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Big data has become the ubiquitous watch word of medical innovation. The rapid development of machine-learning techniques and artificial intelligence, in particular, has promised to revolutionize medical practice from the allocation of resources to…

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Abstract

The HeLa cell line was created in 1951 without consent from Henrietta Lacks, the person whose tissue sample was used. In 2021, the descendants of Henrietta Lacks sued a well-known biotechnology company for the profits it made from the HeLa cell line. In this article, ownership of the cell lines is investigated from a South African legal perspective by considering three possible contemporary scenarios bearing points of similarity to the Henrietta Lacks case. In the first scenario, informed consent is obtained to use a tissue sample for research and to commercialize the products of such research; in the second scenario, informed consent is materially deficient because of an honest mistake on the part of the research institution; and in the third scenario, informed consent is materially deficient due to willful disregard of the law on the part of the research institution. In the first two scenarios, ownership of the cell line created from the tissue sample would vest in the research institution, and the research participant would not have any legal action for financial compensation. However, in the third scenario, ownership of the cell line would vest in the research participant, who would be able to claim all profits made from trading the cell line. Whether the research institution acted in good faith is therefore a crucial determinant of the legal outcome.

Keywords: HeLa, ownership, South Africa, law, informed consent

I. INTRODUCTION

The case of Henrietta Lacks is well known. In 1951, she was diagnosed with cancer and was undergoing treatment at Johns Hopkins Hospital in the United States. A tissue sample retrieved during a biopsy was sent to the research laboratory of cell biologist Dr George Otto Gey, who analyzed retrieved cells from patients. From this sample, Gey's laboratory assistant isolated the cancer cells. These cells proved to be different from all previous cells studied in Gey's laboratory. By applying a technique developed by Gey, these cells could grow and divide indefinitely in culture medium. Using Lacks' cancer cells, Gey developed the world's first immortal human cell line, which he called the 'HeLa' cell line after Henrietta Lacks. Gey distributed the HeLa cell line widely and free of charge. Over the past 70 years, the HeLa cell line was used in countless health-related innovations, ranging from the polio vaccine in the 1950s to studying the human genome and more recently the COVID-19 vaccines. Almost 11,000 patents are involved in the HeLa cell line. The problem, however, is that Henrietta Lacks' consent was never obtained to use the retrieved cells for research purposes. Although the actions that took place were accepted practice 70 years ago, society's values have since changed. As such, the HeLa cell line has become controversial. The history of the HeLa cell line raises several ethical and legal issues. One central issue is the ownership of the HeLa cell line. This issue is sure to be canvassed in the lawsuit filed at the end of 2021 by the estate of Henrietta Lacks against Thermo Fisher Scientific; it is likely that all profits made by the biotech company from commercializing the HeLa cell line will be claimed.

In this essay, I investigate the ownership of the cell lines through the lens of South African law. I pose the hypothetical question: if a case similar to that of Henrietta Lacks occurred in present-day South Africa, who would own the HeLa cell line? As a secondary question, I consider possible private law remedies that a present-day South African Henrietta Lacks may have at her disposal. Given the differences between present-day South Africa and 1950s America, the exact facts of the Henrietta Lacks case cannot simply be superimposed on present-day South Africa. This is illustrated by the following observations: contrary to the Henrietta Lacks case, consent for using her human biological material (HBM) in research is a legal requirement in present-day South Africa. Also, contrary to Gey who presumably believed-correctly so-that he did not break the law in any way, it would not be plausible for present-day South African researchers to believe that no consent is required. And, as I show in my analysis below, a researcher's honesty regarding the consent requirement is a crucial consideration in the law. Accordingly, I present three possible scenarios regarding research participant consent-or the lack thereof-in current South African law. These three scenarios are: (i) legal compliance, (ii) legal non-compliance because of an honest mistake, and (iii) willful legal non-compliance by research institutions with informed consent legal requirements. The reader can compare the legal positions of these three scenarios.

Before I can commence with the analysis of the three scenarios, I first need to answer the foundational-and sometimes contested-question of whether HBM in the research context is susceptible of ownership in South African law.

II. CAN HBM BE OWNED?

Two factors contribute to the lack of consensus about the legal status of HBM in the research context. First, the South African courts have not yet had the opportunity to apply common law principles to the issue of ownership of HBM in the research context. Second, South African statutory law in the area of health research is fragmented, sometimes contradictory, and mostly not explicit on the issue. In a recent article, Thaldar and Shozi present the first comprehensive analysis of South African law on the topic and conclude that HBM in the research context is indeed susceptible of ownership. Their analysis is summarized in the following two paragraphs.

Common Law

South Africa's common law-Roman-Dutch law-is a flexible system that develops with the times. The abstract principle in Roman-Dutch law is that anything that is useful and valuable, not part of something else or of a human body, and that is capable of human control, qualifies as a legal object that is susceptible of ownership. Accordingly, although a tissue sample might not have been susceptible of ownership in Roman times (because the Romans did not have much use for it), a tissue sample that is currently used for research is indeed susceptible of ownership because it is (a) useful and valuable in research, (b) removed and hence separated from a human body, and (c) capable of human control. This interpretation is supported by judgments in jurisdictions that share the same principles as found in Roman-Dutch law.

Statute Law

South Africa's statute law necessarily implies ownership of HBM in the research context. The National Health Act (NHA) and its regulations consistently use the term 'donation' to denote a situation where certain kinds of HBM (tissue, gametes, blood, or blood products) or HBM generally are provided by a research participant to a research institution for the purposes of research. 'Donation' is a legal technical term that entails that ownership of the donated object is transferred from the donor to the donee. This means that the research participant is the original owner of HBM originating from his or her body, and that the research participant transfers ownership of the HBM to the research institution to which the HBM is donated. Importantly, the NHA provides (in section 55) that it is unlawful to provide human tissue for research in any way other than the way provided for in the NHA. Accordingly, human tissue samples may not be provided by a research participant for research in any way other than donation- i.e., transfer of ownership is peremptory.

In the next two paragraphs, I address two common arguments against the conclusions reached above. Also, since HBM contain genetic information, I also consider whether South Africa's data protection legislation has any effect on these conclusions.

But if one May Not Trade in HBM, how Can it Be Owned?

The answer to this question has two parts. First, the fact that there are certain restrictions on what owners may lawfully do with their property does not mean that there is no ownership. For example, the fact that one may not trade in cannabis does not mean that one cannot own cannabis. The same applies to HBM. Second, the claim that one may not trade in HBM is not entirely true. The NHA's trade restrictions only apply to the quartet of tissue, blood, blood products, or gametes-not to HBM generally. Nothing in the definition of 'tissue' in the NHA suggests that it includes objects of a new kind that can be derived from tissue, such as cell lines. It is important to note that legal rules that are applicable to one kind of object are not necessarily applicable to the kinds of objects that can be derived from the former kind. For example, a legal rule that televisions must be licensed would be applicable to televisions, but not to a component such as the metal housing or to recycled metal from the television. Similarly, a cell line is a different kind of legal object from tissue, and hence is not included in the trade ban on tissue.

Can Research Participants Retain Ownership?

In 2018, the South African Minister of Health promulgated a material transfer agreement (MTA) to be used whenever biospecimens are transferred for purposes of research or clinical trials within or out of South Africa (SA MTA). In contrast with the statutes discussed above, the SA MTA provides that 'the donor remains the owner' of the HBM. However, this provision has been highlighted as problematic. First, the provision does not make sense, as donation would not be donation without the transfer of ownership. Without the transfer of ownership, the nature of the transaction would be something else, such as lending. Second, the Minister of Health can only make regulations within the framework provided by the NHA; the Minister of Health cannot change the provisions of the NHA. The NHA makes the transfer of ownership peremptory, and any provision to the contrary in the SA MTA is therefore null and void. It should also be noted that the SA MTA itself states that it is only a 'framework'. As such, users of the SA MTA are free to change any of the substantive provisions of the SA MTA as they wish-as long as the main headings are retained in some form. A revised version of the SA MTA that retained the original framework but eliminated this and other problematic issues in the original SA MTA was developed by a group of law academics and is freely available online.

What about POPIA?

South Africa's data protection legislation, the Protection of Personal Information Act 4 of 2013 (POPIA) entered into force on 1 July 2021. POPIA provides for comprehensive informational privacy rights for persons to whom personal information relate-i.e., data subjects. Given that HBM contains genetic information about a person, and genetic information qualifies as personal information, does POPIA not apply to such HBM? The answer is 'no'. The reason is that POPIA only applies to personal information that is 'entered in a record by or for a responsible party' [section 3(1)]. Genetic information contained in HBM naturally occurs in such HBM, rather than being 'entered in' such HBM 'by or for a responsible party'. Accordingly, HBM and the genetic information that it contains fall outside the scope of POPIA.

Now that it has been established that HBM in the research context is susceptible of ownership, that the transfer of ownership by the research participant to the research institution is peremptory, and that HBM and the information contained therein are not regulated by POPIA, I consider the three scenarios.

III. PRESENT-DAY SCENARIOS

Scenario 1-Legal Compliance

The NHA provides (section 55 read with section 71) that tissue samples may only be taken with the written informed consent of the research participant. In the first scenario, it is assumed that all involved act lawfully, and the hypothetical present-day Henrietta Lacks gives informed consent in writing for the removal of a tissue sample. Her informed consent is sufficiently comprehensive to cover research using the sample, and the commercialization of products from such research. In this scenario, it should be clear that ownership of the tissue sample is automatically, through the operation of the NHA, transferred to the research institution. If the research institution isolates the individual cells from the tissue sample and creates a cell line from the tissue sample, the default position would be that the research institution is the owner of the cell line. Since cell lines fall outside the NHA's trade ban, the research institution would be free to commercialize the cell line.

Scenario 2-Legal Non-Compliance Because of an Honest Mistake

In the second scenario, the informed consent provided by the hypothetical present-day Henrietta Lacks is defective in a material way because of an honest mistake on the part of the research institution. For example, if the research institution mistakenly makes an incorrect representation to present-day Henrietta Lacks regarding a material aspect of the transaction, there would not be true consent, and the donation would be unlawful and hence void. An example of this would be if the research institution represented to present-day Henrietta Lacks that she would retain ownership of her donated tissue sample, while in fact (because of the restriction imposed by the NHA) it is a legal impossibility. Ironically, the result would be that this false representation would become true: since the donation is void, present-day Henrietta Lacks would remain the owner of the tissue sample. Assuming that the research institution makes the incorrect representation because it sincerely believes it to be legally possible, the research institution would become the bona fide possessor of the tissue sample. This is important, as the analysis below will show.

The next part of the analysis is to consider the creation of the cell line from the tissue sample from a property law perspective. Importantly, a cell line is a new kind of object that is created from the tissue sample. South African law provides a solution to this situation with the Roman law concept specificatio. This entails that where a person who is in good faith possession of an object but not the owner thereof, makes an object of a new kind out of the original object, and without an agreement between the maker and the owner, the maker acquires ownership in the new object. Classic examples are wine from another's grapes, a statue from another's gold, and clothes from another's wool. Accordingly, in this scenario too, the research institution would become the owner of the cell line.

Would the present-day Henrietta Lacks have any remedy to financially compensate her for the loss of ownership? In South Africa's common law, Roman-Dutch law, a person who lost ownership due to the operation of specificatio, would in principle be able to rely on an unjustified enrichment action to be compensated. For an unjustified enrichment action to be successful, the present-day Henrietta Lacks would need to prove, inter alia, that she was impoverished by the operation of specificatio. However, since the NHA places tissue outside the realm of trade, present-day Henrietta Lacks' tissue sample had no patrimonial value, and its loss therefore had zero effect on her assets. As such, the present scenario would likely not be actionable by a present-day Henrietta Lacks.

Scenario 3-Willful Legal Non-compliance

In the last scenario, the informed consent provided by the hypothetical present-day Henrietta Lacks is materially defective because the research institution acts in willful defiance of the law. This would be the case if, rather than mistakenly making an incorrect representation to the present-day Henrietta Lacks, the research institution makes the same representation knowing it to be false. Accordingly, although the research institution would be in possession of the tissue sample, the donation agreement would be unlawful and hence void, and ownership of the sample would not transfer to the research institution. Specificatio would not be applicable (as the research institution is not acting in good faith) and the present-day Henrietta Lacks would automatically be the owner of the cell line derived from her tissue sample.

In this scenario, the present-day Henrietta Lacks has two common law remedies. First, she can claim possession of the cell line from anyone who is in possession of it. (The legal description of the action would be rei vindicatio.) Second, she can claim all profits from anyone who sold the cell line, using a general enrichment action. (The legal description would be condictio sine causa specialis.) Why would an enrichment action succeed in this scenario, but not in the previous scenario? The reason is that in this scenario the present-day Henrietta Lacks is the owner of a tradeable legal object(s), namely the cell line. It follows that the profits made by others from her cell line ought to have been her profits, and the fact that she did not receive such profits means that she was impoverished. Accordingly, those who sell her cell line for profit enrich themselves at her expense, and she is entitled to claim such profits from them.

IV. CONCLUSION

To encapsulate: where a research institution uses a tissue sample from a research participant to create a cell line, the research institution will be the owner of the cell line and the research participant will have no claim to any income generated by such cell line-provided that the research institution complied with the statutory informed consent requirements, or where the informed consent was deficient, it was an honest mistake on the part of the research institution. However, where the informed consent was deficient due to willful disregard of the law by the research institution, the research participant would be the owner of the cell line and would be entitled to all profits generated by the commercialization of the cell line.

It should be noted that classical Roman law was developed around the value of good faith, rather than based on strict compliance with rules. Via Roman-Dutch law, this more flexible approach is also part of South African law. Accordingly, strict compliance with the informed consent requirement of the NHA is not the only factor in South African law that determines ownership of the eventual cell line. The other factor is the honesty of the researchers involved-whether they acted in good faith.

I conclude with an observation regarding the US lawsuit brought by Henrietta Lacks' estate against Thermo Fisher Scientific. At the time of writing, Thermo Fisher Scientific filed a motion to dismiss arguing that the case was brought outside the timeframe allowed by Maryland's statute of limitations. If this case proceeds to court-and if it is not subsequently settled out of court-the court's judgment would be an insightful addition to other leading US authorities on the rights of research participants vis-à-vis research institutions. It should be noted, however, that although South African courts may have regard for foreign judgments, such judgments are of course not binding authority in South Africa, and would only be relevant to the extent that the legal principles informing the judgment are applicable in South African law. Yet, what is true of both US and South African law-as both are largely uncodified systems of law-is that finding the legal position often requires an integration of various sources of the law. This includes the relevant jurisdiction's statute law and common law, which may have its roots in centuries or millennia past. As illustrated by the analysis of the three scenarios above, problems brought about by modern science may have ancient solutions.

ACKNOWLEDGEMENT

The author would like to express his gratitude to Beverley Townsend and Bonginkosi Shozi for their useful comments on previous drafts of this essay, and to Michaela Steytler for assisting with the technical editing of the essay. Any remaining errors are the author's alone.

FUNDING

The author acknowledges the support by the US National Institute of Mental Health and the US National Institutes of Health (award number U01MH127690). The content of this essay is solely the author's responsibility and does not necessarily represent the official views of the US National Institute of Mental Health or the US National Institutes of Health.

Footnotes

Faroque A. Khan, The Immortal Life of Henrietta Lacks, 43 J. IMA. 93 (2011).

Academy of Science of South Africa & Department of Science and Technology, Human Genetics and Genomics in South Africa: Ethical, Legal and Social Implications (2018), https://research.assaf.org.za/bitstream/handle/20.500.11911/106/2018_assaf_ethical_genetics_genomics_consensus.pdf?sequence=1&isAllowed=y; Safia Mahomed et al., The Legal Position on the Classification of Human Tissue in South Africa: Can Tissues Be Owned?, 6 S. Afr. J. Bioethics & L., 16 (2013); Donrich W. Jordaan, Social Justice and Research Using Human Biological Material: A Response to Mahomed, Nöthling-Slabbert and Pepper, 106 S. Afr. Med. J. 678 (2016); S. Mahomed et al., Ownership and Human Tissue - the Legal Conundrum: A Response to Jordaan's Critique, 107 S. Afr. Med. J. 196 (2017); Donrich W. Jordaan, An Imaginary Legal Conundrum: A Reply to the Response by Mahomed, Nöthling-Slabbert and Pepper, 107 S. Afr. Med. J. 199 (2017); M.S. Pepper, Exporting DNA - Striking a Balance between Preventing Exploitation and Promoting Innovation, 107 S. Afr. Med. J. 106 (2017) .

Magda Slabbert, This Is My Kidney, I Can Do What I Want with It - Property Rights and Ownership of Human Organs, 30 Obiter 499 (2021).

Donrich W. Thaldar & Bonginkosi Shozi, The Legal Status of Human Biological Material Used for Research, 138 S. Afr. L. J. 881 (2021).

R v. Kelly & Lindsay [1999] QB 621 (Eng. & Wales); Roche v. Douglas [2000] WASC 146, (2000) 22 WAR 331 (Austl.); CC v. AW 2005 ABQB 290 (Can.); Yearworth v. North Bristol NHS Trust [2009] EWCA Civ. 37, [2010] QB 1 (Eng.).

National Health Act 61 of 2003 (S. Afr.).

Regulations Relating to the Use of Human Biological Material, GN R.177 of GG 35099 (2 Mar 2012) (S. Afr.); Regulations Regarding the General Control of Human Bodies, Tissue, Blood, Blood Products and Gametes, GN R.180 of GG 35099 (2 March 2012) (S. Afr.); Regulations Relating to Research with Human Participants, GN R.719 of GG 38000 (19 Sep 2014) (S. Afr.).

Minister of Justice and Constitutional Development v. Prince 2018 (6) SA 393 (CC) (S. Afr.).

Material Transfer Agreement for Human Biological Materials, GN 719 of GG 41781 (20 July 2018) (S. Afr.).

Donrich W. Thaldar et al., South Africa's New Standard Material Transfer Agreement: Proposals for Improvement and Pointers for Implementation, BMC Med. Ethics, Sept. 2020, 21; Donrich Thaldar, One Material Transfer Agreement to Rule Them All? A Call for Revising South Africa's New Standard Material Transfer Agreement, Hum. & Soc. Sci. Comm., Sept. 2020, 7.

Protection of Personal Information Act 4 of 2013 (S. Afr.).

Donrich W. Thaldar & Beverley A. Townsend, Exempting Health Research from the Consent Provisions of POPIA, Potchefstroom Electronic L. J., Sept. 2021, 24; Donrich Thaldar, Why POPIA Does Not Apply to DNA, S. Afr. J. Sci., Jun. 2021, 117.

Aldine Timber Company v. Hlatwayo 1932 TPD 337 (S. Afr.); S v. Riekert 1977 (3) SA 181 (T) (S. Afr.).

McCarthy Retail Ltd v. Shortdistance Carriers CC [2001] ZASCA 14 (S. Afr.); Kudu Granite Operations (Pty) Ltd v. Caterna Ltd [2003] ZASCA 64 (S. Afr.).

K v. Minister of Safety and Security 2005 (6) SA 419 (CC) (S. Afr.).

Articles from Journal of Law and the Biosciences are provided here courtesy of Oxford University Press

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Who would own the HeLa cell line if the Henrietta Lacks case happened in present-day South Africa?

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The HeLa cell line was created in 1951 without consent from Henrietta Lacks, the person whose tissue sample was used. In 2021, the descendants of Henrietta Lacks sued a well-known biotechnology company for the profits it made from the HeLa cell ...

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The staggering death toll of scientific lies

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Scientific fraud kills people. Should it be illegal?

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Research

Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it. ¹ A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.

Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance.

In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values. ²

In order to maximize AI benefits, we recommend nine steps for going forward:

Encourage greater data access for researchers without compromising users' personal privacy,

invest more government funding in unclassified AI research,

promote new models of digital education and AI workforce development so employees have the skills needed in the 21st-century economy,

create a federal AI advisory committee to make policy recommendations,

engage with state and local officials so they enact effective policies,

regulate broad AI principles rather than specific algorithms,

take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,

maintain mechanisms for human oversight and control, and

penalize malicious AI behavior and promote cybersecurity.

Intentionality

Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.

Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.

Intelligence

AI generally is undertaken in conjunction with machine learning and data analytics. ⁵ Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues. All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

Adaptability

AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved "experience" is immediately and fully transferable to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways. ⁶

One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that "artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030." ⁷ That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that "AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption." ⁸ Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

Finance

Investments in financial AI in the United States tripled between 2013 and 2014 to a total of $12.2 billion.⁹ According to observers in that sector, "Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check."¹⁰ In addition, there are so-called robo-advisers that "create personalized investment portfolios, obviating the need for stockbrokers and financial advisers."¹¹ These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.

Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels. ¹⁴

National security

Artificial intelligence will accelerate the traditional process of warfare so rapidly that a new term has been coined: hyperwar.

The big data analytics associated with AI will profoundly affect intelligence analysis, as massive amounts of data are sifted in near real time-if not eventually in real time-thereby providing commanders and their staffs a level of intelligence analysis and productivity heretofore unseen. Command and control will similarly be affected as human commanders delegate certain routine, and in special circumstances, key decisions to AI platforms, reducing dramatically the time associated with the decision and subsequent action. In the end, warfare is a time competitive process, where the side able to decide the fastest and move most quickly to execution will generally prevail. Indeed, artificially intelligent intelligence systems, tied to AI-assisted command and control systems, can move decision support and decisionmaking to a speed vastly superior to the speeds of the traditional means of waging war. So fast will be this process, especially if coupled to automatic decisions to launch artificially intelligent autonomous weapons systems capable of lethal outcomes, that a new term has been coined specifically to embrace the speed at which war will be waged: hyperwar.

While the ethical and legal debate is raging over whether America will ever wage war with artificially intelligent autonomous lethal systems, the Chinese and Russians are not nearly so mired in this debate, and we should anticipate our need to defend against these systems operating at hyperwar speeds. The challenge in the West of where to position "humans in the loop" in a hyperwar scenario will ultimately dictate the West's capacity to be competitive in this new form of conflict. ¹⁷

Just as AI will profoundly affect the speed of warfare, the proliferation of zero day or zero second cyber threats as well as polymorphic malware will challenge even the most sophisticated signature-based cyber protection. This forces significant improvement to existing cyber defenses. Increasingly, vulnerable systems are migrating, and will need to shift to a layered approach to cybersecurity with cloud-based, cognitive AI platforms. This approach moves the community toward a "thinking" defensive capability that can defend networks through constant training on known threats. This capability includes DNA-level analysis of heretofore unknown code, with the possibility of recognizing and stopping inbound malicious code by recognizing a string component of the file. This is how certain key U.S.-based systems stopped the debilitating "WannaCry" and "Petya" viruses.

Preparing for hyperwar and defending critical cyber networks must become a high priority because China, Russia, North Korea, and other countries are putting substantial resources into AI. In 2017, China's State Council issued a plan for the country to "build a domestic industry worth almost $150 billion" by 2030.¹⁸ As an example of the possibilities, the Chinese search firm Baidu has pioneered a facial recognition application that finds missing people. In addition, cities such as Shenzhen are providing up to $1 million to support AI labs. That country hopes AI will provide security, combat terrorism, and improve speech recognition programs.¹⁹ The dual-use nature of many AI algorithms will mean AI research focused on one sector of society can be rapidly modified for use in the security sector as well.²⁰

Health care

AI tools are helping designers improve computational sophistication in health care. For example, Merantix is a German company that applies deep learning to medical issues. It has an application in medical imaging that "detects lymph nodes in the human body in Computer Tomography (CT) images." ²¹ According to its developers, the key is labeling the nodes and identifying small lesions or growths that could be problematic. Humans can do this, but radiologists charge $100 per hour and may be able to carefully read only four images an hour. If there were 10,000 images, the cost of this process would be $250,000, which is prohibitively expensive if done by humans.

What deep learning can do in this situation is train computers on data sets to learn what a normal-looking versus an irregular-appearing lymph node is. After doing that through imaging exercises and honing the accuracy of the labeling, radiological imaging specialists can apply this knowledge to actual patients and determine the extent to which someone is at risk of cancerous lymph nodes. Since only a few are likely to test positive, it is a matter of identifying the unhealthy versus healthy node.

AI has been applied to congestive heart failure as well, an illness that afflicts 10 percent of senior citizens and costs $35 billion each year in the United States. AI tools are helpful because they "predict in advance potential challenges ahead and allocate resources to patient education, sensing, and proactive interventions that keep patients out of the hospital." ²²

Criminal justice

AI is being deployed in the criminal justice area. The city of Chicago has developed an AI-driven "Strategic Subject List" that analyzes people who have been arrested for their risk of becoming future perpetrators. It ranks 400,000 people on a scale of 0 to 500, using items such as age, criminal activity, victimization, drug arrest records, and gang affiliation. In looking at the data, analysts found that youth is a strong predictor of violence, being a shooting victim is associated with becoming a future perpetrator, gang affiliation has little predictive value, and drug arrests are not significantly associated with future criminal activity. ²³

Judicial experts claim AI programs reduce human bias in law enforcement and leads to a fairer sentencing system. R Street Institute Associate Caleb Watney writes:

Empirically grounded questions of predictive risk analysis play to the strengths of machine learning, automated reasoning and other forms of AI. One machine-learning policy simulation concluded that such programs could be used to cut crime up to 24.8 percent with no change in jailing rates, or reduce jail populations by up to 42 percent with no increase in crime rates. ²⁴

However, critics worry that AI algorithms represent "a secret system to punish citizens for crimes they haven't yet committed. The risk scores have been used numerous times to guide large-scale roundups." ²⁵ The fear is that such tools target people of color unfairly and have not helped Chicago reduce the murder wave that has plagued it in recent years.

Transportation

Transportation represents an area where AI and machine learning are producing major innovations. Research by Cameron Kerry and Jack Karsten of the Brookings Institution has found that over $80 billion was invested in autonomous vehicle technology between August 2014 and June 2017. Those investments include applications both for autonomous driving and the core technologies vital to that sector. ²⁸

Autonomous vehicles-cars, trucks, buses, and drone delivery systems-use advanced technological capabilities. Those features include automated vehicle guidance and braking, lane-changing systems, the use of cameras and sensors for collision avoidance, the use of AI to analyze information in real time, and the use of high-performance computing and deep learning systems to adapt to new circumstances through detailed maps. ²⁹

Light detection and ranging systems (LIDARs) and AI are key to navigation and collision avoidance. LIDAR systems combine light and radar instruments. They are mounted on the top of vehicles that use imaging in a 360-degree environment from a radar and light beams to measure the speed and distance of surrounding objects. Along with sensors placed on the front, sides, and back of the vehicle, these instruments provide information that keeps fast-moving cars and trucks in their own lane, helps them avoid other vehicles, applies brakes and steering when needed, and does so instantly so as to avoid accidents.

Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. This means that software is the key-not the physical car or truck itself.

Ride-sharing companies are very interested in autonomous vehicles. They see advantages in terms of customer service and labor productivity. All of the major ride-sharing companies are exploring driverless cars. The surge of car-sharing and taxi services-such as Uber and Lyft in the United States, Daimler's Mytaxi and Hailo service in Great Britain, and Didi Chuxing in China-demonstrate the opportunities of this transportation option. Uber recently signed an agreement to purchase 24,000 autonomous cars from Volvo for its ride-sharing service. ³²

However, the ride-sharing firm suffered a setback in March 2018 when one of its autonomous vehicles in Arizona hit and killed a pedestrian. Uber and several auto manufacturers immediately suspended testing and launched investigations into what went wrong and how the fatality could have occurred. ³³ Both industry and consumers want reassurance that the technology is safe and able to deliver on its stated promises. Unless there are persuasive answers, this accident could slow AI advancements in the transportation sector.

Smart cities

Metropolitan governments are using AI to improve urban service delivery. For example, according to Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson:

The Cincinnati Fire Department is using data analytics to optimize medical emergency responses. The new analytics system recommends to the dispatcher an appropriate response to a medical emergency call-whether a patient can be treated on-site or needs to be taken to the hospital-by taking into account several factors, such as the type of call, location, weather, and similar calls. ³⁴

Since it fields 80,000 requests each year, Cincinnati officials are deploying this technology to prioritize responses and determine the best ways to handle emergencies. 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.

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. For its smart cities index, the magazine Fast Company ranked American locales and found Seattle, Boston, San Francisco, Washington, D.C., and New York City as the top adopters. Seattle, for example, has embraced sustainability and is using AI to manage energy usage and resource management. Boston has launched a "City Hall To Go" that makes sure underserved communities receive needed public services. It also has deployed "cameras and inductive loops to manage traffic and acoustic sensors to identify gun shots." San Francisco has certified 203 buildings as meeting LEED sustainability standards. ³⁵

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. Among the top applications noted in the report are "smart meters for utilities, intelligent traffic signals, e-governance applications, Wi-Fi kiosks, and radio frequency identification sensors in pavement." ³⁶

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? What about questions of legal liability in cases where algorithms cause harm? ³⁷

The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues.

Data access problems

The key to getting the most out of AI is having a "data-friendly ecosystem with unified standards and cross-platform sharing." AI depends on data that can be analyzed in real time and brought to bear on concrete problems. Having data that are "accessible for exploration" in the research community is a prerequisite for successful AI development. ³⁸

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.S. ranks eighth overall in the world, compared to 93 for China. ³⁹

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.

Biases in data and algorithms

Racial issues also come up with facial recognition software. Most such systems operate by comparing a person's face to a range of faces in a large database. As pointed out by Joy Buolamwini of the Algorithmic Justice League, "If your facial recognition data contains mostly Caucasian faces, that's what your program will learn to recognize." ⁴² Unless the databases have access to diverse data, these programs perform poorly when attempting to recognize African-American or Asian-American features.

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:

The rise of automation and the increased reliance on algorithms for high-stakes decisions such as whether someone get insurance or not, your likelihood to default on a loan or somebody's risk of recidivism means this is something that needs to be addressed. Even admissions decisions are increasingly automated-what school our children go to and what opportunities they have. We don't have to bring the structural inequalities of the past into the future we create. ⁴³

AI ethics and transparency

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. ⁴⁴

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. According to Brookings researcher Jon Valant, the New Orleans-based Bricolage Academy "gives priority to economically disadvantaged applicants for up to 33 percent of available seats. In practice, though, most cities have opted for categories that prioritize siblings of current students, children of school employees, and families that live in school's broad geographic area." ⁴⁵ Enrollment choices can be expected to be very different when considerations of this sort come into play.

Depending on how AI systems are set up, they can facilitate the redlining of mortgage applications, help people discriminate against individuals they don't like, or help screen or build rosters of individuals based on unfair criteria. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers. ⁴⁶

For these reasons, the EU is implementing the General Data Protection Regulation (GDPR) in May 2018. The rules specify that people have "the right to opt out of personally tailored ads" and "can contest 'legal or similarly significant' decisions made by algorithms and appeal for human intervention" in the form of an explanation of how the algorithm generated a particular outcome. Each guideline is designed to ensure the protection of personal data and provide individuals with information on how the "black box" operates. ⁴⁷

Legal liability

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. A body of case law has shown that the situation's facts and circumstances determine liability and influence the kind of penalties that are imposed. Those can range from civil fines to imprisonment for major harms. ⁴⁸ The Uber-related fatality in Arizona will be an important test case for legal liability. 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. For example, in the case of Airbnb, the firm "requires that people agree to waive their right to sue, or to join in any class-action lawsuit or class-action arbitration, to use the service." By demanding that its users sacrifice basic rights, the company limits consumer protections and therefore curtails the ability of people to fight discrimination arising from unfair algorithms. ⁴⁹ But whether the principle of neutral networks holds up in many sectors is yet to be determined on a widespread basis.

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.

Improving data access

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. ⁵⁰ Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.

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. ⁵¹ As part of the arrangement, researchers were required to undergo background checks and could only access data from secured sites in order to protect user privacy and security.

In the U.S., 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.

Google long has made available search results in aggregated form for researchers and the general public. Through its "Trends" site, scholars can analyze topics such as interest in Trump, views about democracy, and perspectives on the overall economy. ⁵² That helps people track movements in public interest and identify topics that galvanize the general public.

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.

Increase government investment in AI

Promote digital education and workforce development

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 2017, the National Science Foundation funded over 6,500 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. ⁵⁷ The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.

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. AI will reconfigure how society and the economy operate, and there needs to be "big picture" thinking on what this will mean for ethics, governance, and societal impact. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas.

One example of new ways to prepare students for a digital future is IBM's Teacher Advisor program, utilizing Watson's free online tools to help teachers bring the latest knowledge into the classroom. 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. ⁵⁸ As such, they are precursors of new educational environments that need to be created.

Create a federal AI advisory committee

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.

In order to move forward in this area, several members of Congress have introduced the "Future of Artificial Intelligence Act," a bill designed to establish broad policy and legal principles for AI. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence. The legislation provides a mechanism for the federal government to get advice on ways to promote a "climate of investment and innovation to ensure the global competitiveness of the United States," "optimize the development of artificial intelligence to address the potential growth, restructuring, or other changes in the United States workforce," "support the unbiased development and application of artificial intelligence," and "protect the privacy rights of individuals." ⁵⁹

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 540 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 540 days to 180 days. Waiting nearly two years for a committee report will certainly result in missed opportunities and a lack of action on important issues. Given rapid advances in the field, having a much quicker turnaround time on the committee analysis would be quite beneficial.

Engage with state and local officials

According to the legislation's developers, city officials want to know how these algorithms work and make sure there is sufficient AI transparency and accountability. In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage. It is scheduled to report back to the mayor on a range of AI policy, legal, and regulatory issues by late 2019.

Some observers already are worrying that the taskforce won't go far enough in holding algorithms accountable. For example, Julia Powles of Cornell Tech and New York University argues that the bill originally required companies to make the AI source code available to the public for inspection, and that there be simulations of its decisionmaking using actual data. After criticism of those provisions, however, former Councilman James Vacca dropped the requirements in favor of a task force studying these issues. He and other city officials were concerned that publication of proprietary information on algorithms would slow innovation and make it difficult to find AI vendors who would work with the city. ⁶² It remains to be seen how this local task force will balance issues of innovation, privacy, and transparency.

Regulate broad objectives more than specific algorithms

The GDPR being implemented in Europe place severe restrictions on the use of artificial intelligence and machine learning. According to published guidelines, "Regulations prohibit any automated decision that 'significantly affects' EU citizens. This includes techniques that evaluates a person's 'performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements.'" ⁶⁵ In addition, these new rules give citizens the right to review how digital services made specific algorithmic choices affecting people.

By taking a restrictive stance on issues of data collection and analysis, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

It makes more sense to think about the broad objectives desired in AI and enact policies that advance them, as opposed to governments trying to crack open the "black boxes" and see exactly how specific algorithms operate. Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

Take biases seriously

Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole.

For these advances to be widely adopted, more transparency is needed in how AI systems operate. Andrew Burt of Immuta argues, "The key problem confronting predictive analytics is really transparency. We're in a world where data science operations are taking on increasingly important tasks, and the only thing holding them back is going to be how well the data scientists who train the models can explain what it is their models are doing." ⁶⁶

Maintaining mechanisms for human oversight and control

Some individuals have argued that there needs to be avenues for humans to exercise oversight and control of AI systems. For example, Allen Institute for Artificial Intelligence CEO Oren Etzioni argues there should be rules for regulating these systems. First, he says, AI must be governed by all the laws that already have been developed for human behavior, including regulations concerning "cyberbullying, stock manipulation or terrorist threats," as well as "entrap[ping] people into committing crimes." Second, he believes that these systems should disclose they are automated systems and not human beings. Third, he states, "An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information." ⁶⁷ His rationale is that these tools store so much data that people have to be cognizant of the privacy risks posed by AI.

In the same vein, the IEEE Global Initiative has ethical guidelines for AI and autonomous systems. Its experts suggest that these models be programmed with consideration for widely accepted human norms and rules for behavior. AI algorithms need to take into effect the importance of these norms, how norm conflict can be resolved, and ways these systems can be transparent about norm resolution. Software designs should be programmed for "nondeception" and "honesty," according to ethics experts. When failures occur, there must be mitigation mechanisms to deal with the consequences. In particular, AI must be sensitive to problems such as bias, discrimination, and fairness. ⁶⁸

A group of machine learning experts claim it is possible to automate ethical decisionmaking. Using the trolley problem as a moral dilemma, they ask the following question: If an autonomous car goes out of control, should it be programmed to kill its own passengers or the pedestrians who are crossing the street? They devised a "voting-based system" that asked 1.3 million people to assess alternative scenarios, summarized the overall choices, and applied the overall perspective of these individuals to a range of vehicular possibilities. That allowed them to automate ethical decisionmaking in AI algorithms, taking public preferences into account. ⁶⁹ This procedure, of course, does not reduce the tragedy involved in any kind of fatality, such as seen in the Uber case, but it provides a mechanism to help AI developers incorporate ethical considerations in their planning.

Penalize malicious behavior and promote cybersecurity

To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decisionmaking, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.

The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.

Yet the manner in which AI systems unfold has major implications for society as a whole. It matters how policy issues are addressed, ethical conflicts are reconciled, legal realities are resolved, and how much transparency is required in AI and data analytic solutions. ⁷⁴ Human choices about software development affect the way in which decisions are made and the manner in which they are integrated into organizational routines. Exactly how these processes are executed need to be better understood because they will have substantial impact on the general public soon, and for the foreseeable future. AI may well be a revolution in human affairs, and become the single most influential human innovation in history.
Note: We appreciate the research assistance of Grace Gilberg, Jack Karsten, Hillary Schaub, and Kristjan Tomasson on this project. The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars. Support for this publication was generously provided by Amazon. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment. John R. Allen is a member of the Board of Advisors of Amida Technology and on the Board of Directors of Spark Cognition. Both companies work in fields discussed in this piece.

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How artificial intelligence is transforming the world | Brookings

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Darrell West and John Allen examine the societal and political aspects of developing artificial intelligence technologies.