Benefits and Challenges to “Period Policies” – Menstruating Individuals Are Empowered Through Inclusive Dialogue and Advocacy

The governing laws within the United States center the experience of white, cis- gender, able-bodied males and largely ignore the existence and experience of menstruating individuals within the workplace. Despite the progress made since Judy Blume’s watershed novel, Are You There, God? It’s Me, Margaret, the topic of menstruation is still avoided, shamed, stigmatized, and misunderstood. One possibility to advance the rights of menstruating individuals and recognize these cyclical realities is menstrual leave, or “period policies.” The goal of this type of legislation is to address the intersectional indignities of menstrual injustice and normalize periods through more accommodating employment regulations. However, the dialogue surrounding these policies has been limited and risks leaving behind individuals who are already excluded from many workplace protections, compounding the discrimination and disparate treatment experienced.

When crises collide—Policing a pandemic during social unrest

In 2020, the United States was shaken by concurrent crises: the COVID-19 pandemic and protests for racial equality. Both crises present significant challenges for law enforcement. On the one hand, the protests for racial equality drew the public’s attention to the criminal justice system’s disparate treatment of Blacks and other people of colour. On the other hand, the pandemic required the expansion of police duties to enforce public health mandates. To ensure compliance, law enforcement may arrest, detain, and even use force to prevent the transmission of communicable diseases that may have an irreversible impact on human health, such as COVID-19. Policing, however, is at a critical point in America. The government is expanding police powers for the sake of public health; all the while, public indignation about police (ab)uses of power has fuelled calls for its defunding. It is therefore important to explore Americans’ views of policing pandemics during periods of social unrest, focusing on the recognition that socio-economic and racial inequities shape perceptions. The data from this project derives from surveys with Americans on the specific topics of race, policing, racial protests, and COVID-19. The study finds that Americans perceive the police as legitimate overall; however, there are divergences based on race, gender, and marital status. These differences may contribute meaningful insights to the current discourse on police legitimacy in America.

Private Securities Litigation

This chapter focuses on the erratic and unacceptable private securities litigation framework that prevails in the United States. The litigation structure contained in the federal securities acts was based on a different era and is not suitable for today’s securities markets. Although federal legislation has been enacted to address perceived shortcomings on an episodic basis, significant gaps and inconsistencies exist. Likewise, the federal courts, faced with a fractured statutory regimen, frequently have construed the remedial provisions in a wooden and unduly restrictive manner. The consequence of these congressional and judicial actions is a disparate liability framework that lacks sound logic, consistency, and even-handed treatment for plaintiffs and defendants alike. This chapter provides several examples of the inconsistencies and disparate treatment that prevail under the federal securities laws. Thereafter, recommendations for corrective measures are proffered. These proposals, if adopted and effectively implemented, should instill a substantially greater degree of certainty, uniformity, and equity than currently exists.

Insider Trading

This chapter addresses regulation of insider trading in the United States. Uncertainties and inconsistencies prevail in this setting resulting in disparate treatment for similarly situated actors. Other developed countries, while applying many principles of U.S. securities law to their securities markets, have rejected the U.S. approach in the insider trading context. To redress this situation, Congress should enact comprehensive legislation that meaningfully addresses the contours of the insider trading prohibition. Among other mandates, this legislation would: require corporate insiders to provide advance notice of their contemplated transactions in the subject company’s equity securities; bar corporate insiders and other access persons from trading in the subject company’s securities during the interval between the occurrence of a reportable event and the making of a SEC filing (such as a Form 8-K); close loopholes that currently exist with respect to the propriety of insider trading plans; and adopt a comprehensive access approach governing the legality of trading and tipping on the basis of material nonpublic information.

Are Ellipses important for Machine Translation?

This article describes an experiment to evaluate the impact of different types of ellipses discussed in theoretical linguistics on Neural Machine Translation (NMT), using English to Hindi/Telugu as source and target languages. Evaluation with manual methods shows that most of the errors made by Google NMT are located in the clause containing the ellipsis, the frequency of such errors is slightly more in Telugu than Hindi, and the translation adequacy shows improvement when ellipses are reconstructed with their antecedents. These findings not only confirm the importance of ellipses and their resolution for MT, but also hint towards a possible correlation between the translation of discourse devices like ellipses with the morphological incongruity of the source and target. We also observe that not all ellipses are translated poorly and benefit from reconstruction, advocating for a disparate treatment of different ellipses in MT research.

Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling

Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers. However, these platforms can induce unfairness either through an unequal income distribution or disparate treatment of riders. We investigate two methods to reduce forms of inequality in ride-pooling platforms: by incorporating fairness constraints into the objective function and redistributing income to drivers who deserve more. To test these out, we use New York City taxi data to evaluate their performance on both the rider and driver side. For the first method, we find that optimizing for driver fairness out-performs state-of-the-art models in terms of the number of riders serviced, showing that optimizing for fairness can assist profitability in certain circumstances. For the second method, we explore income redistribution as a method to combat income inequality by having drivers keep an $r$ fraction of their income, and contribute the rest to a redistribution pool. For certain values of $r$, most drivers earn near their Shapley value, while still incentivizing drivers to maximize income, thereby avoiding the free-rider problem and reducing income variability. While the first method is useful because it improves both rider and driver-side fairness, the second method is useful because it improves fairness without affecting profitability, and both methods can be combined to improve rider and driver-side fairness.

MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset

The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV (Medical Information Mart for Intensive Care, version IV), the largest publicly available healthcare dataset, and conduct comprehensive analyses of dataset representation bias as well as interpretability and prediction fairness of deep learning models for in-hospital mortality prediction. In terms of interpretability, we observe that (1) the best-performing interpretability method successfully identifies critical features for mortality prediction on various prediction models; (2) demographic features are important for prediction. In terms of fairness, we observe that (1) there exists disparate treatment in prescribing mechanical ventilation among patient groups across ethnicity, gender and age; (2) all of the studied mortality predictors are generally fair while the IMV-LSTM (Interpretable Multi-Variable Long Short-Term Memory) model provides the most accurate and unbiased predictions across all protected groups. We further draw concrete connections between interpretability methods and fairness metrics by showing how feature importance from interpretability methods can be beneficial in quantifying potential disparities in mortality predictors.

Gay = STIs? Exploring gay and lesbian sexual health stereotypes and their implications for prejudice and discrimination

Gay men and lesbian women face health inequities as well as disparate treatment from healthcare providers. Stereotypes surrounding sexual health might contribute to these disparities. In five studies (N=1858), we explored sexual health stereotypes about gay men and lesbian women and their implications in prejudice/discrimination. In Studies 1, 2A, and 2B, we found people explicitly associated gay men with promiscuity and sexually transmitted infections (STIs) more than lesbian women or straight men/women. Implicitly, both gay men and lesbian women were more associated with promiscuity and STIs than straight counterparts. In Studies 3A and 3B, we tested whether these associations have consequences—finding that people express more prejudice and discrimination towards gay men and lesbian women with STIs versus those with non-STIs or straight counterparts with either disease type. Taken together, the current research identifies some psychological factors that may underpin health disparities and healthcare barriers for gay and lesbian people.