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Asian American Allyship
No full textGeorge Floyd\u27s tragic death not only sparked numerous nationwide protests decrying the continued violence against Black people, but also resurrected conversations around the complicity of Asian Americans in Black oppression. Just as officer Tou Thao, a Hmong American, stood idly by while a white officer stepped on Floyd\u27s neck, many Asian Americans have taken positions that run contrary to policies that foster inclusion, or what may be termed integrative egalitarianism -- the idea that governmental programs . . . designed to overcome arbitrary inequalities stemming from accidents of birth are a worthwhile investment in society\u27s future. Using the Floyd-Thao narrative as a backdrop, this Essay takes a look at the tensions that underlie economic and social relationships between Asian and Black communities in America and how, in the realm of higher education, longtime conservative activist Edward Blum\u27s recent affirmative action lawsuits are examples of how Asians\u27 mythical model minority status has been weaponized to maintain the status quo, pitting one minority group against another and quashing even modest attempts to provide opportunities for underrepresented groups.This Essay suggests that the way forward can only be achieved by understanding that both Asian and Black Americans have an incentive to seek a more just and equitable society and that both should resist calls to demonize the other. Instead, serious attempts at long0time coalition building between these groups should be facilitated and maintained
How AI Can Learn from the Law: Putting Humans in the Loop Only on Appeal
No full textWhile the literature on putting a “human in the loop” in artificial intelligence (AI) and machine learning (ML) has grown significantly, limited attention has been paid to how human expertise ought to be combined with AI/ML judgments. This design question arises because of the ubiquity and quantity of algorithmic decisions being made today in the face of widespread public reluctance to forgo human expert judgment. To resolve this conflict, we propose that human expert judges be included via appeals processes for review of algorithmic decisions. Thus, the human intervenes only in a limited number of cases and only after an initial AI/ML judgment has been made. Based on an analogy with appellate processes in judiciary decision-making, we argue that this is, in many respects, a more efficient way to divide the labor between a human and a machine. Human reviewers can add more nuanced clinical, moral, or legal reasoning, and they can consider case-specific information that is not easily quantified and, as such, not available to the AI/ML at an initial stage. In doing so, the human can serve as a crucial error correction check on the AI/ML, while retaining much of the efficiency of AI/ML’s use in the decision-making process. In this paper, we develop these widely applicable arguments while focusing primarily on examples from the use of AI/ML in medicine, including organ allocation, fertility care, and hospital readmission
The Development, Implementation, and Oversight of Artificial Intelligence in Health Care: Legal and Ethical Issues
No full textArtificial Intelligence (AI), especially of the machine learning (ML) variety, is used by health care organizations to assist with a number of tasks, including diagnosing patients and optimizing operational workflows. AI products already proliferate the health care market, with usage increasing as the technology matures. Although AI may potentially revolutionize health care, the use of AI in health settings also leads to risks ranging from violating patient privacy to implementing a biased algorithm. This chapter begins with a broad overview of health care AI and how it is currently used. We then adopt a “lifecycle” approach to discussing issues with health care AI. We start by discussing the legal and ethical issues pertaining to how data to build AI are gathered in health care settings, focusing on privacy. Next, we turn to issues in algorithm development, especially algorithmic bias. We then discuss AI deployment to treat patients, focusing on informed consent. Finally, we will discuss existing oversight mechanisms for health AI in the United States: liability and regulation.https://insight.dickinsonlaw.psu.edu/book-contributions/1027/thumbnail.jp
Mapping the Evolution of Legitimacy: Arbitration Clauses in Investment Chapters of American International Free Trade Agreements
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To Limit Air Pollution\u27s Risks: A Law/Science Success Story
No full textThis is the first major study of the National Ambient Air Quality Standards (NAAQS) in a generation. This study focuses on a unique co-evolution of science and law over more than a half-century of development. Our NAAQSs can be found everywhere from your phone’s air quality index to the trends of cardiovascular and respiratory disease in America. Yet no one has studied the ways in which law and the science of air pollution have reciprocally determined each other through them. Through a study of 26 reviews across seven presidencies and 21 reported opinions adjudicating 106 petitions challenging those reviews/revisions, this piece offers firm conclusions about the NAAQS-setting history and what it can teach us about deliberate improvements of the law/science interface