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Antitrust enforcers now recognize the risks associated with many sellers' use of a singular hub's pricing algorithm. But what if many rivals use several different hubs for dynamic pricing? The common assumption is that in such instances, competition among the pricing hubs would support competition among the sellers. This Article, however, argues differently and instead introduces the concept of secondary algorithmic tacit collusion, which leads to anticompetitive effects independent of primary market conditions. This phenomenon may lead to the evils of price fixing but on a far wider scale. Contrary to traditional tacit collusion, this aggregated form of collusion--via algorithmic hub-and-spoke structures--can occur in markets with many competitors and with seemingly competitive dynamics. This Article outlines how the combination of hub-and-spoke frameworks on the primary market and conscious parallelism on the secondary market for algorithmic pricing services can lead to secondary tacit collusion, and what the agencies and courts can do to prevent this harm., TABLE OF CONTENTS I. INTRODUCTION 462 II. ALGORITHMIC ALIGNMENT 468 A. Tacit Algorithmic Collusion 468 B. Algorithmic Hub-and-Spoke 473 C. Difference Between Hub-and-Spoke and Tacit Algorithmic 477 Collusion III. SECONDARY [...]

According to the sole normative foundation for trademark protection--'search costs' theory--trademarks transmit useful information to consumers, enabling an efficient marketplace. The marketplace, however, is in the midst of a fundamental change. Increasingly, retail is virtual, marketing is data-driven, and purchasing decisions are automated by AI. Predictive analytics are changing how consumers shop. Search costs theory no longer accurately describes the function of trademarks in this marketplace. Consumers now have numerous digital alternatives to trademarks that more efficiently provide them with increasingly accurate product information. Just as store shelves are disappearing from consumers' retail experience, so are trademarks disappearing from their product search. Consumers may want to buy a product where the brand is the essential feature of the product such that the brand is the product, but they no longer need the assistance of a trademark to find the product. By reflexively continuing to protect trademarks in the name of search costs theory, courts give only lip service to consumer interests without questioning whether trademarks are fulfilling any useful information function. In many cases, trademarks may actually misinform consumers by masking the identity of the producer or its distanced relationship with the trademark owner. Without having deliberately decided to do so, trademark law is now protecting 'brands as property' without any supportive normative rationale. Removing the veil of search costs theory will enable courts to consider whether trademark protection is justified in particular cases., INTRODUCTION I. THE NORMATIVE RATIONALE FOR TRADEMARKS A. Why Protect Trademarks? B. Trademarks Convey Source of Origin Information to Consumers C. Trademarks Reduce Consumers' Search Costs II. UNDERMINING TRADEMARK'S RATIONALE [...]

Credit is a crucial determinant of financial success for most US consumers, but not all consumers can access it. This financial exclusion is partially due to traditional credit-risk scoring and approval processes that cannot assess the creditworthiness of 'credit invisible' or 'thin file' consumers--that is, consumers who do not have enough traditional data depicting their financial payment history. Consequently, some consumer-reporting agencies and lenders turn to alternative data credit-scoring systems as a way to increase financial inclusion. The enormous complexity of these alternative consumer credit-scoring systems, however, raises significant accuracy and transparency issues--most of which stem from their secret, legally protected status--as well as heightened concerns over the use of discriminatory and biased scoring practices using nontraditional behavioral data. If these issues are not addressed, alternative data-driven credit-scoring systems can potentially amplify transparency and discrimination issues, preventing consumers from understanding the factors that impact their credit scores. At the same time, they can position underprivileged groups to face increased discrimination in terms of both accessing credit and receiving favorable interest rates. This Note proposes four regulatory solutions and suggests enhancements to the Model Fairness and Transparency in Credit Scoring Act developed by legal and technology scholars Hurley and Adebayo. The current regulatory framework can better address discrimination by requiring lenders to disclose how they define 'creditworthiness' so that consumers can gain a better understanding of the standards to which they are being held. It can also push lenders to foster more appropriate credit standards. Moreover, federal legislation is needed to curtail or prohibit the use of nontraditional behavioral data, especially data derived from a consumer's social networks, which can unfairly penalize consumers for their social or cultural associations. If this type of legislation is not likely to pass at a federal level, then regulatory agencies should regulate these firms under the presumption that behavioral data is inherently discriminatory until proven otherwise. Finally, regulators should seek to incentivize firms using alternative credit scoring methodologies to seek no-action letters., TABLE OF CONTENTS I. TRADITIONAL VERSUS ALTERNATIVE DATA IN CREDIT RISK SCORING 628 A. How Consumer Credit Scores Are Used 629 B. The Purpose of Using Alternative Data for Consumer [...]

Algorithms have become increasingly common, and with this development, so have algorithms that approximate human speech. This has introduced new issues with which courts and legislators will have to grapple. Courts in the United States have found that search engine results are a form of speech that is protected by the Constitution, and cases in Europe concerning liability for autocomplete suggestions have led to varied results. Beyond these instances, insight into how courts handle algorithmic speech are few and far between. By focusing on three categories of algorithmic speech, defined as curated production, interactive/responsive production, and semi-autonomous production, this Article analyzes these various forms of algorithmic speech within the international framework for freedom of expression. After a brief introduction of that framework and a look towards approaches to algorithmic speech in the United States, the Article then examines whether the creators or controllers of different forms of algorithms should be considered content providers or mere intermediaries, the determination of which ultimately has implications for liability, which is also explored. The Article then looks at possible interferences with algorithmic speech, and how such interferences may be examined under the three-part test--particular attention is paid to the balancing of rights and interests at play--in order to answer the question of the extent to which algorithmic speech is worthy of protection under international standards of freedom of expression. Finally, other relevant issues surrounding algorithmic speech are discussed that will have an impact going forward, many of which involve questions of policy and societal values that accompany granting algorithmic speech protection., TABLE OF CONTENTS I. INTRODUCTION 1328 A. Background 1328 B. What is Algorithmic Speech? 1331 II. ALGORITHMIC SPEECH AND THE SUBSTANTIVE SCOPE OF FREEDOM OF EXPRESSION 1335 A. Frameworks for [...]

A hash search is a very accurate, computationally efficient technique for testing whether a computer contains illicit material. Although police have been running hash searches for many years, case law [...]

One of the central questions in free speech jurisprudence is what activities the First Amendment encompasses. This Article considers that question in the context of an area of increasing importance--algorithm-based [...]