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.@en

Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future.@en

Video blogs (vlogs) are a popular media form for people to present themselves. In case a vlogger would be a job candidate, vlog content can be useful for automatically assessing the candidates traits, as well as potential interviewability. Using a dataset from the CVPR ChaLearn competition, we build a model predicting Big Five personality trait scores and interviewability of vloggers, explicitly targeting explainability of the system output to humans without technical background. We use human-explainable features as input, and a linear model for the systems building blocks. Four multimodal feature representations are constructed to capture facial expression, movement, and linguistic usage. For each, PCA is used for dimensionality reduction and simple linear regression for the predictive model. Our system's accuracy lies in the middle of the quantitative competition chart, while we can trace back the reasoning behind each score and generate a qualitative analysis report per video.@en