Emerging AI applications will undoubtedly advance our technology and improve our lives. They will likely make our roads safer and our homes more comfortable, improve our food production and ease the burdens of many everyday tasks. There exists a dark side to such advancement, however, and the meteoric rise of AI technology will certainly raise many significant societal questions. There is perhaps no greater uncertainty than how AI will impact our economic growth and likely displace some of our workforce in coming decades. <\/p>\n\n\n\n
Much closer on the horizon, three pressing legal questions have already emerged and remain largely unanswered: First, who will be legally responsible when AI causes injury? Second, how will we protect the immense value of AI innovation? Third, how will we balance the competing interests of AI\u2019s societal benefits with its societal costs, such as reduced individual privacy? Before considering these legal gray areas\u2014AI liability, innovation and privacy\u2014it is pivotal to first understand the scope and the importance of specificity when addressing AI.<\/p>\n\n\n\n
What Do We Mean by \u201cArtificial Intelligence\u201d?<\/h1>\n\n\n\n
Although AI is nearly ubiquitous, it has no universal definition. It is not an area of law nor a single industry. AI is a technological revolution that impacts virtually all facets of our lives. A common definition of AI refers to the capability of machines to emulate human behavior, particularly intelligence and decision making.[2]<\/sup><\/a>https:\/\/www.merriam-webster.com\/dictionary\/artificial%20intelligence<\/span><\/a><\/span><\/span> However, this definition is certainly underinclusive in how society uses the term. Sure, artificial intelligence includes Terminators, IBM\u2019s Watson and other highly sophisticated, autonomous and (perhaps in the future) self-aware computer systems. But AI\u2014perhaps in conflation with automation\u2014is often used to describe much more, such as: (1) software that performs processing typically performed by humans; (2) software that uses data to provide reports; (3) fitness trackers; (4) software that uses data for predictive analytics; (5) smart thermostats; (6) software that predicts illness spread, weather or traffic; (7) hardware components for robotic systems; (8) software that understands and mimics human speech; (9) virtual assistants; (10) underlying computer algorithm designs; (11) content recommendations on streaming platforms; and (12) autonomous robotic systems. In categorizing patents, even the USPTO found no definition with adequate specificity and instead defined AI patents by identifying eight \u201ccomponent technologies.\u201d[3]<\/sup><\/a>USPTO, supra note i at 3.<\/span><\/span> Given the varying definitions, AI\u2019s scope for now is defined only by the label we ascribe to it.\u00a0<\/p>\n\n\n\n To be sure, AI is different from the Internet of Things (IoT), which generally refers to devices with sensors capable of gathering data and communicating over the internet. But the line between AI, IoT and mere software can become blurry. Ultimately, what becomes clear about the definition of AI is that it lacks clarity because AI\u2019s potential scope, as understood by the general public, often stretches far beyond the narrower scope ascribed by scientists and engineers.[4]<\/sup><\/a>More specific terms, such as machine learning and deep neural network, which are subsets of AI, provide better clarity regarding the meaning of the technology at issue. However, they do not resolve … Continue reading<\/span><\/span><\/span>\u00a0<\/p>\n\n\n\n AI\u2019s evolving and broadening nature presents challenges in measuring its impact and analyzing policy decisions. Potential liability from errors in weather forecasting software presents different considerations than errors from medical diagnostic software. Innovation in autonomous vehicle and drone technology impacts our economy differently than automated calendaring software. And the data associated with what temperature our thermostat is set to at night presents different privacy concerns than the devices in our living room listening to (and perhaps recording)[5]<\/sup><\/a>See, e.g., Matt Day, Giles Turner and Natalia Drozdiak, \u201cThousands of Amazon Workers Listen to Alexa Users\u2019 Conversations\u201d (Apr. 11, 2019), … Continue reading<\/span><\/span><\/span> our conversations.\u00a0<\/p>\n\n\n\n Accordingly, specificity is important. In making policy decisions, characterizing the issue merely as AI can be misleading. Yet, addressing AI policies and regulation at the micro level for each individual technology can be overwhelming and inefficient. Luckily, this is not necessary since there exists a \u201cGoldilocks level\u201d of specificity when addressing AI. Although, as a category, AI is far too broad to be specific, commonalities pervade its continuum. For example, like technologies can be grouped together. The key to meaningful dialogue and specificity is recognizing AI\u2019s breadth and deliberately using specificity for precisely the AI category at issue. When addressing AI, we must articulate its scope and meaning or group it only with contextually similar applications.[6]<\/sup><\/a>It may seem contradictory to emphasize the importance of specificity in making policy decisions relating to AI and then to discuss AI generally in this article. But the focus of this article is not … Continue reading<\/span><\/span><\/span><\/p>\n\n\n\n Our society\u2019s readiness for AI, in many ways, will be measured by the readiness of our legal system. After all, our legal system is the system of rules for what conduct society is willing to accept, how we are willing to allocate risk, and who we believe is deserving of compensation. Laws govern everything we do. Although AI will raise many questions regarding legal policy\u2014some of which have yet to be considered\u2014three leading questions have emerged: First, how we will impose liability for injuries caused by AI; second, how we intend to protect and promote the innovation of AI technology; and third, how we will balance concerns for individual privacy from AI use with benefits to society as a whole. <\/p>\n\n\n\n \u201cWho is responsible for this?\u201d That question has echoed in our minds since childhood. At its core, this simple question is rooted in the fundamental notion that those \u201cresponsible\u201d for causing harm should be required to remedy it\u2014which most often means \u201cpay for it.\u201d As AI continues to play an increasing role in our everyday lives, the potential for harm (and liability) seems inevitable. Self-driving cars crash, automated software applications malfunction, and AI predictions prove to be wrong. When harm results, we will once again ask, \u201cWho is responsible for this?\u201d Except, we will be asking that question in a new frontier where decisions might have been made by a robot (or autonomous system) instead of a human. <\/p>\n\n\n\n In the absence of a contract that answers the question, liability for such wrongdoing is governed by tort law in the U. S. The law of torts imposes liability for both intentional torts (when the wrongdoer\u2019s conduct was intentional) and negligent torts (when an actor had no intention of causing harm but did so in a way that society views as falling below a \u201creasonable\u201d standard of care). Negligence (unintended harm) is the most common form of tort liability and will likely continue to be in the context of AI, where the vast majority of AI is likely to be programmed to avoid causing harm. To demonstrate liability for negligence, a party must generally demonstrate four elements: First, the defendant owed a duty to the plaintiff; second, the defendant breached a duty to the plaintiff; third, the defendant\u2019s actions were the cause (both \u201cactual\u201d and \u201cproximate\u201d) of the plaintiff\u2019s injury; and fourth, the plaintiff sustained some harm or injury. <\/p>\n\n\n\n In a negligence lawsuit involving AI, the plaintiff will be obvious: the person who suffered the harm. But who will the defendant be? The AI system? The person who developed the AI system? The company that developed the AI system? The company that sold the AI system? The person who operated the AI system? The list is limited perhaps only by the creativity of the plaintiff\u2019s attorney and whatever legal limits exist for liability under tort law. <\/p>\n\n\n\n Given the clear focus of the analysis on the defendant\u2019s actions to prove a negligence claim, it is imperative to name the right party as a defendant. As a matter of practice, a plaintiff lawyer\u2019s creativity to seek a meaningful recovery for the client is frequently guided by the opportunity to sue wealthy parties, often a company. Why? Because they are most likely to result in a payment to the plaintiff. Obtaining a $10 million judgment can quickly become a Pyrrhic victory, when the defendant found liable has no assets (insurance or funds) to satisfy the judgment. In such a case, the plaintiff wins the legal claim, yet remains uncompensated. Worse, since many personal injury cases are litigated on a contingency basis (where the plaintiff\u2019s lawyer is compensated only if the plaintiff recovers), a dim prospect of actual payment might result in difficulty even obtaining a lawyer. For some accidents, this might not pose a significant concern. In most fender-benders, for example, finding a party liable results in compensation either through the insurance company or the responsible party, who is likely to have assets to satisfy a small judgment. But raise the stakes to a single plaintiff with very significant injury (for example, a child killed by a self-driving car or a plaintiff misdiagnosed by medical software) or thousands of plaintiffs with relatively minor harm (for example, a smart thermostat that turns off the heat to thousands of homes or a digital assistant that mistakenly orders items online[7]<\/sup><\/a>See, e.g., Maham Abedi, \u201cAmazon Echo mistakenly orders cat food after hearing TV commercial\u201d (Feb. 14, 2018), https:\/\/globalnews.ca\/news\/4025172\/amazon-echo-orders-cat-food-tv-commercial\/<\/span><\/a><\/span><\/span> ) and the potential for under-compensation becomes real, particularly against an individual or a small, underinsured company with few assets.[8]<\/sup><\/a>Many of the companies leading AI development are large companies that would not raise such concerns. However, if liability does not extend to such companies, companies with fewer assets that were … Continue reading<\/span><\/span><\/span>\u00a0<\/p>\n\n\n\n Devising a general rule as to which party should be held liable anytime AI causes harm is difficult. Like any negligence case, context is critical, and the liability of the actor will depend on the particular circumstances of the case, as well as what led to the harm. The questions central to the inquiry of liability are likely to include: Whose conduct fell below society\u2019s expectations, and was the harm foreseeable from the conduct at issue? Yet, it doesn\u2019t take a lawyer to appreciate the difficulties raised by negligence elements for imposing liability for AI-caused harm.<\/p>\n\n\n\n For many obvious reasons, a suit against an AI system itself is implausible, at least until AI systems start gaining personhood[9]<\/sup><\/a>In 2017, an AI system granted citizenship by Saudi Arabia became the first robot to be given personhood. See Emily Reynolds, \u201cThe agony of Sophia, the world\u2019s first robot citizen condemned to a … Continue reading<\/span><\/span><\/span> and owning property. When a dog bites someone, the plaintiff doesn\u2019t sue the dog.\u00a0<\/p>\n\n\n\n Also, negligence suits against AI developers are far from guaranteed to provide recovery because problems might arise with demonstrating that the developer owed a duty to the plaintiff, if the software is used in a way not intended by the developer or harms someone who was not anticipated to be impacted by the software. An additional concern might be proving that the developer was the cause of the harm, if the AI system caused harm in an unexpected and unforeseeable way. <\/p>\n\n\n\n AI systems can often be a \u201cblack box\u201d due to the inability to know exactly how the system operates or makes its decisions. The more complex the thinking of the AI system, the further removed the developer is from foreseeability, and the less likely there is to be liability. Similarly, a reseller of an AI system might not have done anything unreasonable simply by providing a product or service developed by someone else. As such, there may be a need to rethink the applicability\u2014or at least the scope\u2014of foreseeability in our traditional analysis of negligence law when it comes to AI liability. <\/p>\n\n\n\n An alternative avenue to liability against AI systems may be based on strict liability\u2014a tort claim that imposes liability regardless of whether the defendant\u2019s actions were intentional or reasonable. But strict liability laws are limited to very specific contexts, such as animals, abnormally dangerous activities and products liability. Although the discussion has spanned decades, it is still far from clear whether software constitutes a \u201cproduct\u201d that is subject to strict products liability.[10]<\/sup><\/a>See Bryan H. Choi, \u201cCrashworthy Code,\u201d 94 Wash. L. Rev. 39, 53 (2019) (\u201c[N]one of those arguments are new, and they have long failed to move any court to extend products liability law to … Continue reading<\/span><\/span><\/span> Since AI provided by a party primarily\u2014sometimes exclusively\u2014comprises software, strict liability currently might not extend to AI.\u00a0<\/p>\n\n\n\n However, even if an AI system does not fit within any of these categories, expanded strict liability laws (and accompanying insurance policies) may emerge as the leading way to govern the compensation of harm caused by AI systems. Importantly, imposition of liability without intent or negligence has drawbacks and requires careful consideration, especially regarding corporate willingness and ability to absorb such risk into business practices. A broader scope of insurance coverage leads to more expensive insurance and a higher cost of doing business, which might prohibit or discourage some AI uses and developers. <\/p>\n\n\n\n So, when the familiar question \u201cWho is responsible for this?\u201d arises in the context of the new AI frontier, we can take comfort in the robust, time-tested legal framework that we can look to for answers. Yet, that comfort may be misplaced. Tort law is largely shaped by constantly changing policy decisions about how our society chooses to allocate risk and provide compensation. Moreover, tort law is primarily governed by state law, which creates the very real potential for inconsistent laws and policies across different states. In the context of AI\u2019s emerging issues, the existing legal framework remains largely uncharted as to where these policy lines should be drawn. Absent legislation on AI liability, the boundaries for responsibility in this new frontier will continue to develop through common law (litigating individual cases in the courts). Since the development of common law takes significant time and a willingness for parties to take on the increasing costs of litigation, the law on AI liability could lag far behind society\u2019s fast-paced adoption of AI. <\/p>\n\n\n\n The boom in AI development has seen an enormous amount of innovation in just the last decade. For example, \u201c[t]here were 10 times as many AI patent applications published in 2019 as in 2013\u201d and \u201c[t]he same time period saw an almost four-fold increase in granted AI patents.\u201d[11]<\/sup><\/a>See Center for Security and Emerging Technology (CSET), \u201cPatents and Artificial Intelligence: A Primer,\u201d 2 and 13 (2020), … Continue reading<\/span><\/span><\/span> Not surprisingly, AI\u2019s immense value has created a significant legal battleground for exploiting and protecting AI innovation. When it comes to leveraging AI innovation, individuals and companies have two key questions to consider: Do I have the legal right to do what I would like to do, and do I have the legal right to exclude others from doing it. The regulation of these legal questions falls squarely in the domain of intellectual property (IP).\u00a0<\/p>\n\n\n\n IP refers to intangible property\u2014\u201ccreations of the mind, such as inventions; literary and artistic works; designs; and symbols, names and images used in commerce.\u201d[12]<\/sup><\/a>\u201cWhat is Intellectual Property?\u201d, World Intellectual Property Organization, https:\/\/www.wipo.int\/about-ip\/en\/<\/span><\/a><\/span><\/span> IP law is comprised of four core legal areas: patents, trade secrets, copyrights and trademarks. Patent law, in particular, is a critical way to protect innovation in the U. S. The scope of what can be patented is quite broad: \u201c[A]ny new and useful process, machine, manufacture or composition of matter, or \u2026 improvement thereof\u201d is eligible for a patent, provided that the applicant can satisfy all other requirements in the statute.[13]<\/sup><\/a>35 U.S.C. \u00a7 101.<\/span><\/span> An owner of a patent enjoys a powerful monopoly to exclude others in the U.S. from making, using, offering to sell or selling the patented invention.[14]<\/sup><\/a>See 35 U.S.C. \u00a7 271(a).<\/span><\/span>\u00a0<\/p>\n\n\n\n An alternative for protecting innovation\u2014particularly innovation that is kept secret\u2014is trade secret law. Governed by both federal and state law, trade secret law protects \u201call forms and types of financial, business, scientific, technical, economic or engineering information,\u201d but only if the owner has taken reasonable measures to keep such information secret and the information \u201cderives independent economic value \u2026 from not being generally known.\u201d[15]<\/sup><\/a>See, e.g., 18 U.S.C. \u00a7 1839(3).<\/span><\/span> Although trade secret law does not protect against reverse engineering or independent discovery, it prohibits others from \u201cmisappropriating\u201d\u2014acquiring or disclosing\u2014a trade secret through \u201cimproper means.\u201d[16]<\/sup><\/a>See 18 U.S.C. \u00a7 1839(5) and (6).<\/span><\/span><\/p>\n\n\n\n Copyright law protects original works of authorship fixed in tangible form\u2014such as literary works, musical works, motion pictures, sound recordings, architectural works\u2014from unlawful reproduction and distribution.[17]<\/sup><\/a>See 17 U.S.C. \u00a7\u00a7 102, 106.<\/span><\/span> It does not protect, however, any \u201cidea, procedure, process, system, method of operation, concept, principle, or discovery.\u201d[18]<\/sup><\/a>See 17 U.S.C. \u00a7 102(b).<\/span><\/span> Thus, for example, copyright law might protect against the reproduction of the particular way in which a cooking recipe is expressed, but it would not preclude others from using or sharing the underlying process described in the recipe.\u00a0<\/p>\n\n\n\n Trademark law has a narrower scope in protecting AI innovation. A trademark is \u201cany word, name, symbol or device, or any combination thereof\u201d used to distinguish one\u2019s goods and indicate the source of goods.[19]<\/sup><\/a>15 U.S.C. \u00a7 1127.<\/span><\/span> Simply put, it is \u201chow customers recognize you in the marketplace and distinguish you from your competitors.\u201d[20]<\/sup><\/a>\u201cWhat is a trademark?\u201d, USPTO, https:\/\/www.uspto.gov\/trademarks\/basics\/what-trademark<\/span><\/a><\/span><\/span><\/p>\n\n\n\n The importance of IP and its value in today\u2019s world cannot be overstated. Gone are the days when most companies\u2019 value was tied to the buildings they owned and the widgets they made. According to recent reports, \u201cintangible assets\u201d\u2014a very significant portion of which are IP rights\u2014are \u201cnow responsible for 90 percent of all business value,\u201d as opposed to just 32 percent in 1985.[21]<\/sup><\/a>https:\/\/www.oceantomo.com\/intangible-asset-market-value-study\/<\/span><\/a>; see also https:\/\/www.aon.com\/getmedia\/60fbb49a-c7a5-4027-ba98-0553b29dc89f\/Ponemon-Report-V24.aspx<\/span><\/a><\/span><\/span> With so much value now tied to IP rights, the competition for IP innovation and ownership has never been greater. Moreover, the demand\u2014even dependence\u2014on owning IP has amplified the importance of the delicate balance at the center of patent and copyright law.\u00a0<\/p>\n\n\n\n A core principle underlying patent and copyright laws is that they provide strong incentives for individuals and companies to devote resources and time to innovation by granting them exclusive rights as a reward for their investment. However, as we grant more IP rights to individual inventors and authors, the more we limit the public\u2019s use and access to those rights. For example, granting a broad patent on drone technology leaves less for society in that same space due to the powerful monopoly to exclude others from using or selling the patented invention. As the U.S. Supreme Court has explained, \u201cmonopolization of [basic tools of science and technology] through the grant of a patent might tend to impede innovation more than it would tend to promote it.\u201d[22]<\/sup><\/a>Mayo Collaborative Servs. v. Prometheus Lab\u2019ys, Inc., 566 U.S. 66, 71 (2012).<\/span><\/span> Additionally, the exertion of broad IP rights can provide significant (and sometimes improper) leverage against competitors in both the marketplace and litigation.\u00a0<\/p>\n\n\n\n The debate on the appropriate scope of IP rights to promote, rather than stifle, innovation is far from new. And that debate is certain to carry through into the policy discussions surrounding the AI revolution. Recently, the National Security Commission on Artificial Intelligence asserted that \u201c[t]he United States lacks the comprehensive IP policies it needs for the AI era and is hindered by legal uncertainties in current U.S. patent eligibility and patentability doctrine.\u201d[23]<\/sup><\/a>National Security Commission on Artificial Intelligence, \u201cFinal Report,\u201d 12 (2021), https:\/\/www.nscai.gov\/wp-content\/uploads\/2021\/03\/Full-Report-Digital-1.pdf<\/span><\/a><\/span><\/span> Others believe that the exponential growth in AI patents,[24]<\/sup><\/a>SPTO, supra note i at 5; see also CSET, supra note xi at 13.<\/span><\/span> AI publications[25]<\/sup><\/a>Human-Centered AI Institute, Stanford University, \u201cArtificial Intelligence Index Report 2021,\u201d 18 (2021), https:\/\/aiindex.stanford.edu\/wp-content\/uploads\/2021\/11\/2021-AI-Index-Report_Master.pdf<\/span><\/a><\/span><\/span> and AI investment[26]<\/sup><\/a>CSET, \u201cTracking AI Investment,\u201d 8 (2020), https:\/\/cset.georgetown.edu\/wp-content\/uploads\/CSET-Tracking-AI-Investment.pdf<\/span><\/a><\/span><\/span> demonstrates tremendous promise for AI innovation under the current legal framework. Although many views exist on where to draw legal boundaries for protecting AI IP, everyone seems to share the view that the future answers to these questions will have tremendous importance for AI innovation in the U.S.\u00a0<\/p>\n\n\n\n As the historical debate on balancing IP rights takes center stage in the emerging AI space, a related, but perhaps even more complex, question has developed with it. While AI IP has traditionally meant the inventions and artistic works developed and created by individuals and companies in the realm of AI, as AI systems become more sophisticated, AI has moved from being intellectual property to generating intellectual property. For example, on July 29, 2019, the USPTO received a patent application listing a single non-human inventor for an \u201c[i]nvention generated by artificial intelligence.\u201d [27]<\/sup><\/a>The USPTO denied the application and refused to grant a patent. See Decision on Petition, https:\/\/www.uspto.gov\/sites\/default\/files\/documents\/16524350_22apr2020.pdf<\/span><\/a><\/span><\/span> This raises the unique and novel question of who owns IP generated by AI.<\/p>\n\n\n\n Although not fully settled, current U.S. patent law appears not to allow AI to own a patent or to be listed as an inventor on a patent. In answering the question whether an \u201cartificial intelligence machine [can] be an \u2018inventor\u2019 under the Patent Act,\u201d a federal district court (in the companion litigation to the above patent application) recently held that \u201cthe clear answer is no.\u201d[28]<\/sup><\/a>Thaler v. Hirshfeld, 558 F. Supp. 3d 238, 240 (E.D. Va. Sept. 2, 2021), appeal pending, No. 21-2347 (Fed. Cir. Sept. 24, 2021).<\/span><\/span> In the appeal of that case, the USPTO continued to maintain that under \u201c[t]he plain language\u2026 [of] the Patent Act\u2026 \u2013 only a human being can be an \u2018inventor.\u2019\u201d[29]<\/sup><\/a>Thaler, No. 21-2347, Dkt. No. 34 at 17.<\/span><\/span> Importantly, though, some other countries have taken a different approach and permitted AI to be listed as an inventor on a patent.[30]<\/sup><\/a>South Africa was the first jurisdiction to grant Thaler\u2019s patent application. Australia\u2019s federal court initially held that inventors need not be human, but a later decision by the full federal … Continue reading<\/span><\/span><\/span>\u00a0<\/p>\n\n\n\n Addressing a similar question, copyright law has been interpreted not to allow AI to be listed as an author of an artistic work. Although the Copyright Act does not define \u201cauthor,\u201d the Register of Copyrights has identified in its administrative manual that \u201c[t]oqualify as a work of \u2018authorship\u2019 a work must be created by a human being.\u201d[31]<\/sup><\/a>Compendium of U.S. Copyright Office Practices \u00a7 313.2, available at https:\/\/www.copyright.gov\/comp3\/chap300\/ch300-copyrightable-authorship.pdf<\/span><\/a><\/span><\/span> \u201cWorks that do not satisfy this requirement are not copyrightable.\u201d[32]<\/sup><\/a>Id.<\/span><\/span> The Copyright Review Board recently reaffirmed this view of the law when it denied copyright registration for an AI-generated artwork.[33]<\/sup><\/a>https:\/\/www.copyright.gov\/rulings-filings\/review-board\/docs\/a-recent-entrance-to-paradise.pdf<\/span><\/a><\/span><\/span> Court decisions have reached similar holdings that non-humans are not authors for purposes of copyright law.[34]<\/sup><\/a>See, e.g., Naruto v. Slater, 2016 WL 362231, at *4 (N.D. Cal. Jan. 28, 2016), aff\u2019d, 888 F.3d 418 (9th Cir. 2018) (holding that a six-year-old crested macaque \u201cis not an \u2018author\u2019 within the … Continue reading<\/span><\/span><\/span>\u00a0<\/p>\n\n\n\n These legal holdings have intensified the question of who owns AI-generated inventions and artistic works, if the AI system does not meet the legal requirements to be an inventor or author. Is it the company that owns the AI at the time of invention\/creation? The company that originally developed the AI? Or does the invention\/artistic work fall into the public domain with no private owner? These questions are far from only theoretical or academic. For individuals and companies who use and rely on AI technology in their business, the answer to ownership of AI-generated inventions and artistic works may have tremendous impact on the value of their business. <\/p>\n\n\n\n With technology\u2019s pervasiveness in our lives, the reality\u2014whether we like it or not\u2014is that we create trails of data in almost everything we do. <\/p><\/blockquote>\n\n\n\n Quality data is extremely important to AI innovation. In fact, AI depends on data to function. The Economist recently proclaimed, in an article title, that \u201cThe World\u2019s Most Valuable Resource Is No Longer Oil, But Data.\u201d[35]<\/sup><\/a>https:\/\/www.economist.com\/leaders\/2017\/05\/06\/the-worlds-most-valuable-resource-is-no-longer-oil-but-data<\/span><\/a><\/span><\/span> AI systems that provide reports or predictions utilize and analyze large amounts of data to achieve their desired function. Even more importantly, more sophisticated AI systems, such as those making autonomous decisions, depend on data to learn how to differentiate and identify patterns and objects. For example, to train AI software to recognize a picture of a cat, the developer can utilize a large dataset of cat pictures to allow the AI system to learn what a cat picture looks like. Once the AI system has reviewed a sufficient number of pictures, it can rely on its trained algorithm to autonomously recognize a picture of a cat from a group of pictures. Without quality data, however, it would be virtually impossible for sophisticated AI systems to achieve their objectives.[36]<\/sup><\/a>Use of data to train AI does not itself provide ownership or protection for the data. As noted in the above section, whether a party can protect data it uses is a separate question governed by … Continue reading<\/span><\/span><\/span>\u00a0<\/p>\n\n\n\n In some industries, it can be difficult to obtain useful data for AI development. For instance, in developing medical diagnostic software, access to medical imaging datasets can be very limited.[37]<\/sup><\/a>See Edmund L. Andrews, \u201cThe Open-Source Movement Comes to Medical Datasets\u201d (Aug. 2, 2021), https:\/\/hai.stanford.edu\/news\/open-source-movement-comes-medical-datasets<\/span><\/a><\/span><\/span> In many other areas, however, access to data is plentiful\u2014at least for some (often larger) companies. For example, Amazon has access to an immense amount of data on the shopping habits and trends of most Americans. With technology\u2019s pervasiveness in our lives, the reality\u2014whether we like it or not\u2014is that we create trails of data in almost everything we do. Your phone tracks where you go and how long you stay there. Your browser and social media applications track your internet footprints. Your fitness tracker records your health and sleep patterns. And cars not only monitor your driving habits but now check your level of attention to the road.<\/p>\n\n\n\n Abundant access to an increasing amount of user data provides opportunities for tremendous societal benefits. For instance, location data from phones helps find missing persons and solve crimes, internet activity provides convenience in quickly finding relevant information and products, social media posts help support societal movements, digital health devices improve our health and alert us to concerns, and driving data helps reduce accidents and create safer roads. As AI applications become more sophisticated, their impact and potential to improve our society will continue to expand. <\/p>\n\n\n\n However, there is a dark side to constantly sharing data about ourselves. Unfortunately, not all data is used for public good, much less for the benefit of individuals. In fact, much of it is collected for commercial gain. Unchecked, data use in AI algorithms has the potential to hide biases and perpetuate biased decisions without adequate oversight.[38]<\/sup><\/a>See, e.g., Jeffrey Dastin, \u201cAmazon scraps secret AI recruiting tool that showed bias against women\u201d (Oct. 10, 2018), … Continue reading<\/span><\/span><\/span>\u00a0<\/p>\n\n\n\n In addition, everyone has different expectations of privacy because not everyone is willing to share private data with the world, even for the greater good. Have you ever run an internet browser search for a product only to be unsuspectingly spammed with advertisements for the same product minutes later? Such targeted advertising occurs based on data left behind in your internet footprints. Surprising as it may be, the collection and sharing of data often happens behind the scenes, so that people may not even recognize what data they are sharing. Although companies often provide disclosures about the data they collect and how they use it (usually explained in user agreements), not every user takes the time to read those lengthy documents. Those who do may not fully understand them and likely would be powerless to change them.<\/p>\n\n\n\n Even further, some public or self-disclosed data simply requires no user permission. Take into account that data can often be shared and sold\u2014not to mention hacked or stolen\u2014and it becomes nearly impossible to understand how your private data is being used, much less predict where it will go. Since individuals can be reidentified even from \u201canonymized\u201d data, removing identifying information in large datasets offers limited protection. Some companies now offer products that help keep data private,[39]<\/sup><\/a>See, e.g., https:\/\/duckduckgo.com\/<\/span><\/a><\/span><\/span> but much of the control still lies in the companies that collect, store and use the data. Thus, the most meaningful protection for individual privacy will have to come from the laws regulating those companies.\u00a0<\/p>\n\n\n\n In 2018, the European Union passed a comprehensive data protection law: the General Data Protection Regulation (GDPR).[40]<\/sup><\/a>See https:\/\/gdpr.eu\/what-is-gdpr\/<\/span><\/a><\/span><\/span> This regulation applies not just to companies in Europe but to anyone\u2014even those not in the EU\u2014who \u201cprocess the personal data of E.U. citizens or residents, or \u2026 offer goods or services to such people.\u201d[41]<\/sup><\/a>Id.<\/span><\/span> So, if a hotel in Fargo hosts Europeans or a business in Bismarck sells products to Europeans, that venue or company might be subject to the regulation\u2019s requirements. The GDPR provides a \u201ccompliance checklist\u201d for U.S. companies.[42]<\/sup><\/a>https:\/\/gdpr.eu\/compliance-checklist-us-companies\/<\/span><\/a><\/span><\/span> The definition of \u201cpersonal data\u201d under the GDPR is very broad and includes \u201cany information that relates to an individual who can be directly or indirectly identified,\u201d such as \u201c[n]ames and email addresses\u2026 [l]ocation information, ethnicity, gender, biometric data, religious beliefs, web cookies and political opinions.\u201d[43]<\/sup><\/a>Is Our Legal System Ready for AI? <\/h1>\n\n\n\n
AI Liability<\/h1>\n\n\n\n
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Protection of AI Innovation<\/h1>\n\n\n\n
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Privacy of AI Data<\/h1>\n\n\n\n
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