Enforcing the Post-Financial Crisis Ban on Abusive Conduct

Government enforcers have long contended with corporate misconduct, from the abuses of corporate power and monopolization of the late 19th and early 20th centuries to the set up to fail products and digital dark patterns of the 21st century. This Article explains the Consumer Financial Protection Bureau’s (CFPB) Policy Statement on Abusive Acts or Practices, which is appended to this article, and its importance in protecting people from business excesses. The statutory prohibition on illegal abusive conduct was a response to the predatory mortgage lending practices that drove the 2007-2008 financial crisis and sought to reach conduct that might not be considered “unfair” or “deceptive.” However, it is rooted in early 20th century attempts to regulate fair dealing. As government enforcers confront new challenges, they can look to a framework of consumer protection that is ingrained in the American tradition – one based on Congress’ understanding of right and wrong and market reality, rather than theoretical economic models. This Article places the prohibition on abusive conduct as part of a history dating back to the common law standards of fair dealing. Congress has long tailored federal prohibitions in response to changes to business practices and given government enforcers new tools to meet new challenges. The Article also discusses the key objectives of the statement, including providing a straight-forward and analytical framework to help enforcers evaluate wrongdoing and promote a visceral understanding of the prohibition. Finally, the Article closes by outlining some key aspects of the policy statement and the public policy concerns motivating them, including condemning conduct that tricks people or exploits unequal bargaining power

Secularism, Religion, and the State in a Time of Global Crisis: Theoretical Reflections on the Work of Abdullahi An-Na\u27im

This Essay presents a primarily theoretical examination of critical aspects of Abdullahi An-Na’im’s body of work. Drawing on my earlier work, the essay describes the current historical moment as one of “crisis globalization,” a normative condition characterized by the rise of authoritarianism and erosion of democracy across the globe, a backlash against religious and other kinds of minorities, as well as by a general sense of existential uncertainty stemming from the impact of climate change, terrorism, and our vulnerability to pandemics like Covid-19. I argue that An-Na’im’s work speaks especially powerfully to several aspects of this new condition. An-Na’im’s theorization and reconceptualization of the relationship between the secular and the religious, and his elaboration on the role of state and society in mediating that relationship, help us think through and grasp the rise of authoritarianism and religious majoritarianism. They also illuminate a path and template for countering these trends, as elaborated in An-Na’im’s articulation of the necessity and challenge of endowing the relationship of the state and religion, and a corresponding idea of the secular, with cultural legitimacy. In the Essay, I also examine these ideas with reference to recent developments in India, the distinct character of whose experience with secularism seems increasingly under threat

The Madness of Jodh Singh: Patriotism and Paranoia in the Ghadar Archives

My paper focuses on Jodh Singh, a marginal figure in the archives of the Ghadar Party, who was arrested for High Treason against the United States for his role in the “Hindu Conspiracy” plots aimed at the British government of India. Incarcerated in a California prison, Singh was moved to a sanatarium on displaying symptoms of insanity. Through a close reading of a web of archival documents and scholarly reflections—at the center of which lies the report of a commission appointed to inquire into his mental condition—I examine the account of the madness of Jodh Singh as a statement about patriotism and paranoia. In engagement with the work of Foucault, Guha, and scholars of the Ghadar movement, I describe how the record of Singh’s experiences indicts the juridical-legal-medical framework of American society as operating on a distinction between legtimate and illegitimate madness. I also examine how Jodh Singh points to the glimmers of a critique of the self-image of the Ghadar Party as a revolutionary movement committed to egalitarian principles. I conclude with a reflection on what Jodh Singh might tell us about the relationship between madness, political aspiration, and the yearning for solidarity

MeditAid:a wearable adaptive neurofeedback-based system for training mindfulness state

A recent interest in interaction design is towards the development of novel technologies emphasizing the value of mindfulness, monitoring, awareness, and self-regulation for both health and wellbeing. Whereas existing systems have focused mostly on relaxation and awareness of feelings, there has been little exploration on tools supporting the self-regulation of attention during mindfulness sitting meditation. This paper describes the design and initial evaluation of MeditAid, a wearable system integrating electroencephalography (EEG) technology with an adaptive aural entrainment for real time training of mindfulness state. The system identifies different meditative states and provides feedback to support users in deepening their meditation. We report on a study with 16 meditators about the perceived strengths and limitations of the MeditAid system. We demonstrate the benefits of binaural feedback in deepening meditative states, particularly for novice meditators

The Case for “Unfair Methods of Competition” Rulemaking

A key feature of antitrust today is that the law is developed entirely through adjudication. Evidence suggests that this exclusive reliance on adjudication has failed to deliver a predictable, efficient, or participatory antitrust regime. Antitrust litigation and enforcement are protracted and expensive, requiring extensive discovery and costly expert analysis. In theory, this approach facilitates nuanced and factspecific analysis of liability and well-tailored remedies. But in practice, the exclusive reliance on case-by-case adjudication has yielded a system of enforcement that generates ambiguity, drains resources, privileges incumbents, and deprives individuals and firms of any real opportunity to participate in the process of creating substantive antitrust rules. It is difficult to quantify this harm. This Essay argues that rulemaking under § 5 of the Federal Trade Commission Act should supplement antitrust adjudication, and that this institutional shift would lower enforcement costs, reduce ambiguity, and facilitate greater democratic participation. We build on existing scholarship to debunk the view that the Federal Trade Commission (FTC) does not have competition rulemaking authority pursuant to the Administrative Procedure Act conferring Chevron deference, and trace legislative history to underscore how Congress designed the FTC to play a unique institutional role. We close by outlining an initial set of factors that should weigh in favor of rulemaking: when there is significant learning from past enforcement and when private litigation would be unlikely. Finally, we pose questions in the context of the FTC’s recent hearings to prompt further discussion on where this unused tool would be most usefu

Using Cross-Loss Influence Functions to Explain Deep Network Representations

As machine learning is increasingly deployed in the real world, it is ever more vital that we understand the decision-criteria of the models we train. Recently, researchers have shown that influence functions, a statistical measure of sample impact, may be extended to approximate the effects of training samples on classification accuracy for deep neural networks. However, prior work only applies to supervised learning setups where training and testing share an objective function. Despite the rise in unsupervised learning, self-supervised learning, and model pre-training, there are currently no suitable technologies for estimating influence of deep networks that do not train and test on the same objective. To overcome this limitation, we provide the first theoretical and empirical demonstration that influence functions can be extended to handle mismatched training and testing settings. Our result enables us to compute the influence of unsupervised and self-supervised training examples with respect to a supervised test objective. We demonstrate this technique on a synthetic dataset as well as two Skip-gram language model examples to examine cluster membership and sources of unwanted bias

A Conversation

A conversation about an essay on future South Asia in Seminar 632, April 201

A Conversation

A conversation about an essay on future South Asia in Seminar 632, April 201