A Linguistic Analysis to Quantify Over-Explanation and Under-Explanation in Job Interviews

Receiving insight into the thoughts and feelings of a recruiter is vital to understanding effective job interviews. To ascertain categorical responses and speech patterns, audio and visual data from mock job interviews were collected between interviewees and company representatives. From the study, extracted features of audio and visual data were compiled. As a result, several approaches involving deep learning were leveraged to infer the probability of an over-explained or under-explained snippet of text

The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies

Purpose: The aim of this study is to contribute to the understanding of the power of artificial intelligence (AI) in recruitment and to highlight the opportunities and challenges associated with its use.   Theoretical framework: This paper provides a comprehensive analytical review of current AI-based recruitment strategies, drawing on both academic research and industry reports.   Design/methodology/approach: The paper critically evaluates the potential benefits and drawbacks of using AI in recruitment and assesses the effectiveness of various AI-based recruitment strategies.   Findings: The results indicate that AI-based recruitment strategies such as resume screening, candidate matching, video interviewing, chatbots, predictive analytics, gamification, virtual reality assessments, and social media screening offer significant potential benefits for organizations, including improved efficiency, cost savings, and better-quality hires. However, the use of AI in recruitment also raises ethical and legal concerns, including the potential for algorithmic bias and discrimination.   Research, Practical & Social implications: The study concludes by emphasizing the need for further research and development to ensure that AI-based recruitment strategies are effective, unbiased, and aligned with ethical and legal standards.   Originality/value: The value of the study lies in its comprehensive exploration of AI in recruitment, synthesizing insights from academic and industry perspectives, and assessing the balance of potential benefits against ethical and legal concerns

Slices of Attention in Asynchronous Video Job Interviews

International audienceThe impact of non verbal behaviour in a hiring decision remains an open question. Investigating this question is important, as it could provide a better understanding on how to train candidates for job interviews and make recruiters be aware of influential non verbal behaviour. This research has recently been accelerated due to the development of tools for the automatic analysis of social signals (facial expression detection, speech processing, etc), and the emergence of machine learning methods. However, these studies are still mainly based on hand engineered features, which imposes a limit to the discovery of influential social signals. On the other side, deep learning methods are a promising tool to discover complex patterns without the necessity of feature engineering. In this paper, we focus on studying influential non verbal social signals in asynchronous job video interviews that are discovered by deep learning methods. We use a previously published deep learning system that aims at inferring the hirability of a candidate with regard to a sequence of interview questions. One particularity of this system is the use of attention mechanisms, which aim at identifying the relevant parts of an answer. Thus, information at a fine-grained temporal level could be extracted using global (at the interview level) annotations on hirability. While most of the deep learning systems use attention mechanisms to offer a quick visualization of slices when a rise of attention occurs, we perform an in-depth analysis to understand what happens during these moments. First, we propose a methodology to automatically extract slices where there is a rise of attention (attention slices). Second, we study the content of attention slices by comparing them with randomly sampled slices. Finally, we show that they bear significantly more information for hirability than randomly sampled slices, and that such information is related to visual cues associated with anxiety and turn taking