Law Commons^™

A Public Technology Option, Hannah Bloch-Wehba

Faculty Scholarship

Private technology increasingly underpins public governance. But the state’s growing reliance on private firms to provide a variety of complex technological products and services for public purposes brings significant costs for transparency: new forms of governance are becoming less visible and less amenable to democratic control. Transparency obligations initially designed for public agencies are a poor fit for private vendors that adhere to a very different set of expectations.

Aligning the use of technology in public governance with democratic values calls for rethinking, and in some cases abandoning, the legal structures and doctrinal commitments that insulate private vendors from meaningful …

National Telecommunications And Information Administration: Comments From Researchers At Boston University And The University Of Chicago, Ran Canetti, Aloni Cohen, Chris Conley, Mark Crovella, Stacey Dogan, Marco Gaboardi, Woodrow Hartzog, Rory Van Loo, Christopher Robertson, Katharine B. Silbaugh

Faculty Scholarship

These comments were composed by an interdisciplinary group of legal, computer science, and data science faculty and researchers at Boston University and the University of Chicago. This group collaborates on research projects that grapple with the legal, policy, and ethical implications of the use of algorithms and digital innovation in general, and more specifically regarding the use of online platforms, machine learning algorithms for classification, prediction, and decision making, and generative AI. Specific areas of expertise include the functionality and impact of recommendation systems; the development of Privacy Enhancing Technologies (PETs) and their relationship to privacy and data security laws; …

Is Disclosure And Certification Of The Use Of Generative Ai Really Necessary?, Maura R. Grossman, Paul W. Grimm, Daniel G. Brown

Faculty Scholarship

No abstract provided.

Algorithmic Governance From The Bottom Up, Hannah Bloch-Wehba

Faculty Scholarship

Artificial intelligence and machine learning are both a blessing and a curse for governance. In theory, algorithmic governance makes government more efficient, more accurate, and more fair. But the emergence of automation in governance also rests on public-private collaborations that expand both public and private power, aggravate transparency and accountability gaps, and create significant obstacles for those seeking algorithmic justice. In response, a nascent body of law proposes technocratic policy changes to foster algorithmic accountability, ethics, and transparency.

This Article examines an alternative vision of algorithmic governance, one advanced primarily by social and labor movements instead of technocrats and firms. …

Using Artificial Intelligence In The Law Review Submissions Process, Brenda M. Simon

Faculty Scholarship

The use of artificial intelligence to help editors examine law review submissions may provide a way to improve an overburdened system. This Article is the first to explore the promise and pitfalls of using artificial intelligence in the law review submissions process. Technology-assisted review of submissions offers many possible benefits. It can simplify preemption checks, prevent plagiarism, detect failure to comply with formatting requirements, and identify missing citations. These efficiencies may allow editors to address serious flaws in the current selection process, including the use of heuristics that may result in discriminatory outcomes and dependence on lower-ranked journals to conduct …

Content Moderation As Surveillance, Hannah Bloch-Wehba

Faculty Scholarship

Technology platforms are the new governments, and content moderation is the new law, or so goes a common refrain. As platforms increasingly turn toward new, automated mechanisms of enforcing their rules, the apparent power of the private sector seems only to grow. Yet beneath the surface lies a web of complex relationships between public and private authorities that call into question whether platforms truly possess such unilateral power. Law enforcement and police are exerting influence over platform content rules, giving governments a louder voice in supposedly “private” decisions. At the same time, law enforcement avails itself of the affordances of …

The Role Of Data For Ai Startup Growth, James Bessen, Stephen Michael Impink, Lydia Reichensperger, Robert Seamans

Faculty Scholarship

Artificial intelligence (“AI”)-enabled products are expected to drive economic growth. Training data are important for firms developing AI-enabled products; without training data, firms cannot develop or refine their algorithms. This is particularly the case for AI startups developing new algorithms and products. However, there is no consensus in the literature on which aspects of training data are most important. Using unique survey data of AI startups, we find that startups with access to proprietary training data are more likely to acquire venture capital funding.

Ethical Ai Development: Evidence From Ai Startups, James Bessen, Stephen Michael Impink, Lydia Reichensperger, Robert Seamans

Faculty Scholarship

Artificial Intelligence startups use training data as direct inputs in product development. These firms must balance numerous trade-offs between ethical issues and data access without substantive guidance from regulators or existing judicial precedence. We survey these startups to determine what actions they have taken to address these ethical issues and the consequences of those actions. We find that 58% of these startups have established a set of AI principles. Startups with data-sharing relationships with high-technology firms; that were impacted by privacy regulations; or with prior (non-seed) funding from institutional investors are more likely to establish ethical AI principles. Lastly, startups …

When Ai Goes To War: Corporate Accountability For Virtual Mass Disinformation, Algorithmic Atrocities, And Synthetic Propaganda, Jon M. Garon

Faculty Scholarship

No abstract provided.

The Input Fallacy, Talia B. Gillis

Faculty Scholarship

Algorithmic credit pricing threatens to discriminate against protected groups. Traditionally, fair lending law has addressed such threats by scrutinizing inputs. But input scrutiny has become a fallacy in the world of algorithms.

Using a rich dataset of mortgages, I simulate algorithmic credit pricing and demonstrate that input scrutiny fails to address discrimination concerns and threatens to create an algorithmic myth of colorblindness. The ubiquity of correlations in big data combined with the flexibility and complexity of machine learning means that one cannot rule out the consideration of protected characteristics, such as race, even when one formally excludes them. Moreover, using …

Our Brains Beguil'd: Copyright Protection For Ai Created Works, Vicenc Feliu

Faculty Scholarship

No abstract provided.

Algorithms In Business, Merchant-Consumer Interactions, & Regulation, Tabrez Y. Ebrahim

Faculty Scholarship

The shift towards the use of algorithms in business has transformed merchant–consumer interactions. Products and services are increasingly tailored for consumers through algorithms that collect and analyze vast amounts of data from interconnected devices, digital platforms, and social networks. While traditionally merchants and marketeers have utilized market segmentation, customer demographic profiles, and statistical approaches, the exponential increase in consumer data and computing power enables them to develop and implement algorithmic techniques that change consumer markets and society as a whole. Algorithms enable targeting of consumers more effectively, in real-time, and with high predictive accuracy in pricing and profiling strategies. In …

Beyond Algorithms: Toward A Normative Theory Of Automated Regulation, Felix Mormann

Faculty Scholarship

The proliferation of artificial intelligence in our daily lives has spawned a burgeoning literature on the dawn of dehumanized, algorithmic governance. Remarkably, the scholarly discourse overwhelmingly fails to acknowledge that automated, non-human governance has long been a reality. For more than a century, policymakers have relied on regulations that automatically adjust to changing circumstances, without the need for human intervention. This article surveys the track record of self-adjusting governance mechanisms to propose a normative theory of automated regulation.

Effective policymaking frequently requires anticipation of future developments, from technology innovation to geopolitical change. Self-adjusting regulation offers an insurance policy against the …

An Education Theory Of Fault For Autonomous Systems, William D. Smart, Cindy M. Grimm, Woodrow Hartzog

Faculty Scholarship

Automated systems like self-driving cars and “smart” thermostats are a challenge for fault-based legal regimes like negligence because they have the potential to behave in unpredictable ways. How can people who build and deploy complex automated systems be said to be at fault when they could not have reasonably anticipated the behavior (and thus risk) of their tools?

Part of the problem is that the legal system has yet to settle on the language for identifying culpable behavior in the design and deployment for automated systems. In this article we offer an education theory of fault for autonomous systems—a new …

Artificial Financial Intelligence, William Magnuson

Faculty Scholarship

Recent advances in the field of artificial intelligence have revived long-standing debates about what happens when robots become smarter than humans. Will they destroy us? Will they put us all out of work? Will they lead to a world of techno-savvy haves and techno-ignorant have-nots? These debates have found particular resonance in finance, where computers already play a dominant role. High-frequency traders, quant hedge funds, and robo-advisors all represent, to a greater or lesser degree, real-world instantiations of the impact that artificial intelligence is having on the field. This Article will argue that the primary danger of artificial intelligence in …

Information Technology And Firm Employment, James Bessen

Faculty Scholarship

Do firms displace labor with new information technologies such as “artificial intelligence”? It is challenging to distinguish the effects of technology adoption from unobserved productivity and demand shocks. We take a first look at the economic impacts of large custom software investment —“IT spikes”—using a novel methodology to obtain consistent estimates. Following these events, firm employment increases by about 7% and revenues by about 11%. Rather than displace labor, IT spikes increase revenues and markups, implying decreased labor share of output. Moreover, growth is greater for firms that use AI, IT-producing firms, newer firms, and those in the trade, service, …

Gdpr And The Importance Of Data To Ai Startups, James Bessen, Stephen Michael Impink, Lydia Reichensperger, Robert Seamans

Faculty Scholarship

What is the impact of the European Union’s General Data Protection Regime (“GDPR”) and data regulation on AI startups? How important is data to AI product development? We study these questions using unique survey data of commercial AI startups. AI startups rely on data for their product development. Given the scale and scope of their business models, these startups are particularly susceptible to policy changes impacting data collection, storage and use. We find that training data and frequent model refreshes are particularly important for AI startups that rely on neural nets and ensemble learning algorithms. We also find that firms …

Automation In Moderation, Hannah Bloch-Wehba

Faculty Scholarship

This Article assesses recent efforts to encourage online platforms to use automated means to prevent the dissemination of unlawful online content before it is ever seen or distributed. As lawmakers in Europe and around the world closely scrutinize platforms’ “content moderation” practices, automation and artificial intelligence appear increasingly attractive options for ridding the Internet of many kinds of harmful online content, including defamation, copyright infringement, and terrorist speech. Proponents of these initiatives suggest that requiring platforms to screen user content using automation will promote healthier online discourse and will aid efforts to limit Big Tech’s power.

In fact, however, the …

Artificial Intelligence Inventions & Patent Disclosure, Tabrez Y. Ebrahim

Faculty Scholarship

Artificial intelligence (“AI”) has attracted significant attention and has imposed challenges for society. Yet surprisingly, scholars have paid little attention to the impediments AI imposes on patent law’s disclosure function from the lenses of theory and policy. Patents are conditioned on inventors describing their inventions, but the inner workings and the use of AI in the inventive process are not properly understood or are largely unknown. The lack of transparency of the parameters of the AI inventive process or the use of AI makes it difficult to enable a future use of AI to achieve the same end state. While …

Digital Market Perfection, Rory Van Loo

Faculty Scholarship

Google’s, Apple’s, and other companies’ automated assistants are increasingly serving as personal shoppers. These digital intermediaries will save us time by purchasing grocery items, transferring bank accounts, and subscribing to cable. The literature has only begun to hint at the paradigm shift needed to navigate the legal risks and rewards of this coming era of automated commerce. This Article begins to fill that gap first by surveying legal battles related to contract exit, data access, and deception that will determine the extent to which automated assistants are able to help consumers to search and switch, potentially bringing tremendous societal benefits. …

Rise Of The Robot Lawyers?, Milan Markovic

Faculty Scholarship

The advent of artificial intelligence has provoked considerable speculation about the future of the American workforce, including highly educated professionals such as lawyers and doctors. Although most commentators are alarmed by the prospect of intelligent machines displacing millions of workers, this is not so with respect to the legal sector. Media accounts and some legal scholars envision a future where intelligent machines perform the bulk of legal work, and legal services are less expensive and more accessible. This future is purportedly at hand as lawyers struggle to compete with technologically savvy alternative legal service providers.

This Article challenges the notion …

Data-Informed Duties In Ai Development, Frank A. Pasquale

Faculty Scholarship

Law should help direct—and not merely constrain—the development of artificial intelligence (AI). One path to influence is the development of standards of care both supplemented and informed by rigorous regulatory guidance. Such standards are particularly important given the potential for inaccurate and inappropriate data to contaminate machine learning. Firms relying on faulty data can be required to compensate those harmed by that data use—and should be subject to punitive damages when such use is repeated or willful. Regulatory standards for data collection, analysis, use, and stewardship can inform and complement generalist judges. Such regulation will not only provide guidance to …

Automation And Predictive Analytics In Patent Prosecution: Uspto Implications And Policy, Tabrez Y. Ebrahim

Faculty Scholarship

Artificial-intelligence technological advancements bring automation and predictive analytics into patent prosecution. The information asymmetry between inventors and patent examiners is expanded by artificial intelligence, which transforms the inventor-examiner interaction to machine-human interactions. In response to automated patent drafting, automated office-action responses, "cloems" (computer-generated word permutations) for defensive patenting, and machine-learning guidance (based on constantly updated patent-prosecution big data), the United States Patent and Trademark Office (USPTO) should reevaluate patent-examination policy from economic, fairness, time, and transparency perspectives. By conceptualizing the inventor-examiner relationship as a "patenting market," economic principles suggest stronger efficiencies if both inventors and the USPTO have better information …

Data-Centric Technologies: Patent And Copyright Doctrinal Disruptions, Tabrez Y. Ebrahim

Faculty Scholarship

Data-centric technologies create information content that directly controls, modifies, or responds to the physical world. This information content resides in the digital world yet has profound economic and societal impact in the physical world. 3D printing and artificial intelligence are examples of data-centric technologies. 3D printing utilizes digital data for eventual printing of physical goods. Artificial intelligence learns from data sets to make predictions or automated decisions for use in physical applications and systems. 3D printing and artificial intelligence technologies are based on digital foundations, blur the digital and physical divide, and dramatically improve physical goods, objects, products, or systems. …

Law's Halo And The Moral Machine, Bert I. Huang

Faculty Scholarship

How will we assess the morality of decisions made by artificial intelli­gence – and will our judgments be swayed by what the law says? Focusing on a moral dilemma in which a driverless car chooses to sacrifice its passenger to save more people, this study offers evidence that our moral intuitions can be influenced by the presence of the law.

The Business Of Ai Startups, James Bessen, Stephen Michael Impink, Robert Seamans, Lydia Reichensperger

Faculty Scholarship

New machine learning techniques have led to an acceleration of “artificial intelligence” (AI). Numerous papers have projected substantial job losses based on assessments of technical feasibility. But what is the actual impact? This paper reports on a survey of commercial AI startups, documenting rich detail about their businesses and their impacts on their customers. These firms report benefits of AI that are more often about enhancing human capabilities than replacing them. Their applications more often increase professional, managerial, and marketing jobs and decrease manual, clerical, and frontline service jobs. These startups sell to firms of different sizes, in different industries …

The Policy Challenge Of Artificial Intelligence, James Bessen

Faculty Scholarship

New "artificial intelligence" (AI) technology promises to bring dramatic social and economic changes, demanding major policy changes. In intellectual property and antitrust law, AI will exacerbate a damaging trend: across all major sectors of the economy, proprietary information technology is increasing the market dominance of large firms. This trend might not seem like bad news, but it is evidence of a slowdown in the spread of technical knowledge throughout the economy. The result is rising industry concentration, slower productivity growth and growing wage inequality. The key challenge to IP and antitrust policy will be counter this trend yet maintain innovation …

Humans Forget, Machines Remember: Artificial Intelligence And The Right To Be Forgotten, Tiffany Li, Eduard Fosch Villaronga, Peter Kieseberg

Faculty Scholarship

To understand the Right to be Forgotten in context of artificial intelligence, it is necessary to first delve into an overview of the concepts of human and AI memory and forgetting. Our current law appears to treat human and machine memory alike – supporting a fictitious understanding of memory and forgetting that does not comport with reality. (Some authors have already highlighted the concerns on the perfect remembering.) This Article will examine the problem of AI memory and the Right to be Forgotten, using this example as a model for understanding the failures of current privacy law to reflect the …

A Rule Of Persons, Not Machines: The Limits Of Legal Automation, Frank A. Pasquale

Faculty Scholarship

No abstract provided.

Ai And Jobs: The Role Of Demand, James Bessen

Faculty Scholarship

In manufacturing, technology has sharply reduced jobs in recent decades. But before that, for over a century, employment grew, even in industries experiencing rapid technological change. What changed? Demand was highly elastic at first and then became inelastic. The effect of artificial intelligence (AI) on jobs will similarly depend critically on the nature of demand. This paper presents a simple model of demand that accurately predicts the rise and fall of employment in the textile, steel, and automotive industries. This model provides a useful framework for exploring how AI is likely to affect jobs over the next 10 or 20 …