings of China, the European Union, and the United States and makes policy recommendations. Here is the report summary: “Many nations are racing to achieve a global innovation advantage in artificial intelligence (AI) because they understand that AI is a foundational technology that can boost competitiveness, increase productivity, protect national security, and help solve societal challenges. This report compares China, the European Union, and the United States in terms of their relative standing in the AI economy by examining six categories of metrics—talent, research, development, adoption, data, and hardware. It finds that despite China’s bold AI initiative, the United States still leads in absolute terms. China comes in second, and the European Union lags further behind. This order could change in coming years as China appears to be making more rapid progress than either the United States or the European Union. Nonetheless, when controlling for the size of the labor force in the three regions, the current U.S. lead becomes even larger, while China drops to third place, behind the European Union. This report also offers a range of policy recommendations to help each nation or region improve its AI capabilities.”

About Face

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 Verge reports 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.”

Events and Announcements

AAAI Policy Initiative

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

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

2019 ACM SIGAI Election Reminder

Please remember to vote and to review the information on 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.

AI Research Roadmap

The Computing Community Consortium (CCC) is requesting comments on the draft of A 20-Year Community Roadmap for AI Research in the US. Please submit your comments here by May 28, 2019. See the AI Roadmap Website for more information. 

Here is a link to the whole report and links to individual sections:

     Title Page, Executive Summary, and Table of Contents 

  1. Introduction
  2. Major Societal Drivers for Future Artificial Intelligence Research 
  3. Overview of Core Technical Areas of AI Research Roadmap: Workshop Reports 
    1. Workshop I: A Research Roadmap for Integrated Intelligence 
    2. Workshop II: A Research Roadmap for Meaningful Interaction 
    3. Workshop III: A Research Roadmap for Self-Aware Learning 
  4. Major Findings 
  5. Recommendations
  6. Conclusions

Appendices (participants and contributors)