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I. Introduction 100 II. The Current Environmental Compliance Process and Its Shortcomings 103 III. Current Uses of Technology and Big Data by EPA 107 IV. Innovative Uses of AI for [...], The environmental compliance process has always been a significant undertaking by the United States Environmental Protection Agency (EPA). Engrained in the process is the call on the agency to use technology to foster more complete compliance with the nation's environmental statutes. While these statutes were enacted during a "data-starved" time, engrained in them are technology-forcing mandates that have prompted exponential gains in the quality of the natural environment. Now, however, EPA faces new challenges to facilitate even greater gains during a time when the agency's resources are dwindling. This Essay analyzes how EPA has turned to technology to facilitate environmental compliance, both today and for applications in the future. The focus of this Essay is on artificial intelligence (AT) and machine learning, though the discussion also touches on more overarching forms of technology use, such as data analytics. Part II starts with a discussion of how the current environmental compliance process plays out, noting the under-compliance problem that is a consequence of EPA balancing a decrease in resources with the agency's increasing regulatory responsibilities. Part III explains how EPA currently uses technology and big data as a means to mitigate the problems discussed in Part II. For example, the agency's Next Generation Compliance initiative involves emissions technology and electronic reporting components that serve as a benchmark for how the agency will use technology moving forward. Part IV concludes with a discussion of novel ways EPA can use AI to facilitate compliance in the future. This Part highlights two recent studies that used machine learning to predict noncompliance risk and to identify facilities that require a certain environmental permit but are currently operating without one. Problems with these future applications are also discussed, including data accuracy issues and systematic biases that may be inherent in the data used.

TABLE OF CONTENTS I INTRODUCTION 290 II AI IN THE FINANCIAL MARKETS 293 A Uses of AI in the Financial Markets 294 1 Consumer Finance 294 2 High-Speed Trading 297 [...], Consumer interaction with the financial market ranges from applying for credit cards, to financing the purchase of a home, to buying and selling securities. And with each transaction, the lender, bank, and brokerage firm are likely utilizing artificial intelligence (AI) behind the scenes to augment their operations. While AI's ability to process data at high speeds and in large quantities makes it an important tool for financial institutions, it is imperative to be attentive to the risks and limitations that accompany its use. In the context of financial markets, AI's lack of decision-making transparency, often called the "black box problem," along with AI's dependence on quality data, present additional complexities when considering the aggregate effect of algorithms deployed in the market. Owing to these issues, the benefits of AI must be weighed against the particular risks that accompany the spread of this technology throughout the markets. Financial regulation, as it stands, is complex, expensive, and often involves overlapping regulations and regulators. Thus far, financial regulators have responded by publishing guidance and standards for firms utilizing AI tools, but they have stopped short of demanding access to source codes, setting specific standards for developers, or otherwise altering traditional regulatory frameworks. While regulators are no strangers to regulating new financial products or technology, fitting AI within the traditional frameworks of prudential regulation, registration requirements, supervision, and enforcement actions leaves concerning gaps in oversight. This Article examines the suitability of the current financial regulatory frameworks for overseeing AI in the financial markets. It suggests that regulators consider developing multi-faceted approaches to promote AI accountability. This Article recognizes the potential harms and likelihood for regulatory arbitrage if these regulatory gaps remain unattended and thus suggests focusing on key elements for future regulation--namely, the human developers and regulation of data to truly "hold AI accountable." Therefore, holding AI accountable requires identifying the different ways in which sophisticated algorithms may cause harm to the markets and consumers if ineffectively regulated, and developing an approach that can flexibly respond to these broad concerns. Notably, this Article cautions against reliance on self-regulation and recommends that future policies take an adaptive approach to address current and future AI technologies.

Introduction I. The COVID-19 Justice Transformation II. AI Legal Help III. Opportunities and Challenges Conclusion Introduction According to a recent Organization for Economic Co-operation and Development (OECD) study, half of [...], The authors argue that the digital transformation toward remote justice in response to the COVID-19 pandemic was not a paradigm shift; the root of this transition lies in the long-standing access to justice problem which was exacerbated, not caused, by the COVID-19 pandemic. The authors suggest that the role of technology in access to justice is much greater than simply a digitization of long-standing practices. Rather, technological innovations in the legal field provide opportunities to improve access to legal representation and to refine court processes. Non-state initiatives, such as MyOpenCourt, can help alleviate the gaps in access to justice. Long term, the authors suggest that using direct-to-public (D TP) tools, such as legal assistance systems powered by artificial intelligence (AI), can help push toward their vision of a consistent global system of online dispute resolution. However, the use of DTP tools also raises concerns regarding privacy, security, and the unauthorized practice of law. In light of this, the authors call for greater research on the legality of DTP AI tools.