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Markus Langer
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3 records found
1
Psychology Meets Machine Learning
Interdisciplinary Perspectives on Algorithmic Job Candidate Screening
Book chapter
(2018)
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Cynthia C.S. Liem, Markus Langer, Andrew Demetriou, Annemarie M.F. Hiemstra, Sukma Achmadnoer Sukma Wicaksana, Marise Ph. Born, Cornelis J. König
In a rapidly digitizing world, machine learning algorithms are increasingly employed in scenarios that directly impact humans. This also is seen in job candidate screening. Data-driven candidate assessment is gaining interest, due to high scalability and more systematic assessment mechanisms. However, it will only be truly accepted and trusted if explainability and transparency can be guaranteed. The current chapter emerged from ongoing discussions between psychologists and computer scientists with machine learning interests, and discusses the job candidate screening problem from an interdisciplinary viewpoint. After introducing the general problem, we present a tutorial on common important methodological focus points in psychological and machine learning research. Following this, we both contrast and combine psychological and machine learning approaches, and present a use case example of a data-driven job candidate assessment system, intended to be explainable towards non-technical hiring specialists. In connection to this, we also give an overview of more traditional job candidate assessment approaches, and discuss considerations for optimizing the acceptability of technology-supported hiring solutions by relevant stakeholders. Finally, we present several recommendations on how interdisciplinary collaboration on the topic may be fostered.
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In a rapidly digitizing world, machine learning algorithms are increasingly employed in scenarios that directly impact humans. This also is seen in job candidate screening. Data-driven candidate assessment is gaining interest, due to high scalability and more systematic assessment mechanisms. However, it will only be truly accepted and trusted if explainability and transparency can be guaranteed. The current chapter emerged from ongoing discussions between psychologists and computer scientists with machine learning interests, and discusses the job candidate screening problem from an interdisciplinary viewpoint. After introducing the general problem, we present a tutorial on common important methodological focus points in psychological and machine learning research. Following this, we both contrast and combine psychological and machine learning approaches, and present a use case example of a data-driven job candidate assessment system, intended to be explainable towards non-technical hiring specialists. In connection to this, we also give an overview of more traditional job candidate assessment approaches, and discuss considerations for optimizing the acceptability of technology-supported hiring solutions by relevant stakeholders. Finally, we present several recommendations on how interdisciplinary collaboration on the topic may be fostered.
In this paper we investigate the data on 178 launched CubeSats and conduct a nonparametric and parametric analysis, where the dead-on-arrival (DOA) cases as well as the subsystem contribution to failure are specifically addressed. Using Maximum Likelihood Estimation, a Single Weibull and a 2-Weibull mixture parametric model are fitted to the non-parametric data. Furthermore, by combining developers’ beliefs on several reliability aspects from a survey conducted in late 2014 with data from past missions, we make a first attempt to correlate space engineering “best guesses” and intuition to actual data. Finally, the probabilistic CubeSat reliability estimation tool is introduced as a method to reduce the infant mortality of CubeSats: CubeSat developers should be able to estimate their required functional testing time on subsystem and system level at an early project stage, while targeting a desired reliability goal on their CubeSat.
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In this paper we investigate the data on 178 launched CubeSats and conduct a nonparametric and parametric analysis, where the dead-on-arrival (DOA) cases as well as the subsystem contribution to failure are specifically addressed. Using Maximum Likelihood Estimation, a Single Weibull and a 2-Weibull mixture parametric model are fitted to the non-parametric data. Furthermore, by combining developers’ beliefs on several reliability aspects from a survey conducted in late 2014 with data from past missions, we make a first attempt to correlate space engineering “best guesses” and intuition to actual data. Finally, the probabilistic CubeSat reliability estimation tool is introduced as a method to reduce the infant mortality of CubeSats: CubeSat developers should be able to estimate their required functional testing time on subsystem and system level at an early project stage, while targeting a desired reliability goal on their CubeSat.
This paper provides results and conclusions on a survey on the implementation and reliability aspects of CubeSat bus interfaces, with an emphasis on the data bus and power distribution. It provides recommendations for a future CubeSat bus standard. The survey is based on a literature study and a questionnaire representing 60 launched CubeSats and 44 to be launched CubeSats. It is found that the bus interfaces are not the main driver for mission failures. However, it is concluded that the Inter Integrated Circuit (I2C) data bus, as implemented in a great majority of the CubeSats, caused some catastrophic satellite failures and a vast amount of bus lockups. The power distribution may lead to catastrophic failures if the power lines are not protected against overcurrent. A connector and wiring standard widely implemented in CubeSats is based on the PC/104 standard. Most participants find the 104 pin connector of this standard too large. For a future CubeSat bus interface standard, it is recommended to implement a reliable data bus, a power distribution with overcurrent protection and a wiring harness with smaller connectors compared with PC/104.
...
This paper provides results and conclusions on a survey on the implementation and reliability aspects of CubeSat bus interfaces, with an emphasis on the data bus and power distribution. It provides recommendations for a future CubeSat bus standard. The survey is based on a literature study and a questionnaire representing 60 launched CubeSats and 44 to be launched CubeSats. It is found that the bus interfaces are not the main driver for mission failures. However, it is concluded that the Inter Integrated Circuit (I2C) data bus, as implemented in a great majority of the CubeSats, caused some catastrophic satellite failures and a vast amount of bus lockups. The power distribution may lead to catastrophic failures if the power lines are not protected against overcurrent. A connector and wiring standard widely implemented in CubeSats is based on the PC/104 standard. Most participants find the 104 pin connector of this standard too large. For a future CubeSat bus interface standard, it is recommended to implement a reliable data bus, a power distribution with overcurrent protection and a wiring harness with smaller connectors compared with PC/104.