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Young, Black, and Wrongfully Charged: A Cumulative Disadvantage Framework
No full textThe term wrongful conviction typically refers to the conviction or adjudication of individuals who are factually innocent. Decades of research has rightfully focused on uncovering contributing factors of convictions of factually innocent people to inform policy and practice. However, in this paper we expand our conceptualization of wrongful conviction. Specifically, we propose a redefinition that includes other miscarriages of justice: A wrongful conviction is a conviction or adjudication for someone who never should have been involved in the juvenile or criminal legal system in the first place. Although there are various miscarriages of justice that might appropriately be categorized under this reconceptualization, in this paper we focus specifically on those whose system involvement was the result of engaging in no wrongdoing beyond normative adolescent behavior. With this reconceptualization in mind, we highlight how the intersection of youthfulness and race puts youth of color at risk of both wrongful conviction based on factual innocence and wrongful conviction based on criminalization of normative youthful behavior. We rely on a cumulative disadvantage framework to demonstrate how system responses to both youthfulness and race drive youth of color deeper into the legal system and further contribute to their wrongful conviction. In doing so, we describe how the disadvantage created by youthfulness and racial bias compound within and across each stage of system processing. We also illustrate that it is the behavior of youth of color, not white youth, that is disproportionately criminalized. Youth of color are currently and have historically been overrepresented within the juvenile and criminal legal systems; this disparity can be seen even amongst the youngest system-involved youth. We conclude by presenting recommendations for future research efforts to account for multiple system time points, race, and youthfulness. Finally, we describe what we believe to be important considerations for changes to policy and practice that might begin to correct the unnecessary and disproportionate wrongful charging and conviction of youth of color
Ethische und rechtliche Herausforderungen digitaler Medizin in Pandemien
No full texthttps://insight.dickinsonlaw.psu.edu/book-contributions/1011/thumbnail.jp
We Are...Community!
No full textThe concept of “community” has become increasingly important in law schools, relating both to our professionalism and the education of our students. During the recent celebration of our school’s 185th anniversary of its founding, I addressed one of the school’s core values, that of “community”. This article explores that value and its meaning both within our law schools and the greater society, serving to advance the public interest and the interests of our law students, the legal academy and practicing attorneys everywhere. The message conveys is universal and contemporary, going well beyond our anniversary celebration. Ultimately, it can help serve to guide the academy, the profession and students of all law schools to understand what “community” can mean to them
Direct-to-Consumer Medical Machine Learning and Artificial Intelligence Applications
No full textDirect-to-consumer medical artificial intelligence/machine learning applications are increasingly used for a variety of diagnostic assessments, and the emphasis on telemedicine and home healthcare during the COVID-19 pandemic may further stimulate their adoption. In this Perspective, we argue that the artificial intelligence/machine learning regulatory landscape should operate differently when a system is designed for clinicians/doctors as opposed to when it is designed for personal use. Direct-to-consumer applications raise unique concerns due to the nature of consumer users, who tend to be limited in their statistical and medical literacy and risk averse about their health outcomes. This creates an environment where false alarms can proliferate and burden public healthcare systems and medical insurers. While similar situations exist elsewhere in medicine, the ease and frequency with which artificial intelligence/machine learning apps can be used, and their increasing prevalence in the consumer market, calls for careful reflection on how to effectively regulate them. We suggest regulators should strive to better understand how consumers interact with direct-to-consumer medical artificial intelligence/machine learning apps, particularly diagnostic ones, and this requires more than a focus on the system’s technical specifications. We further argue that the best regulatory review would also consider such technologies’ social costs under widespread use
Can Computational Antitrust Succeed
No full textComputational antitrust comes to us at a time when courts and agencies are underfunded and overwhelmed, all while having to apply indeterminate rules to massive amounts of information in fast-moving markets. In the same way that Amazon disrupted e-commerce through its inventory and sales algorithms and TikTok’s progressive recommendation system keeps users hooked, computational antitrust holds the promise to revolutionize antitrust law. Implemented well, computational antitrust can help courts curate and refine precedential antitrust cases, identify anticompetitive effects, and model innovation effects and counterfactuals in killer acquisition cases. The beauty of AI is that it can reach outcomes humans alone cannot define as “good” or “better” as the untrained neural network interrogates itself via the process of trial and error. The maximization process is dynamic, with the AI being capable of scouring options to optimize the best rewards under the given circumstances, 1 mirroring how courts operationalize antitrust policy–computing the expected reward from executing a policy in a given environment. At the same time, any system is only as good as its weakest link, and computational antitrust is no exception. The synergistic possibilities that humans and algorithms offer depend on their interplay. Humans may lean on ideology as a heuristic when they must interpret the rule of reason according to economic theory and evidence. For this reason, it becomes imperative to understand, mitigate, and, where appropriate, harness those biases
Five Approaches to Insuring Cyber Risks
No full textCyber risks are some of the most dangerous risks of the twenty-first century. Many types of businesses, including retail stores, healthcare entities, and financial institutions, as well as government entities, are the targets of cyber attacks. The simple reality is that no computer security system is completely safe. They all can be breached if the hackers are skilled enough and determined. Consequently, the worldwide damages caused by cyber attacks are predicted to reach $10.5 trillion by 2025. Insuring such risks is a monumental task.
The cyber insurance market currently is fragmented with hundreds of insurers selling their own cyber risk insurance policies that cover different types of cyber risks. This means the purchasers of cyber insurance must be experts in both insurance and cyber security in order to make a knowledgeable purchase. And, even knowledgeable purchasers of cyber insurance can only obtain limited coverage for cyber risks. This is because the insurance is sold on a named peril, as opposed to all-risk, basis and the policies contain numerous exclusions. Cyber policies also have relatively low policy limits in comparison to other lines of insurance and the enormity of the risks presented.
This Article explores ways the cyber insurance market could be improved. In doing so, it analyzes the current cyber insurance market, including the history of cyber insurance and the challenges that insuring cyber risks present. The Article then offers five different approaches to insuring cyber risks moving forward that address many of the problems with the current cyber insurance market. Ultimately, the Article concludes the fifth approach, the novel “All-Risk Private-Public” approach, would be the best one