Leveraging Bias in Forensic Science

Dr. Simon Cole calls for a more hierarchical organization of forensic science in his challenging Article, Acculturating Forensic Science: What is ‘Scientific Culture’, and How can Forensic Science Adopt it? Koppl thinks Dr. Cole is right to say that there are different roles in forensic science, but somewhat mistaken in his call for hierarchy

Using Procedural Justice to Understand, Explain, and Prevent Decision-Making Errors in Forensic Sciences

It has been estimated that in the United States there are 20,000 false felony convictions a year due to deficiencies in the forensic science and criminal justice systems (Koppl, 2010c). As many of these errors can be attributed to flaws in the processes by which forensic science decisions are made, the principles of procedural justice are a useful lens for analyzing these processes and recommending improved practices. In this secondary analysis of current research, decision-making processes in forensic sciences are analyzed using Leventhal’s six criteria for establishing procedural justice. Specifically, we assesses the current state of forensic science, explain how some industry practices may be prone to error and bias, and provide practical suggestions for improving industry practices to better adhere to the principles of procedural justice. In addition, the implications of this analysis for practitioners outside of forensic sciences are discussed

The Walras Paradox

Some standard interpretations of Leon Walras' political economy are challenged by a study of his philosophical and political ideas. Walras thought that his general-equilibrium theory was both a normative scheme and pure science. This "Walras paradox" is resolved by an understanding of the metaphysical ideas he borrowed from the French "eclectic" philosopher Etienne Vacherot. For Walras, general-equilibrium theory provides the foundations for "scientific socialism," A (non-Marxian) synthesis of liberalism and socialism. This interpretation of general-equilibrium theory shows that common ideas about "neoclassical" economics do not fit Walras.

The Law and Big Data

In this Article we critically examine the use of Big Data in the legal system. Big Data is driving a trend towards behavioral optimization and personalized law, in which legal decisions and rules are optimized for best outcomes and where law is tailored to individual consumers based on analysis of past data. Big Data, however, has serious limitations and dangers when applied in the legal context. Advocates of Big Data make theoretically problematic assumptions about the objectivity of data and scientific observation. Law is always theory-laden. Although Big Data strives to be objective, law and data have multiple possible meanings and uses and thus require theory and interpretation in order to be applied. Further, the meanings and uses of law and data are indefinite and continually evolving in ways that cannot be captured or predicted by Big Data. Due to these limitations, the use of Big Data will likely generate unintended consequences in the legal system. Large-scale use of Big Data will create distortions that adversely influence legal decision-making, causing irrational herding behaviors in the law. The centralized nature of the collection and application of Big Data also poses serious threats to legal evolution and democratic accountability. Furthermore, its focus on behavioral optimization necessarily restricts and even eliminates the local variation and heterogeneity that makes the legal system adaptive. In all, though Big Data has legitimate uses, this Article cautions against using Big Data to replace independent legal judgmen

The Law and Big Data

In this Article we critically examine the use of Big Data in the legal system. Big Data is driving a trend towards behavioral optimization and personalized law, in which legal decisions and rules are optimized for best outcomes and where law is tailored to individual consumers based on analysis of past data. Big Data, however, has serious limitations and dangers when applied in the legal context. Advocates of Big Data make theoretically problematic assumptions about the objectivity of data and scientific observation. Law is always theory-laden. Although Big Data strives to be objective, law and data have multiple possible meanings and uses and thus require theory and interpretation in order to be applied. Further, the meanings and uses of law and data are indefinite and continually evolving in ways that cannot be captured or predicted by Big Data. Due to these limitations, the use of Big Data will likely generate unintended consequences in the legal system. Large-scale use of Big Data will create distortions that adversely influence legal decision-making, causing irrational herding behaviors in the law. The centralized nature of the collection and application of Big Data also poses serious threats to legal evolution and democratic accountability. Furthermore, its focus on behavioral optimization necessarily restricts and even eliminates the local variation and heterogeneity that makes the legal system adaptive. In all, though Big Data has legitimate uses, this Article cautions against using Big Data to replace independent legal judgmen

Bespoke scapegoats: Scientific advisory bodies and blame avoidance in the Covid-19 pandemic and beyond

Scholars have not asked why so many governments created ad hoc scientific advisory bodies (ahSABs) to address the Covid-19 pandemic instead of relying on existing public health infrastructure. We address this neglected question with an exploratory study of the US, UK, Sweden, Italy, Poland, and Uganda. Drawing on our case studies and the blame-avoidance literature, we find that ahSABs are created to excuse unpopular policies and take the blame should things go wrong. Thus, membership typically represents a narrow range of perspectives. An ahSAB is a good scapegoat because it does little to reduce government discretion and has limited ability to deflect blame back to government. Our explanation of our deviant case of Sweden, that did not create and ahSAB, reinforces our general principles. We draw the policy inference that ahSAB membership should be vetted by the legislature to ensure broad membership.Comment: 2 tables, 1 figur