They see AI as a way to deal with large volumes of data and figure out efficient ways of responding to public requests. Rather than address service issues in an ad hoc manner, authorities are trying to be proactive in how they provide urban services.
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Cincinnati is not alone. A number of metropolitan areas are adopting smart city applications that use AI to improve service delivery, environmental planning, resource management, energy utilization, and crime prevention, among other things.
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Seattle, for example, has embraced sustainability and is using AI to manage energy usage and resource management. Through these and other means, metropolitan areas are leading the country in the deployment of AI solutions. Indeed, according to a National League of Cities report, 66 percent of American cities are investing in smart city technology.
These examples from a variety of sectors demonstrate how AI is transforming many walks of human existence. The increasing penetration of AI and autonomous devices into many aspects of life is altering basic operations and decisionmaking within organizations, and improving efficiency and response times. At the same time, though, these developments raise important policy, regulatory, and ethical issues. For example, how should we promote data access? How do we guard against biased or unfair data used in algorithms? What types of ethical principles are introduced through software programming, and how transparent should designers be about their choices?
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What about questions of legal liability in cases where algorithms cause harm? The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. In this regard, the United States has a substantial advantage over China. Global ratings on data openness show that U. But right now, the United States does not have a coherent national data strategy.
There are few protocols for promoting research access or platforms that make it possible to gain new insights from proprietary data. It is not always clear who owns data or how much belongs in the public sphere. These uncertainties limit the innovation economy and act as a drag on academic research. In the following section, we outline ways to improve data access for researchers. In some instances, certain AI systems are thought to have enabled discriminatory or biased practices.
Racial issues also come up with facial recognition software. Many historical data sets reflect traditional values, which may or may not represent the preferences wanted in a current system. As Buolamwini notes, such an approach risks repeating inequities of the past:. Even admissions decisions are increasingly automated—what school our children go to and what opportunities they have.
Algorithms embed ethical considerations and value choices into program decisions.
As such, these systems raise questions concerning the criteria used in automated decisionmaking. Some people want to have a better understanding of how algorithms function and what choices are being made. In the United States, many urban schools use algorithms for enrollment decisions based on a variety of considerations, such as parent preferences, neighborhood qualities, income level, and demographic background. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers.
There are questions concerning the legal liability of AI systems. If there are harms or infractions or fatalities in the case of driverless cars , the operators of the algorithm likely will fall under product liability rules. Those can range from civil fines to imprisonment for major harms. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing.
It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer. Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved. In non-transportation areas, digital platforms often have limited liability for what happens on their sites.
In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.
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The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design.
AI requires data to test and improve its learning capacity. In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information. There is a variety of ways researchers could gain data access. One is through voluntary agreements with companies holding proprietary data.
Facebook, for example, recently announced a partnership with Stanford economist Raj Chetty to use its social media data to explore inequality. In the U. Google long has made available search results in aggregated form for researchers and the general public.
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Twitter makes much of its tweets available to researchers through application programming interfaces, commonly referred to as APIs. These tools help people outside the company build application software and make use of data from its social media platform.
They can study patterns of social media communications and see how people are commenting on or reacting to current events. In some sectors where there is a discernible public benefit, governments can facilitate collaboration by building infrastructure that shares data. For example, the National Cancer Institute has pioneered a data-sharing protocol where certified researchers can query health data it has using de-identified information drawn from clinical data, claims information, and drug therapies.
That enables researchers to evaluate efficacy and effectiveness, and make recommendations regarding the best medical approaches, without compromising the privacy of individual patients. There could be public-private data partnerships that combine government and business data sets to improve system performance. For example, cities could integrate information from ride-sharing services with its own material on social service locations, bus lines, mass transit, and highway congestion to improve transportation.
That would help metropolitan areas deal with traffic tie-ups and assist in highway and mass transit planning. Some combination of these approaches would improve data access for researchers, the government, and the business community, without impinging on personal privacy. The federal government has access to vast sources of information.
Opening access to that data will help us get insights that will transform the U. That shortfall is noteworthy because the economic payoffs of AI are substantial. In order to boost economic development and social innovation, federal officials need to increase investment in artificial intelligence and data analytics.
Higher investment is likely to pay for itself many times over in economic and social benefits. As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers.
These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development. For these reasons, both state and federal governments have been investing in AI human capital. For example, in , the National Science Foundation funded over 6, graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education. But there also needs to be substantial changes in the process of learning itself.
It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom. Federal officials need to think about how they deal with artificial intelligence.
As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence.
Among the specific questions the committee is asked to address include the following: competitiveness, workforce impact, education, ethics training, data sharing, international cooperation, accountability, machine learning bias, rural impact, government efficiency, investment climate, job impact, bias, and consumer impact. The committee is directed to submit a report to Congress and the administration days after enactment regarding any legislative or administrative action needed on AI. This legislation is a step in the right direction, although the field is moving so rapidly that we would recommend shortening the reporting timeline from days to days.