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1. Failing to Grasp our Failure to Grasp Automation Failure

   https://doi.org/10.1177/15553434241228799  

   Chiou, Erin K (January 2024, Journal of Cognitive Engineering and Decision Making)

   This paper discusses three points inspired by Skraaning and Jamieson’s perspective on automation failure: (a) the limitations of the automation failure concept with expanding system boundaries; (b) parallels between the failure to grasp automation failure and the failure to grasp trust in automation; (c) benefits of taking a pluralistic approach to definitions in sociotechnical systems science. While a taxonomy of automation-involved failures may not directly improve our understanding of how to prevent those failures, it could be instrumental for identifying hazards during test and evaluation of operational systems. 

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2. Ductile failure modeling

   https://doi.org/10.1007/s10704-016-0142-6  

   Benzerga, Ahmed Amine; Leblond, Jean-Baptiste; Needleman, Alan; Tvergaard, Viggo (September 2016, International Journal of Fracture)
3. Identification of failure modes from circuit samples with confounded causes of failure

   Hsu, S.-H. Huang (July 2019, IEEE International Symposium on On-Line Testing and Robust System Design)

   Circuits may fail in the field due to a wide variety of failure modes. If there are frequent failures in the field, circuits are returned to the manufacturer, and the causes of failure must be identified. The challenge is that wearout mechanisms are confounded in circuit and system-level failure data. Using such failure data, it is often hard to separate the underlying failure causes without time-consuming and expensive physical failure analysis. To distinguish the wearout mechanisms for each failure sample, we have developed a quick and low-cost methodology using maximum likelihood estimation and probability analysis to determine the origin of the failure distributions, region of error, and sorting accuracy. We apply our methodology to analyze the competing wearout mechanisms in 14nm FinFET ring oscillators, as an example, using simulation. We also consider the problem of Trojan detection. 

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4. Failure-Aware Kidney Exchange

   https://doi.org/10.1287/mnsc.2018.3026  

   Dickerson, John P.; Procaccia, Ariel D.; Sandholm, Tuomas (July 2018, Management Science)
5. Failure-Aware Kidney Exchange

   Dickerson, John P.; Procaccia, Ariel D.; Sandholm, Tuomas (January 2019, Management science)