Questionable transactions as grounds for legalization: Immigration, illegality, and law

By differentiating between legal and illegal movements, transactions, andpersons, legal prohibitions and law enforcement practices create boundaries between legitimate and illegitimate social spheres. Individuals who are located in an illegitimate domain survive at least in part through unauthorized and quasi-illegal practices. The boundaries between legitimate and illegitimate social domains are, however, permeable, making it possible for individuals who have at one time been deemed illegal to at another time claim legitimacy. This paper examines one context in which such claims are made: deportation hearings in a U.S. immigration court. During deportation hearings, undocumented immigrants' prior involvement in questionable transactions can be deemed an indication of poor moral character or of non-credibility. At the same time, such involvement can be overlooked or reinterpreted in ways that permit an undocumented immigrant to pass from illegality to legality. Close attention to such ``readings' or interpretations reveals that there is a sense in which the proceedings that award and deny legal status are as questionable as other immigration-related transactions.     

Immigrant workers: health, law, and public policy

Immigrant workers are a large segment of the lower echelon of the U.S. labor force, and as many as 3.6 to 6 million of these workers and their families are living in the U.S. illegally. This paper examines who the recent immigrants are: explains why their current situation in the U.S. is an important public health matter; discusses the ethical and policy issues stemming from their health needs and from illegal status; and concludes with a brief look at some implications of the Simpson-Mazzoli Immigration and Reform Act, currently before Congress. The paper suggests that the illegal status of undocumented workers intensifies their health risks; that the immigrants' responsibility for budget short-falls in public services is not as clearcut as frequently assumed; and that legislation aimed at regulating the status of immigrant workers in the U.S. is unlikely to solve many of the central problems.     

Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI

In recent years a substantial literature has emerged concerning bias, discrimination, and fairness in artificial intelligence (AI) and machine learning. Connecting this work to existing legal non-discrimination frameworks is essential to create tools and methods that are practically useful across divergent legal regimes. While much work has been undertaken from an American legal perspective, comparatively little has mapped the effects and requirements of EU law. This Article addresses this critical gap between legal, technical, and organisational notions of algorithmic fairness. Through analysis of EU non-discrimination law and jurisprudence of the European Court of Justice (ECJ) and national courts, we identify a critical incompatibility between European notions of discrimination and existing work on algorithmic and automated fairness. A clear gap exists between statistical measures of fairness as embedded in myriad fairness toolkits and governance mechanisms and the context-sensitive, often intuitive and ambiguous discrimination metrics and evidential requirements used by the ECJ; we refer to this approach as “contextual equality.”This Article makes three contributions. First, we review the evidential requirements to bring a claim under EU non-discrimination law. Due to the disparate nature of algorithmic and human discrimination, the EU's current requirements are too contextual, reliant on intuition, and open to judicial interpretation to be automated. Many of the concepts fundamental to bringing a claim, such as the composition of the disadvantaged and advantaged group, the severity and type of harm suffered, and requirements for the relevance and admissibility of evidence, require normative or political choices to be made by the judiciary on a case-by-case basis. We show that automating fairness or non-discrimination in Europe may be impossible because the law, by design, does not provide a static or homogenous framework suited to testing for discrimination in AI systems.Second, we show how the legal protection offered by non-discrimination law is challenged when AI, not humans, discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes, prejudice) and unintentional biases (e.g. organisational practices or internalised stereotypes) which can act as a signal to victims that discrimination has occurred. Equivalent signalling mechanisms and agency do not exist in algorithmic systems. Compared to traditional forms of discrimination, automated discrimination is more abstract and unintuitive, subtle, intangible, and difficult to detect. The increasing use of algorithms disrupts traditional legal remedies and procedures for detection, investigation, prevention, and correction of discrimination which have predominantly relied upon intuition. Consistent assessment procedures that define a common standard for statistical evidence to detect and assess prima facie automated discrimination are urgently needed to support judges, regulators, system controllers and developers, and claimants.Finally, we examine how existing work on fairness in machine learning lines up with procedures for assessing cases under EU non-discrimination law. A ‘gold standard’ for assessment of prima facie discrimination has been advanced by the European Court of Justice but not yet translated into standard assessment procedures for automated discrimination. We propose ‘conditional demographic disparity’ (CDD) as a standard baseline statistical measurement that aligns with the Court's ‘gold standard’. Establishing a standard set of statistical evidence for automated discrimination cases can help ensure consistent procedures for assessment, but not judicial interpretation, of cases involving AI and automated systems. Through this proposal for procedural regularity in the identification and assessment of automated discrimination, we clarify how to build considerations of fairness into automated systems as far as possible while still respecting and enabling the contextual approach to judicial interpretation practiced under EU non-discrimination law.     

Forensic palynology: why do it and how it works

Forensic palynology has been a law enforcement tool for over 50 years. Forensic palynology is the application of pollen and spores in solving legal issues, either civil or criminal. Pollen and spores can be obtained from an extremely wide range of items, including bodies. Pollen and spores provide clues as to the source of the items and the characteristics of the environments from which the material on them is sourced. Their usefulness lies in a combination of their abundance, dispersal mechanisms, resistance to mechanical and chemical destruction, microscopic size, and morphology. Their often complex morphology allows identification to an individual parent plant taxon that can be related to a specific ecological habitat or a specific scene. Pollen and spore assemblages characterise different environments and scenes and can easily be picked up and transported away from scenes of interest without providing any visual clue to a suspect as to what has occurred. With so many publications and high-profile cases involving forensic palynology and environmental analysis now receiving publicity, the future of this branch of forensic science is assured. Furthermore, with the development of multi-disciplinary approaches to environmental analyses of crime scenes, far more detailed information is now available to law enforcement agencies, enabling them to determine with greater accuracy what may have happened during the commission of criminal activities.     

Loyalties,and why loyalty should be ignored

The use of storytelling in the judgment process is based on the necessary assumption that experience and meaning are universal. In place of recognizing legitimate differences in the interpretation of social experience, jurors more often are compelled to regard unfamiliar story elements or dissonant interpretations as signs of guilt. When key elements in a case are anchored in different social worlds, defendants may be found guilty simply by reason of their social experiences and their communication styles. The important question arising from this state of affairs is whether anything can be done to correct biased judgment of trials.

Securing access to justice means, at a minimum, recognition for the legitimacy—if not the validity—of one's grievances and aspirations.     

From Hyderabad to Harvard: how US law schools make it worthwhile to clerk on India's Supreme Court

Since the 1990s, judges of the Supreme Court of India have hired law clerks to help them perform some of their routine tasks. However, while clerkships on the U.S. Supreme Court are considered very prestigious and are extensively written about, clerkships on India's Supreme Court are considered to be of significantly lower value by the local legal profession and teaching market in India. Instead, ironically, clerkships on the Supreme Court of India are often pursued by students interested in getting an advanced law degree (usually an LL.M.) at a U.S. law school. Relying on interviews conducted with law clerks and interns who have served on the Supreme Court of India, and using India as a case study, this paper argues that ambitious Indian law students are adopting strategies to “Americanize” themselves in order to culturally arbitrage U.S. law schools' misunderstandings of the global legal profession.