Subtleties in Bayesian decision-theoretic analysis for forensic findings: Notes on recent discussion of the role of validation study data in rational decision making.

This technical note extends a recent discussion in this journal of the role of validation study data in rational decision making. One argument that has been made in this context, using elements of Bayesian decision theory, is that further aggregation of validation study data into error rates involves a loss of information that compromises rational inference and decision making and should therefore be discouraged. This technical note seeks to explain that this argument can be developed at different levels of detail, depending on the definition of the propositions of interest, the forensic findings to be evaluated (and hence the form of the likelihood ratio), and the characterization of the relative desirability of decision consequences.

Polygraph-based deception detection and Machine Learning. Combining the Worst of Both Worlds?

At a time when developments in computational approaches, often associated with the now much-vaunted terms Machine Learning (ML) and Artificial Intelligence (AI), face increasing challenges in terms of fairness, transparency and accountability, the temptation for researchers to apply mainstream ML methods to virtually any type of data seems to remain irresistible. In this paper we critically examine a recent proposal to apply ML to polygraph screening results (where human interviewers have made a conclusion about deception), which raises several questions about the purpose and the design of the research, particularly given the vacuous scientific status of polygraph-based procedures themselves. We argue that in high-stake environments such as criminal justice and employment practice, where fundamental rights and principles of justice are at stake, the legal and ethical considerations for scientific research are heightened.

Unplanned returns to the operating room: a quality improvement methodology for the comparison of institutional outcomes to national benchmarks.

Objective: Unplanned returns to the operating room (RORs) constitute an important quality metric in surgical practice. In this study, the authors present a methodology to compare a department's unplanned ROR rates with national benchmarks in the context of large-scale quality of care surveillance.

Methods: The authors identified unplanned RORs within 30 days from the initial surgery at their institution during the period 2014-2018 using an institutional documentation platform that facilitates the collection of reoperation information by providers in the clinical setting.