On July 28, Governance Studies at Brookings will host a webinar examining the potential problems in the 2020 elections. Panelists will discuss the use and impact of social media manipulation, election interference, voting obstacles, and the spread of disinformation.

Viewers can submit questions for speakers by emailing events@brookings.edu or via Twitter @BrookingsGov or with #Election2020.

       

Artificial Intelligence is here, today. How can society make the best use of it?

Until recently, “artificial intelligence” sounded like something out of science fiction. But the technology of artificial intelligence, AI, is becoming increasingly common, from self-driving cars to e-commerce algorithms that seem to know what you want to buy before you do. Throughout the economy and many aspects of daily life, artificial intelligence has become the transformative technology of our time.

Despite its current and potential benefits, AI is little understood by the larger public and widely feared. The rapid growth of artificial intelligence has given rise to concerns that hidden technology will create a dystopian world of increased income inequality, a total lack of privacy, and perhaps a broad threat to humanity itself.

In their compelling and readable book, two experts at Brookings discuss both the opportunities and risks posed by artificial intelligence—and how near-term policy decisions could determine whether the technology leads to utopia or dystopia.

Drawing on in-depth studies of major uses of AI, the authors detail how the technology actually works. They outline a policy and governance blueprint for gaining the benefits of artificial intelligence while minimizing its potential downsides.

The book offers major recommendations for actions that governments, businesses, and individuals can take to promote trustworthy and responsible artificial intelligence. Their recommendations include: creation of ethical principles, strengthening government oversight, defining corporate culpability, establishment of advisory boards at federal agencies, using third-party audits to reduce biases inherent in algorithms, tightening personal privacy requirements, using insurance to mitigate exposure to AI risks, broadening decision-making about AI uses and procedures, penalizing malicious uses of new technologies, and taking pro-active steps to address how artificial intelligence affects the workforce.

Turning Point is essential reading for anyone concerned about how artificial intelligence works and what can be done to ensure its benefits outweigh its harm.

       

On July 27, America will be treated to unprecedented congressional testimony from four of the most powerful technology leaders in the world:  Jeff Bezos of Amazon, Tim Cook of Apple, Sundar Pichai of Google/Alphabet, and Mark Zuckerberg of Facebook. The reason for this extraordinary appearance before the House Judiciary Antitrust Subcommittee is to discuss competition policy and antitrust issues during an era where large companies exercise considerable market power.

But the public event also provides an opportunity to find out what these influential men think about a range of technology issues. As the co-author of a new Brookings Institution Press book with President John R. Allen entitled Turning Point: Policymaking in the Era of Artificial Intelligence, here are questions that would shed considerable light on current technology controversies facing the United States and the world.

• Have you hired ethicists to help you design products and think about the societal ramifications of your technologies? If so, how do you use them to make decisions and if not, why have you not done so?
• Do you believe the California Consumer Privacy Act is a good model for the nation to adopt and would you encourage members of Congress to pass legislation modeled on that bill?
• How have you diversified your corporate leadership team and your company workforce and what are the current breakdowns for your company as a whole by race, gender, ethnicity, and U.S. states where employees work?
• What have you done to stop the use of your products for racist appeals, hateful actions, or false information? Do you believe private technology companies have an obligation to protect democracy, slow the spread of hate speech, and promote fair elections? If so, how do you define hate speech and fair elections? If not, why not?
• What role do you think government regulation should play in the technology sector? What activities should be subject to regulation and which ones should not be? What agency should administer the particular regulation(s)? How much regulation should take place at the national as opposed to the state level?
• According to an Edelman Trust Barometer, 66 percent of people around the world “worry technology will make it impossible to know if what people are seeing or hearing is real.” Do you have any responsibility for assuaging these fears and what concrete steps are you taking to reduce those fears?
• Do you believe technology companies have a responsibility to combat bias and enforce anti-discrimination laws on their digital platforms?
• How much legal liability do you think technology companies should have for what happens on their platforms?
• Much of our country’s Gross Domestic Product is created on the East Coast, West Coast, and a few metropolitan areas in between. Will you commit to establishing more offices or warehouses and hiring more fulltime employees who work in states where technology-sector jobs have not been as common?
• Do you think artificial intelligence runs the risk of expanding out of control and threatening the human species?
• Do you believe Congress should bring back the Office of Technology Assessment, which was abolished in 1995 just as the digital era was starting? If so, how would you define its mission and what kind of work would you like to see it undertake in its advice to members of Congress? If not, why not?
• Would you currently ride in a fully autonomous vehicle that was operating at 60 miles per hour on an interstate highway? Would you ride in a fully autonomous vehicle operating at the speed limit in the crowded streets of a major downtown area?
• Do you believe law enforcement and immigration officials should use facial recognition technology to identify suspects and prosecute criminals? How long do you think images stored for purposes of facial recognition should be held by law enforcement?
• Do you think the U.S. internet should be governed by the net neutrality principles of anti-discrimination, a prohibition against paid prioritization, and no blocking or slowdown of online traffic?
• How do you protect the integrity of your personal online identity and what steps do you advise other Americans to undertake in order to safeguard their online privacy and security?
• How do you think the United States should handle relations with China in regard to intellectual property, cybersecurity, and human rights?
• How do you think the United States should handle relations with Russia in regard to intellectual property, cybersecurity, and human rights?
• Thirty years from now, what do you think will be the dominant news headlines about digital technology? What do you want those headlines to say?
• What is your greatest fear about technology today?
• What is your greatest hope about technology today?
• What is the one digital product or service you wish had never been created and why that product?

Facebook, Amazon, Google, and Apple are general, unrestricted donors to the Brookings Institution. The findings, interpretations, and conclusions posted in this piece are solely those of the author and not influenced by any donation.

       

Algorithms:

According to author Pedro Domingos, algorithms are “a sequence of instructions telling a computer what to do.”[1] These software-based coding rules started with simple and routine tasks, but now have advanced into more complex formulations, such as providing driving instructions for autonomous vehicles, identifying possible malignancies in X-rays and CT scans, and assigning students to public schools. Algorithms are widely used in finance, retail, communications, national defense, and many other areas.

Artificial Intelligence (AI):

Indian engineers Shukla Shubhendu and Jaiswal Vijay define AI as “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.”[2] This definition emphasizes several qualities that separate AI from mechanical devices or traditional computer software, specifically intentionality, intelligence, and adaptability. AI-based computer systems can learn from data, text, or images and make intentional and intelligent decisions based on that analysis.

Augmented Reality (AR):

Augmented reality puts people in realistic situations that are augmented by computer-generated video, audio, or sensory information. This kind of system allows people to interact with actual and artificial features, be monitored for their reactions, or be trained on the best ways to deal with various stimuli.

Big Data:

Extremely large data sets that are statistically analyzed to gain detailed insights. The data can involve billions of records and require substantial computer-processing power. Data sets are sometimes linked together to see how patterns in one domain affect other areas. Data can be structured into fixed fields or unstructured as free-flowing information. The analysis of big data sets can reveal patterns, trends, or underlying relationships that were not previously apparent to researchers.

Chatbots:

Automated tools for answering human questions. Chatbots are being used in retail, finance, government agencies, nonprofits, and other organizations to respond to frequently asked questions or routine inquiries.

Cloud Computing:

Data storage and processing used to take place on personal computers or local servers controlled by individual users. In recent years, however, storage and processing have migrated to digital servers hosted at data centers operated by internet platforms, and people can store information and process data without being in close proximity to the data center. Cloud computing offers convenience, reliability, and the ability to scale applications quickly.

Computer Vision (CV):

Computers that develop knowledge based on digital pictures or videos.[3] For example, cameras in automated retail outlets that are connected to CV systems can observe what products shoppers picked up, identify the specific items and their prices, and charge consumers’ credit card or mobile payment system without involving a cash register or sales clerk. CV also is being deployed to analyze satellite images, human faces, and video imagery.

Connected Vehicles:

Cars, trucks, and buses that communicate directly with one another and with highway infrastructure. This capacity speeds navigation, raises human safety, and takes advantage of the experiences of other vehicles on the road to improve the driving experience.

Data Analytics:

The analysis of data to gather substantive insights. Researchers use statistical techniques to find trends or patterns in the data, which give them a better understanding of a range of different topics. Data analytic approaches are used in many businesses and organizations to track day-to-day activities and improve operational efficiency.

Data Mining:

Techniques that analyze large amounts of information to gain insights, spot trends, or uncover substantive patterns. These approaches are used to help businesses and organizations improve their processes or identify associations that shed light on relevant questions.

Deepfakes:

Digital images and audio that are artificially altered or manipulated by AI and/or deep learning to make someone do or say something he or she did not actually do or say. Pictures or videos can be edited to put someone in a compromising position or to have someone make a controversial statement, even though the person did not actually do or say what is shown. Increasingly, it is becoming difficult to distinguish artificially manufactured material from actual videos and images.

Deep Learning:

A subset of machine learning that relies on neural networks with many layers of neurons. In so doing, deep learning employs statistics to spot underlying trends or data patterns and applies that knowledge to other layers of analysis. Some have labeled this as a way to “learn by example” and a technique that “perform[s] classification tasks directly from images, text, or sound” and then applies that knowledge independently.[4] Deep learning requires extensive computing power and labeled data, and is used in medical research, automated vehicles, electronics, and manufacturing, among other areas.

Digital Sovereigns:

The speed, scope, and timing of technology innovation today is often decided not by government officials but by coders, software designers, and corporate executives. Digital sovereigns set the rules of the road and terms of service for consumers. What they decide, directly or indirectly, has far-reaching consequences for those using their software or platform. The power of business decisionmakers raises important governance questions regarding who should decide on matters affecting society as a whole and the role that policymakers, consumers, and ethicists should play in digital innovation.

Distributed Collaboration:

Connecting frontline people with others who have differing skills and getting them to work together to solve problems. Distributed collaboration differs from current governance paradigms that emphasize hierarchical, top-down decisionmaking by those who do not always have relevant knowledge about the issues being addressed. The new model takes advantage of the fact that a range of skills are needed to resolve technology issues, and those skills are located in different subject areas and organizational parts. Rather than keeping AI expertise in isolation, distributed collaboration brings together software and product designers, engineers, ethicists, social scientists, and policymakers to draw on their respective expertise and integrate their knowledge to solve pressing problems.

Dual-Use Technologies:

Many technologies can be used in a good or ill manner. The very same facial recognition system could be used to find missing children or provide a means for mass surveillance. It is not the technology per se that raises ethical issues but how the technology is put to use. The dual-use nature of technologies makes regulation difficult because it raises the question of how to gain the benefits of technology innovation while avoiding its detrimental features.

Facial Recognition (FR):

A technology for identifying specific people based on pictures or videos. It operates by analyzing features such as the structure of the face, the distance between the eyes, and the angles between a person’s eyes, nose, and mouth. It is controversial because of worries about privacy invasion, malicious applications, or abuse by government or corporate entities. In addition, there have been well-documented biases by race and gender with some facial recognition algorithms.

5G Networks:

These are fifth-generation wireless telecommunications networks that have been deployed in major cities and feature faster speeds and enhanced capabilities for transmitting data and images. As such, 5G networks enable new digital products and services, such as video streaming, autonomous vehicles, and automated factories and homes that require a fast broadband.

Hyperwar:

High-tech military situations in which robots, sensors, AI, and autonomous systems play important roles and command decisions have to unfold at speeds heretofore unseen in warfare. Because of the acceleration of the pace and scope of conflict, countries will have to conduct simultaneous operations in every warfare domain and national leaders will need to accelerate technology innovation to build a safe and stable future.[5]

Machine Learning (ML):

According to Dorian Pyle and Cristina San Jose of the McKinsey Quarterly, machine learning is “based on algorithms that can learn from data without relying on rules-based programming.”[6] ML represents a way to classify data, pictures, text, or objects without detailed instruction and to learn in the process so that new pictures or objects can be accurately identified based on that learned information. ML furthermore can be used to estimate continuous variables (such as estimating home sales prices) or to play games. Many of its insights come by examining prior data and learning how to improve understanding.

Natural Language Processing (NLP):

The analysis of textual information to make sense of its meaning and intentions. NLP software can take a large amount of text and see how words are linked together to assess positive or negative sentiment, relationships, associations, and meaning. For example, researchers can study medical records to see which patient symptoms appear to be most related to particular illnesses.

Neural Networks:

Researchers use computer software to “perform some task by analyzing training examples” and by grouping data based on common similarities.[7] Similar to the neural nodes of a brain, neural networks learn in layers and build complex concepts out of simpler ones. They break up tasks, identify objects at a number of different levels, and apply that knowledge to other activities. These kinds of systems allow computers to learn and adapt to changing circumstances, similar to the way a brain functions. Deep learning and many of the most prominent recent applications of machine learning operate through neural networks (e.g., driverless cars, deepfakes, and AlphaGo game playing).

Quantum Computing:

Quantum computers have tremendous capacity for storing and processing information because their storage processes are not in the form of a zero or one, as is the case with traditional computers. Rather, they take advantage of superposition—the fact that electrons can be in two places at once—to create “quantum bits” that store multiple values in each point.^[8] That capability dramatically increases storage capacity and decreases processing times, thereby improving the scope of data, textual, or image analysis.

Singularity:

Futurist Ray Kurzweil describes a singularity as a “machine-based superintelligence [that is] greater than human intelligence.”[9] It combines advanced computing power with artificial intelligence, machine learning, and data analytics to create super-powered entities. There are extensive (and unresolved) debates regarding whether humanity will face a computing singularity in the next 50, 100, or 250 years.

Social Credit Systems:

The ubiquity of people’s online activities enables technology that tracks behavior and rates people based on their online actions. As an illustration, some organizations have piloted systems that compile data on social media activities, personal infractions, and behaviors such as paying taxes on time. They use that data to rate people for creditworthiness, travel, school enrollment, and government positions.^[10] These systems are problematic from an ethical standpoint because they lack transparency and can be used to penalize political opponents.

Supervised Learning:

According to Science magazine, supervised learning is “a type of machine learning in which the algorithm compares its outputs with the correct outputs during training. In unsupervised learning, the algorithm merely looks for patterns in a set of data.”[11] Supervised learning allows ML and AI to improve information processing and become more accurate.

Techlash:

The backlash against emerging technologies that has developed among many individuals. People worry about a host of problems related to technology innovation, such as privacy invasions, mass surveillance, widening income inequality, and possible job losses. Figuring out how to assuage understandable human fears is a major societal challenge going forward.

Virtual Reality (VR):

Virtual reality uses headsets equipped with projection visors to put people in realistic-seeming situations that are completely generated by computers. People can see, hear, and experience many types of environments and interact with them. By simulating actual settings, VR can train people how to deal with various situations, vary the features that are observed, and monitor how people respond to differing stimuli.

[1] Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (New York: Basic Books, 2018).

[2] Shukla Shubhendu and Jaiswal Vijay, “Applicability of Artificial Intelligence in Different Fields of Life,” International Journal of Scientific Engineering and Research, vol. 1, no. 1 (September 2013), pp. 28–35.

[3] Jason Brownlee, “A Gentle Introduction to Computer Vision,” Machine Learning Mastery, July 5, 2019.

[4] Math Works, “What Is Deep Learning?” undated.

[5] John R. Allen and Amir Husain, “Hyperwar and Shifts in Global Power in the AI Century,” in Amir Husain and others, Hyperwar: Conflict and Competition in the AI Century (Austin, TX: SparkCognition Press, 2018), p. 15.

[6] Dorian Pyle and Cristina San Jose, “An Executive’s Guide to Machine Learning,” McKinsey Quarterly, June, 2015.

[7] Larry Hardesty, “Explained:  Neural Networks,” MIT News, April 14, 2017.

[8] Cade Metz, “In Quantum Computing Race, Yale Professors Battle Tech Giants,” New York Times, November 14, 2017, p. B3.

[9] Quoted in Tom Wheeler, From Gutenberg to Google: The History of Our Future (Brookings, 2019), p. 226. Also see Ray Kurzweil, The Singularity Is Near: Where Humans Transcend Biology (London: Penguin Books, 2006).

[10] Jack Karsten and Darrell M. West, “China’s Social Credit System Spreads to More Daily Transactions,” TechTank (blog), Brookings, June 18, 2018.

[11] Matthew Hutson, “AI Glossary:  Artificial Intelligence, in So Many Words,” Science, July 7, 2017.

       

On July 9, Governance Studies at Brookings hosted a webinar on the current manufacturing landscape and what lies ahead. Panelists examined ways to revitalize and renew growth amid the health and economic crisis.

The event marks the ninth annual John Hazen White Forum on Public Policy, which convenes leaders from academia, business, and government to discuss and identify solutions to the most pressing challenges facing the United States’ manufacturing industry.

Viewers can submit questions for speakers by emailing events@brookings.edu or via Twitter at @BrookingsGov by using #USmfg.

       

I am pleased to announce that Nicol Turner Lee has been named the new director of the Center for Technology Innovation at the Brookings Institution, effective July 1, 2020. A specialist in equitable access to digital technology, she has undertaken research on telemedicine, broadband infrastructure, digital education, and racial bias in AI, among other topics. She is currently finishing a book on ways to improve digital access for all Americans.

Turner Lee is an innovative leader who has undertaken high-quality research and offered cutting-edge solutions to a number of technology-related problems. She is a high-energy person who brings a lot of passion to what often is a dry and technical field. In addition, she is incredibly creative in thinking about the future of technology and envisioning a world where all can gain the benefits of its current and emerging uses.

“An essential part of Brookings’s mission to develop innovative policy solutions for the most pressing challenges of the 21st century includes addressing the social, political, and economic impacts of rapidly evolving technology,” said Brookings President John R. Allen. “Nicol’s extensive work in advancing digital equity and inclusion for historically disadvantaged populations exemplifies her commitment in this space. Nicol is a true leader and I look forward to seeing her continue to drive real impact as director of the Center for Technology Innovation.”

I will continue to serve as a senior fellow in the Center for Technology Innovation, the Douglas Dillon Chair in Governance Studies, and vice president of the Governance Studies program at Brookings. The Center was founded in 2010 and addresses the policy, legal, regulatory, and ethical aspects of emerging technologies.

Turner Lee is excited to take on this new challenge. She said “technology is continuing to drive major facets of society and the policies that govern innovation matter. I am excited about this role to elevate the research of our scholars and serve as a resource to policymakers in search of guidance around legislative, regulatory, and civil society issues.”

Turner Lee graduated magna cum laude from Colgate University and earned her M.A. and Ph.D. in sociology from Northwestern University. She has been cited in numerous newspaper articles and has appeared on a variety of television outlets. She is a widely sought-after speaker on technology and telecommunications issues in the United States and abroad. She is also an appointee on the Federal Communications Commission’s Advisory Committee on Diversity and Digital Empowerment. She chairs TPRC, one of the nation’s oldest convening of technology experts from academia, government, and industry. She has worked at Brookings as a fellow since 2016 and this year was promoted to senior fellow.

       

As a co-chair of the task force, Brookings Institution President John R. Allen worked with leaders from the Jewish, Muslim, Sikh, and Christian faith communities to issue a new report that offers recommendations for how law enforcement and communities of faith can work together to prevent and mitigate mass casualty attacks.

On February 27, Governance Studies at Brookings hosted an event to examine the rising threat of targeted violence against communities of faith. Panelists discussed the findings of the task force’s report, including what is propelling the rise in violent extremist movements in the United States and how policymakers can help protect the rights and safety of vulnerable communities.

After the panel, speakers answered questions from the audience.

       

Why are Americans so angry with each other?

The United States is caught in a partisan hyperconflict that divides politicians, communities—and even families. Politicians from the president to state and local office-holders play to strongly-held beliefs and sometimes even pour fuel on the resulting inferno. This polarization has become so intense that many people no longer trust anyone from a differing perspective.

Drawing on his personal story of growing up as a fundamentalist Christian on a dairy farm in rural Ohio, then as an academic in the heart of the liberal East Coast establishment, Darrell West analyzes the economic, cultural, and political aspects of polarization. He takes advantage of his experiences inside both conservative and liberal camps to explain the views of each side and offer insights into why each is angry with the other.

West argues that societal tensions have metastasized into a dangerous tribalism that seriously threatens U.S. democracy. Unless people can bridge these divisions and forge a new path forward, it will be impossible to work together, maintain a functioning democracy, and solve the country’s pressing policy problems.