A formulation of death acceptance involving two components, confrontation and integration, is presented and discussed in the light of the literature on death attitudes. A proposed scale (Klug Death Acceptance Scale) to measure the two-component concept is also provided, including data on the scale's reliability and validity, along with some suggested norms. The contribution of this research is the clarification of the theoretical rationale for a multi-dimensional approach to death acceptance, an operationalization of the theory, and a proposed scale to measure it. Additional research on diverse populations, and a variety of measurement approaches, is needed to further establish the reliability and validity of the instrument.

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Most literature in fairness has focused on improving fairness with respect to one single model or one single objective. However, real-world machine learning systems are usually composed of many different components. Unfortunately, recent research has shown that even if each component is "fair", the overall system can still be "unfair" [16]. In this paper, we focus on how well fairness composes over multiple components in real systems. We consider two recently proposed fairness metrics for rankings: exposure and pairwise ranking accuracy gap. We provide theory that demonstrates a set of conditions under which fairness of individual models does compose. We then present an analytical framework for both understanding whether a system's signals can achieve compositional fairness, and diagnosing which of these signals lowers the overall system's end-to-end fairness the most. Despite previously bleak theoretical results, on multiple data-sets-including a large-scale real-world recommender system-we find that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components.Preprint. Under review.