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Indian Preference and the Status of American Indians and Alaska Natives in the Federal Service: Employment, Earnings, Authority, and Perceptions of Fairness
No full textPublic administration scholars have largely ignored American Indians and Alaska Natives in their studies of racial disparities in the federal service, despite strong reasons to believe they face discrimination. Using three large federal data sets (the American Community Survey, federal personnel records, and the Federal Employee Viewpoint Survey), we compare the status of American Indians and Alaska Natives in the federal service to both Whites and other minorities. We find that, largely due to Indian Preference, American Indians and Alaska Natives are much more likely than Blacks, Whites, Latinos, and Asians to hold federal jobs, but they are highly concentrated in the Bureau of Indian Affairs and the Indian Health Service, agencies with which they have a sordid and fraught history. This concentration allows them to work in positions that may advance the interests of their communities and increases their probabilities of being supervisors, but it does not prevent them from being among the poorest-paid and least-satisfied employees in the federal service
Galaxy Next Gen., Inc. v. Bradley Ehlert, et. al., Order on Motion for Reconsideration and for Stay of Trial
Get PDFAnibal Torres, et. al. v. Scott Honan, et. al., Order on Motion to Dismiss and Motion for More Definite Statement
Get PDFAI Diversity and the Future of “Fair” Legal AI
Get PDFThe rapid advancement of artificial intelligence (AI) presents a transformative moment for the legal profession. This Article examines the increasing likelihood of AI reshaping the legal practice, highlights the critical issue of bias, and describes how a multisystem approach to AI can assist in mitigating issues of bias and ultimate fairness. This issue is especially crucial when incorporating AI in automating judicial decision-making. An open, multisystem approach serves as a strategy in mitigating potential biases embedded in AI systems. This Article posits that the thoughtful integration of AI, underpinned by a commitment to diversified systems, will ultimately lead to more equitable and effective legal outcomes. This approach does not replace the human element in law but rather augments and extends it, ensuring that AI serves as a tool for enhancing the end objectives of the legal profession
Formalizing Plea Bargaining
No full textThe Cost of Plea Bargains tells the story of plea bargaining\u27s rise, the early roots of the movement to reform pleas, and the work and recommendations of the American Bar Association\u27s Plea bargaining Task Force. In addition to offering readers the complete task force report, the book also offers deeper insights into many of the concepts, concerns, and proposals raised in the task force\u27s findings
