Data for AI: Interview with Eric Daimler

I recently spoke with Dr. Eric Daimler about how we can build on the framework he and his colleagues established during his tenure as a contributor to issues of AI policy in the White House during the Obama administration. Eric is the CEO of the MIT-spinout Conexus.com and holds a PhD in Computer Science from Carnegie Mellon University. Here are the interesting results of my interview with him. His ideas are important as part of the basis for ACM SIGAI Public Policy recommendations.

LRM: What are the main ways we should be addressing this issue of data for AI? 

EAD: To me there is one big re-framing from which we can approach this collection of issues, prioritizing data interoperability within a larger frame of AI as a total system. In the strict definition of AI, it is a learning algorithm. Most people know of subsets such as Machine Learning and subsets of that called Deep Learning. That doesn’t help the 99% who are not AI researchers. When I have spoken to non-researchers or even researchers who want to better appreciate the sensibilities of those needing to adopt their technology, I think of AI as the interactions that it has. There is the collection of the data, the transportation of the data, the analysis or planning (the traditional domain in which the definition most strictly fits), and the acting on the conclusions. That sense, plan, act framework works pretty well for most people.

LRM: Before you explain just how we can do that, can you go ahead and define some of your important terms for our readers?

EAD: AI is often described as the economic engine of the future. But to realize that growth, we must think beyond AI to the whole system of data, and the rules and context that surround it: our data infrastructure (DI). Our DI supports not only our AI technology, but also our technical leadership more generally; it underpins COVID reporting, airline ticket bookings, social networking, and most if not all activity on the internet. From the unsuccessful launch of healthcare.gov, to the recent failure of Haven, to the months-long hack into hundreds of government databases, we have seen the consequences faulty DI can have. More data does not lead to better outcomes; improved DI does. 

Fortunately, we have the technology and foresight to prevent future disasters, if we act now. Because AI is fundamentally limited by the data that feeds it, to win the AI race, we must build the best DI. The new presidential administration can play a helpful role here, by defining standards and funding research into data technologies. Attention to the need for better DI will speed responsiveness to future crises (consider COVID data delays) and establish global technology leadership via standards and commerce. Investing in more robust DI will ensure that anomalies, like ones that would have helped us identify the Russia hack much sooner, will be evident, so we can prevent future malfeasance by foreign actors. The US needs to build better data infrastructure to remain competitive in AI.

LRM: So how might we go about prioritizing data interoperability?

EAD: In 2016, the Department of Commerce (DOC) discovered that on average, it took six months to onboard new suppliers to a midsize trucking company—because of issues with data interoperability. The entire American economy would benefit from encouraging more companies to establish semantic standards, internally and between companies, so that data can speak to other data. According to a DOC report in early 2020, the technology now exists for mismatched data to communicate more easily and data integrity to be guaranteed, thanks to a new area of math called Applied Category Theory (ACT). This should be made widely available.

LRM: And what about enforcing data provenance? 

EAD: As data is transformed across platforms—including trendy cloud migrations—its lineage often gets lost. A decision denying your small business loan can and should be traceable back to the precise data the loan officer had at that time. There are traceability laws on the books, but they have been rarely enforced because up until now, the technology hasn’t been available to comply. That’s no longer an excuse. The fidelity of data and the models on top of them should be proven—down to the level of math—to have maintained integrity.

LRM: Speaking more generally, how can we start to lay the groundwork to reap the benefits of these advancements in data infrastructure? 

EAD: We need to formalize. When we built 20th century assembly lines, we established in advance where and how screws would be made; we did not ask the village blacksmith to fashion custom screws for every home repair. With AI, once we know what we want to have automated (and there are good reasons to not to automate everything!), we should then define in advance how we want it to behave. As you read this, 18 million programmers are already formalizing rules across every aspect of technology. As an automated car approaches a crosswalk, should it slow down every time, or only if it senses a pedestrian? Questions like this one—across the whole economy—are best answered in a uniform way across manufacturers, based on standardized, formal, and socially accepted definitions of risk.

LRM: In previous posts, I have discussed roles and responsibilities for change in the use of AI. Government regulation is of course important, but what roles do you see for AI tech companies, professional societies, and other entities in making the changes you recommend for DI and other aspects of data for AI?

What is different this time is the abruptness of change. When automation technologies work, they can be wildly disruptive. Sometimes this is very healthy (see: Schumpeter). I find that the “go fast and…” framework has its place, but in AI it can be destructive and invite resistance. That is what we have to watch out for. Only with responsible coordinated action do we encourage adoption of these fantastic and magical technologies. Automation in software can be powerful. These processes need not be linked into sequences just because they can. That is, just because some system can be automated does not mean that it should. Too often there is absolutism in AI deployments when what is called for in these discussions is nuance and context. For example, in digital advertising my concerns are around privacy, not physical safety. When I am subject to a plane’s autopilot, my priorities are reversed.

With my work in the US Federal Government, my bias remains against regulation as a first-step. Shortly after my time with the Obama Whitehouse, I am grateful to have participated with a diverse group for a couple of days at the Halcyon House in Washington D.C. We created some principles for deploying AI to maximize adoption. We can build on these and rally around a sort of LEED-like standard for AI deployment.

Dr. Eric Daimler is CEO & Founder of Conexus and Board Member of Petuum and WelWaze. He was a Presidential Innovation Fellow, Artificial Intelligence and Robotics. Eric is a leading authority in robotics and artificial intelligence with over 20 years of experience as an entrepreneur, investor, technologist, and policymaker.  Eric served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics. Eric has incubated, built and led several technology companies recognized as pioneers in their fields ranging from software systems to statistical arbitrage. Currently, he serves on the boards of WelWaze and Petuum, the largest AI investment by Softbank’s Vision Fund. His newest venture, Conexus, is a groundbreaking solution for what is perhaps today’s biggest information technology problem — data deluge. Eric’s extensive career across business, academics and policy gives him a rare perspective on the next generation of AI.  Eric believes information technology can dramatically improve our world.  However, it demands our engagement. Neither a utopia nor dystopia is inevitable. What matters is how we shape and react to, its development. As a successful entrepreneur, Eric is looking towards the next generation of AI as a system that creates a multi-tiered platform for fueling the development and adoption of emerging technology for industries that have traditionally been slow to adapt.  As founder and CEO of Conexus, Eric is leading CQL a patent-pending platform founded upon category theory — a revolution in mathematics — to help companies manage the overwhelming challenge of data integration and migration. A frequent speaker, lecturer, and commentator, Eric works to empower communities and citizens to leverage robotics and AI to build a more sustainable, secure, and prosperous future. His academic research has been at the intersection of AI, Computational Linguistics, and Network Science (Graph Theory). His work has expanded to include economics and public policy. He served as Assistant Professor and Assistant Dean at Carnegie Mellon’s School of Computer Science where he founded the university’s Entrepreneurial Management program and helped to launch Carnegie Mellon’s Silicon Valley Campus.  He has studied at the University of Washington-Seattle, Stanford University, and Carnegie Mellon University, where he earned his Ph.D. in Computer Science.

Face Recognition and Bad Science

FR and Bad Science: Should some research not be done?

Facial recognition issues continue to appear in the news, as well as in scholarly journal articles, while FR systems are being banned and some research is shown to be bad science. AI system researchers who try to associate facial technology output with human characteristics are sometimes referred to as machine-assisted phrenologists. Problems with FR research have been demonstrated in machine learning research such as work by Steed and Caliskan in “A set of distinct facial traits learned by machines is not predictive of appearance bias in the wild.”  Meanwhile many examples of harmful products and misuses have been identified in areas such as criminality, video interviewing, and many others. Some communities have considered bans on FR products.

Yet, journals and conferences continue to publish bad science in facial recognition.

Some people say the choice of research topics is up to the researchers – the public can choose not to use the products of their research. However, areas such as genetic, biomedical, and cyber security R&D do have limits. Our professional computing societies can choose to disapprove research areas that cause harm. Sources of mitigating and preventing irresponsible research being introduced into the public space include:
– Peer pressure on academic and corporate research and development
– Public policy through laws and regulations
– Corporate and academic self-interest – organizations’ bottom lines can
suffer from bad publicity
– Vigilance by journals about publishing papers that promulgate the misuse
of FR

A recent article by Matthew Hutson in The New Yorker discusses “Who should stop unethical AI.” He remarks that “Many kinds of researchers—biologists, psychologists, anthropologists, and so on—encounter checkpoints at which they are asked about the ethics of their research. This doesn’t happen as much in computer science. Funding agencies might inquire about a project’s potential applications, but not its risks. University research that involves human subjects is typically scrutinized by an I.R.B., but most computer science doesn’t rely on people in the same way. In any case, the Department of Health and Human Services explicitly asks I.R.B.s not to evaluate the “possible long-range effects of applying knowledge gained in the research,” lest approval processes get bogged down in political debate. At journals, peer reviewers are expected to look out for methodological issues, such as plagiarism and conflicts of interest; they haven’t traditionally been called upon to consider how a new invention might rend the social fabric.”

OSTP News

OSTP Launches National AI Initiative Office

The White House Office of Science and Technology Policy announced the establishment of the National Artificial Intelligence Initiative Office.  As outlined in legislation, this Office will serve as the point of contact on Federal AI activities across the interagency, as well as with private sector, academia, and other stakeholders. The Select Committee on Artificial Intelligence will oversee the National AI Initiative Office, and Dr. Lynne E. Parker, Deputy United States Chief Technology Officer, will serve as the Founding Director. As explained in Inside Tech Media, the newly enacted National Defense Authorization Act contains important provisions regarding the development and deployment of AI technologies, many of which build upon previous legislation introduced in the 116th Congress, including the establishment of the National AI Initiative Office.

White House Science Team

On January 15, key members of President-Elect Biden’s were announced. The press release says “These diverse, deeply experienced scientists and experts will play a key role in shaping America’s future — and will prepare us to lead the world in the 21st century and beyond.” President-elect Joe Biden said, “Science will always be at the forefront of my administration — and these world-renowned scientists will ensure everything we do is grounded in science, facts, and the truth. Their trusted guidance will be essential as we come together to end this pandemic, bring our economy back, and pursue new breakthroughs to improve the quality of life of all Americans.”
He will nominate Dr. Eric Lander (photo) as Director of the OSTP and to serve as the Presidential Science Advisor. “The president-elect is elevating the role of science within the White House, including by designating the Presidential Science Advisor as a member of the Cabinet for the first time in history.”
Other key members are
Alondra Nelson, Ph.D., OSTP Deputy Director for Science and Society (photo)
Frances H. Arnold, Ph.D., Co-Chair of the President’s Council of Advisors on Science and Technology (photo)
Maria Zuber, Ph.D., Co-Chair of the President’s Council of Advisors on Science and Technology (photo)
Francis S. Collins, M.D., Ph.D., Director of the National Institutes of Health (photo)
Kei Koizumi, OSTP Chief of Staff  (photo)
Narda Jones, OSTP Legislative Affairs Director (photo)

Policy-Related Article from AI and Ethics

Stix, C., Maas, M.M. Bridging the gap: the case for an ‘Incompletely Theorized Agreement’ on AI policy. AI Ethics (2021). https://doi.org/10.1007/s43681-020-00037-w

Big Issues

Big Tobacco, Big Oil, Big Banks … and Big Tech

A larger discussion is growing out of the recent news about Timnit Gebru and Google. Big Tech is having a huge impact on individuals and society both for the many products and services we enjoy and for the current and potential cases of detrimental effects of unethical behavior or naiveté regarding AI ethics issues. How do we achieve AI ethics responsibility in all organizations, big and small? And, not just in corporations, but governmental and academic research organizations?

Some concerned people focus on regulation, but for a variety of reasons public and community pressure may be quicker and more acceptable. This includes corporations earning reputations for ethical actions in the design and development of AI products and systems. An article in MIT Technology Review by Karen Hao discusses a letter signed by nine members of Congress that “sends an important signal about how regulators will scrutinize tech giants.” Ideally our Public Policy goal is strong AI Ethics national and global communities that self-regulate on AI ethical issues, comparable to other professional disciplines in medical science and cybersecurity. Our AI Ethics community, as guidelines evolve, could provide a supportive and guiding presence in the implementation of ethical norms in the research and development in AI. The idea of a global community is reflected also in a recent speech by European Union President Ursula von der Leyen at the World Leader for Peace and Security Award ceremony. She advocates for transatlantic agreements on AI.

AI Centre of Excellence (AICE)

AICE conducted an inaugural celebration in December, 2020. Director John Kamara founded the AI Centre of Excellence in Kenya and is passionate about creating value and long term impact of AI and ML in Africa. The Centre aims to accomplish this by providing expert training to create skilled and employable AI and ML engineers. The Centre dives into creating sustainable impact through Research and Development. AI research and products are estimated to contribute over $13 trillion to the global economy by 2030. This offers the Centre an opportunity to carry out research in selected sectors and build products based on the research. The world has around 40K AI experts in the world, with nearly half in the US and less than 5% in Africa. Oxford Insights estimates that Kenya ranks first in Africa, and AICE aims to leverage this potential and transform AICE into a full blown Artificial Intelligence Centre of Excellence. Please keep your eyes on Africa and ways our public policy can assist efforts there to grow AI in emerging education and research.

AI Policy Nuggets II

What Can Biden Do for Science?

A Science|Business Webcast presented a forum of public and private sector leaders discussing ideas about the need for the president-elect to convene world leaders to re-establish ‘rules of engagement’ on science.

Brookings Webinar on the Future of AI

“On November 17, 2020, the Brookings Institution Center for Technology Innovation hosted a webinar to discuss the future of AI, how it is being deployed, and the policy and legal issues being raised. Speakers explored ways to mitigate possible concerns and how to move forward safely, securely, and in a manner consistent with human values.”

Section 230 Update

Politico reports that “Trump for months has urged Congress to revoke industry legal shield Section 230, while its staunchest critics largely pushed to revamp it instead. But the president’s more drastic call for a total repeal — echoed by Biden for very different reasons — is gaining traction among Republicans in Washington. The NYT reported Thursday that White House chief of staff Mark Meadows has even offered Trump’s support for a must-pass annual defense spending bill if it includes such a repeal.”

The European AI Policy Conference

AI may be the most important digital innovation technology transforming industries around the world.
“Businesses in Europe are at the forefront of some of the latest advancements in the field, and European universities are home to the greatest concentration of AI researchers in the world. Every week, new case studies emerge showing the potential opportunities that can arise from greater use of the technology.” The European AI Policy Conference brings together leading voices in AI from to discuss why European success in AI is important, how the EU compares to other world leaders today, and what steps European policymakers should take to be more competitive in AI. “The European AI Policy Conference is a high-level forum to connect stakeholders working to promote AI in Europe, showcase advances in AI, and promote AI policies supporting its development to EU policymakers and thought leaders.”

Policy Issues from AI and Ethics

The inaugural issue of the new journal AI and Ethics contains several articles relevant to AI and Public Policy.

Jelinek, T., Wallach, W. & Kerimi, D. “Policy brief: the creation of a G20 coordinating committee for the governance of artificial intelligence” AI Ethics (2020). https://doi.org/10.1007/s43681-020-00019-y

This policy brief proposes a group of twenty (G20) coordinating committee for the governance of artificial intelligence (CCGAI) to plan and coordinate on a multilateral level the mitigation of AI risks. The G20 is the appropriate regime complex for such a metagovernance mechanism, given the involvement of the largest economies and their highest political representatives.

Gambelin, O. “Brave: what it means to be an AI Ethicist” AI Ethics (2020). https://doi.org/10.1007/s43681-020-00020-5

This piece offers a preliminary definition of what it means to be an AI Ethicist, first examining the concept of an ethicist in the context of artificial intelligence, followed by exploring what responsibilities are added to the role in industry specifically, and ending on the fundamental characteristic that underlies it all: bravery.

Smith, P., Smith, L. “Artificial intelligence and disability: too much promise, yet too little substance?” AI Ethics (2020). https://doi.org/10.1007/s43681-020-00004-5

Much has been written about the potential of artificial intelligence (AI) to support, and even transform, the lives of disabled people. Many individuals are benefiting, but what are the true limits of such tools? What are the ethics of allowing AI tools to suggest different courses of action, or aid in decision-making? And does AI offer too much promise for individuals? We draw as to how AI software and technology might best be developed in the future.

Coeckelbergh, M. “AI for climate: freedom, justice, and other ethical and political challenges” AI Ethics (2020). https://doi.org/10.1007/s43681-020-00007-2

Artificial intelligence can and should help to build a greener, more sustainable world and to deal with climate change, but these opportunities also raise ethical and political issues that need to be addressed. This article discusses these issues, with a focus on problems concerning freedom and justice at a global level, and calls for responsible use of AI for climate in the light of these challenges.

Hickok, M. “Lessons learned from AI ethics principles for future actions” AI Ethics (2020). https://doi.org/10.1007/s43681-020-00008-1

The use of AI systems is significantly more prevalent in recent years, and the concerns on how these systems collect, use and process big data has also increased. To address these concerns and advocate for ethical and responsible AI development and implementation, NGOs, research centers, private companies, and governmental agencies have published more than 100 AI ethics principles and guidelines. Lessons must be learned from the shortcomings of AI ethics principles to ensure that future investments, collaborations, standards, codes, and legislation reflect the diversity of voices and incorporate the experiences of those who are already impacted by AI.

Fall Nuggets

USTPC Panel on Section 230

On November 18 from 5:00 to 6:30 PM EST, experts from ACM’s US Technology Policy Committee (USTPC) will discuss the legal liability of Internet platforms such as Facebook and Twitter under Section 230 of the Communications Decency Act. The USTPC panelists are Andy Grosso (Moderator), Mark Rasch, Pam Samuelson, Richard M. Sherman, and Danny Weitzner.

Biden and Science

Participants in a Science and Business Webcast urged that a global assembly “should press leaders of the big industrial nations to open – or re-open – their research systems, while also ensuring that COVID-19 vaccines are freely available to everyone in the world. An international summit.” About an international summit, Robert-Jan Smits, former director-general of the European Commission’s research and innovation directorate said it, “would really show that senior leaders are turning the page,”

Center for Data Innovation On the EU Data Governance Act

“The European Commission is planning to release its Data Governance Act to facilitate data sharing within the EU. The goal is to increase data sharing among businesses, make more public-sector data available for reuse, and foster data sharing of personal data, including for ‘altruistic’ purposes. While the goals of the act are commendable, many of the specific policies outlined in a draft would create a new data localization requirement, undermine the EU’s commitments to digital free trade, and contradict its open data principles.”

AI Data

Confusion in the popular media about terms such as algorithm and what constitutes AI technology cause critical misunderstandings among the public and policymakers. More importantly, the role of data is often ignored in ethical and operational considerations. Even if AI systems are perfectly built, low quality and biased data cause unintentional and even intentional hazards.

Language Models and Data

A generative pre-trained transformer GPT-3 is currently in the news. For example, James Vincent in the July 30, 2020, article in The Verge writes about GPT-3, which was created by OpenAI. Language models, GPT-3 the current ultimate product, have ethics issues on steroids for products being made. Inputs to the system have all the liabilities discussed about Machine Learning and Artificial Neural Network products. The dangers of bias and mistakes are raised in some writings but are likely not a focus among the wide range of enthusiastic product developers using the open-source GPT-3. Language models suggest output sequences of words given an input sequence. Thus, samples of text from social media can be used to produce new text in the same style as the author and potentially can be used to influence public opinion. Cases have been found of promulgating incorrect grammar and misuse of terms based on poor quality inputs to language models. An article by David Pereira includes examples and comments on the use of GPT-3. The article “GPT-3: an AI Game-Changer or an Environmental Disaster?” by John Naughton gives examples of and commentary on results from GPT-3.

Data Governance

A possible meta solution for policymakers to keep up with technological advances is discussed by Alex Woodie in “AI Ethics and Data Governance: A Virtuous Cycle.”

He quotes James Cotton, who is the international director of the Data Management Centre of Excellence at Information Builders’ Amsterdam office: “as powerful as the AI technology is, it can’t be implemented in an ethical manner if the underlying data is poorly managed and badly governed. It’s critical to understand the relationship between data governance and AI ethics. One is foundational for the other. You can’t preach being ethical or using data in an ethical way if you don’t know what you have, where it came from, how it’s being used, or what it’s being used for.”

USTPC in the News

Overview

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.

Policy and AI Ethics

The Alan Turing Institute Public Policy Programme

Among the complexities of public policy making, the new world of AI and data science requires careful consideration of ethics and safety in addressing complex and far-reaching challenges in the public domain. Data and AI systems lead to opportunities that can produce both good and bad outcomes. Ethical and safe systems require intentional processes and designs for organizations responsible for providing public services and creating public policies. An increasing amount of research focuses on developing comprehensive guidelines and techniques for industry and government groups to make sure they consider the range of issues in AI ethics and safety in their work. An excellent example is the Public Policy Programme at The Alan Turing Institute under the direction of Dr. David Leslie [1]. Their work complements and supplements the Data Ethics Framework [2], which is a practical tool for use in any project initiation phase. Data Ethics and AI Ethics regularly overlap.

The Public Policy Programme describes AI Ethics as “a set of values, principles, and techniques that employ widely accepted standards of right and wrong to guide moral conduct in the development and use of AI technologies. These values, principles, and techniques are intended both to motivate morally acceptable practices and to prescribe the basic duties and obligations necessary to produce ethical, fair, and safe AI applications. The field of AI ethics has largely emerged as a response to the range of individual and societal harms that the misuse, abuse, poor design, or negative unintended consequences of AI systems may cause.”

They cite the following as some of the most consequential potential harms:

  • Bias and Discrimination
  • Denial of Individual Autonomy, Recourse, and Rights
  • Non-transparent, Unexplainable, or Unjustifiable Outcomes
  • Invasions of Privacy
  • Isolation and Disintegration of Social Connection
  • Unreliable, Unsafe, or Poor-Quality Outcomes

The Ethical Platform for the Responsible Delivery of an AI Project, strives to enable the “ethical design and deployment of AI systems using a multidisciplinary team effort. It demands the active cooperation of all team members both in maintaining a deeply ingrained culture of responsibility and in executing a governance architecture that adopts ethically sound practices at every point in the innovation and implementation lifecycle.” The goal is to “unite an in-built culture of responsible innovation with a governance architecture that brings the values and principles of ethical, fair, and safe AI to life.”

[1] Leslie, D. (2019). Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. The Alan Turing Institute. https://doi.org/10.5281/zenodo.3240529

[2] Data Ethics Framework (2018). https://www.gov.uk/government/publications/data-ethics-framework/data-ethics-framework.

Principled Artificial Intelligence

In January, 2020, the Berkman Klein Center released a report by Jessica Fjeld and Adam Nagy “Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI”, which summarizes contents of 36 documents on AI principles.

This work acknowledges the surge in frameworks based on ethical and human rights to guide the development and use of AI technologies.  The authors focus on understanding ethics efforts in terms of eight key thematic trends:  

  • Privacy
  • Accountability
  • Safety & security
  • Transparency & explainability
  • Fairness & non-discrimination
  • Human control of technology
  • Professional responsibility
  • Promotion of human values

They report “our analysis examined the forty-seven individual principles that make up the themes, detailing notable similarities and differences in interpretation found across the documents. In sharing these observations, it is our hope that policymakers, advocates, scholars, and others working to maximize the benefits and minimize the harms of AI will be better positioned to build on existing efforts and to push the fractured, global conversation on the future of AI toward consensus.”

Human-Centered AI

Prof. Ben Shneiderman recently presented his extensive work “Human-Centered AI: Trusted, Reliable & Safe” at the University of Arizona’s NSF Workshop on “Assured Autonomy”.  His research emphasizes human autonomy as opposed to the popular notion of autonomous machines. His Open Access paper quickly drew 3200+ downloads. The ideas are now available in the International Journal of Human–Computer Interaction. The abstract is as follows: “Well-designed technologies that offer high levels of human control and high levels of computer automation can increase human performance, leading to wider adoption. The Human-Centered Artificial Intelligence (HCAI) framework clarifies how to (1) design for high levels of human control and high levels of computer automation so as to increase human performance, (2) understand the situations in which full human control or full computer control are necessary, and (3) avoid the dangers of excessive human control or excessive computer control. The methods of HCAI are more likely to produce designs that are Reliable, Safe & Trustworthy (RST). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.”