Markus Langer

• Professor
• Full Professor of Work and Organizational Psychology at University of Freiburg

Expanding research on employment interview training, this study introduces virtual employment interview (VI) training with focus on nonverbal behavior. In VI training, participants took part in a simulated interview with a virtual character. Simultaneously, the computer analyzed participants’ nonverbal behavior and provided real-time feedback for i...

Technologically advanced selection procedures are entering the market at exponential rates. The current study tested two previously held assumptions: (a) providing applicants with procedural information (i.e., making the procedure more transparent and justifying the use of this procedure) on novel technologies for personnel selection would positive...

Digital interviews (or Asynchronous Video Interviews) are a potentially efficient new form of selection interviews, in which interviewees digitally record their answers. Using Potosky's framework of media attributes, we compared them to videoconference interviews. Participants (N = 113) were randomly assigned to a videoconference or a digital inter...

When people interact with novel technologies (e.g., robots, novel technological tools), the word “creepy” regularly pops up. We define creepy situations as eliciting uneasy feelings and involving ambiguity (e.g., on how the behave or how to judge the situation). A common metric for creepiness would help evaluating creepiness of situations and devel...

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 e...

Legislation and ethical guidelines around the globe call for effective human oversight of AI-based systems in high-risk contexts – that is oversight that reliably reduces the risks otherwise associated with the use of AI-based systems. Such risks may relate to the imperfect accuracy of systems (e.g., inaccurate classifications) or to ethical concer...

Organizations, researchers, and software increasingly use automatic speech recognition (ASR) to transcribe speech to text. However, ASR can be less accurate for (i.e., biased against) certain demographic subgroups. This is concerning, given that the machine-learning (ML) models used to automatically score video interviews use ASR transcriptions of...

Design optimizations in human-AI collaboration often focus on cognitive aspects like attention and task load. Drawing on work design literature, we propose that effective human-AI collaboration requires broader consideration of human needs (e.g., autonomy) that affect motivational variables (e.g., meaningfulness). In a simulated drone oversight exp...

Videoconference interviews are now integral to many selection processes. Theoretical arguments and empirical findings suggest that videoconference interviews may lead to different interview performance ratings in comparison to Face-to-Face (FTF) interviews. This has led to the question of the comparability of the psychometric properties of videocon...

Videoconference interviews are now integral to many selection processes. Theoretical arguments and empirical findings suggest that videoconference interviews may lead to different interview performance ratings in comparison to Face‐to‐Face (FTF) interviews. This has led to the question of the comparability of the psychometric properties of videocon...

A central goal of research in explainable artificial intelligence (XAI) is to facilitate human understanding. However, understanding is an elusive concept that is difficult to target. In this paper, we argue that a useful way to conceptualize understanding within the realm of XAI is via certain human abilities. We present four criteria for a useful...

For the integration of artificial intelligence (AI) systems into medical processes it is decisive to address both the trustworthiness of these systems and the trust that physicians and patients have in those systems. Too much trust can result in physicians uncritically relying on this technology, while too little trust may result in physicians not...

Although algorithm-based systems are increasingly used as a decision-support for managers, there is still a lack of research on the effects of algorithm use and more specifically on potential algorithmic bias on decision-makers. To investigate how potential social bias in a recommendation outcome influences trust, fairness perceptions, and moral ju...

This article introduces a framework that is meant to assist in mitigating societal risks that software can pose. Concretely, this encompasses facets of software doping as well as unfairness and discrimination in high-risk decision-making systems. The term software doping refers to software that contains surreptitiously added functionality that is a...

For the integration of artificial intelligence (AI) systems into medical processes it is decisive to address both the trustworthiness of these systems and the trust that physicians and patients have in those systems. Too much trust can result in physicians uncritically relying on this technology, while too little trust may result in physicians not...

This article introduces a framework that is meant to assist in mitigating societal risks that software can pose. Concretely, this encompasses facets of software doping as well as unfairness and discrimination in high-risk decision-making systems. The term software doping refers to software that contains surreptitiously added functionality that is a...

Within the field of Requirements Engineering (RE), the increasing significance of Explainable Artificial Intelligence (XAI) in aligning AI-supported systems with user needs, societal expectations, and regulatory standards has garnered recognition. In general, explainability has emerged as an important non-functional requirement that impacts system...

Designing trustworthy systems and enabling external parties to accurately assess the trustworthiness of these systems are crucial objectives. Only if trustors assess system trustworthiness accurately, they can base their trust on adequate expectations about the system and reasonably rely on or reject its outputs. However, the process by which trust...

Zusammenfassung: Algorithmusbasierte Systeme unterstützen zunehmend Entscheidungen mit zukunftsentscheidenden Konsequenzen. Um positive Aspekte algorithmusbasierter Systeme nutzbar zu machen und gleichzeitig Risiken im Einsatz zu minimieren, wird von verschiedenen Interessensgruppen die Rolle menschlicher Aufsicht betont. Ziel dieses Positionspapie...

We compare modern machine learning techniques to ordinary least squares regression on out-of-sample operational validity, adverse impact, and dropped predictor counts within a common selection scenario: the prediction of job performance from a battery of diverse psychometrically-validated tests. In total, scores from 1.2 billion validation study pa...

Artificial Intelligence and algorithmic technologies support or even automate a large variety of human resource management (HRM) activities. This affects a range of stakeholders with different, partially conflicting perspectives on the opacity and transparency of algorithm-based HRM. In this paper, we explain why opacity is a key characteristic of...

Research has examined trust in humans and trust in automated decision support. Although reflecting a likely realization of decision support in high‐risk tasks such as personnel selection, trust in hybrid human‐automation teams has thus far received limited attention. In two experiments ( N 1 = 170, N 2 = 154) we compare trust, trustworthiness, and...

Dieses Kapitel gibt einen Überblick über das Thema Künstliche Intelligenz (KI) in eignungsdiagnostischen Interviews. Zunächst bieten wir eine kurze Einführung und Entmystifizierung von KI sowie eine Übersicht über verschiedene Arten KI-basierter Systemen (z. B. händisch programmiert, basierend auf maschinellem Lernen). Im nächsten Schritt geht das...

The present study examined how variations in the design of asynchronous video interviews (AVIs) impact important interviewee attitudes, behaviors, and outcomes, including perceived fairness, anxiety, impression management, and interview performance. Using a 2x2 experimental design, we investigated the impact of two common and important design eleme...

Digital life enables situations where people invade other's privacy-sometimes with harmful intentions but often also without such. Given negative effects on victims of privacy invasions, research has examined technical options to prevent privacy-invading behavior (PIB). However, little is known about the sociotechnical environment where PIB occurs....

Automated systems based on artificial intelligence (AI) increasingly support decisions with ethical implications where decision makers need to trust these systems. However, insights regarding trust in automated systems predominantly stem from contexts where the main driver of trust is that systems produce accurate outputs (e.g., alarm systems for m...

The human voice conveys plenty of information about the speaker. A prevalent assumption is that stress-related changes in the human body affect speech production, thus affecting voice features. This suggests that voice data may constitute an easy-to-capture measure of everyday stress levels and can thus serve as an early warning signal of stress-re...

For decades, automation has assisted human labor. Historically, this primarily focused on production and monitoring processes, or on aviation and driver assistance systems. In recent years, however, automation has begun to affect many new domains. Automation now supports decision-making in jurisprudence, medicine, and management, among others. Furt...

Machine learning (ML) approaches, a subfield of artificial intelligence (AI), promise advancements in the field of personnel selection. This chapter introduces ML approaches to personnel selection practitioners and researchers in a non-technical way. We review the empirical research to date, specifically research that has looked at the potentials o...

Machine learning (ML) approaches, a subfield of artificial intelligence (AI), promise advancements in the field of personnel selection. This chapter introduces ML approaches to personnel selection practitioners and researchers in a non-technical way. We review the empirical research to date, specifically research that has looked at the potentials o...

To enhance quality and efficiency of information processing and decision-making, automation based on artificial intelligence and machine learning has increasingly been used to support managerial tasks and duties. In contrast to classical applications of automation (e.g., within production or aviation), little is known about how the implementation o...

In the media, in policy-making, but also in research articles, intelligent systems are referred to as algorithms, artificial intelligence, and computer programs, amongst other terms. We hypothesize that such terminological differences can affect people's perceptions of properties of intelligent systems, people's evaluations of systems in applicatio...

Recently, requirements for the explainability of software systems have gained prominence. One of the primary motivators for such requirements is that explainability is expected to facilitate stakeholders' trust in a system. Although this seems intuitively appealing, recent psychological studies indicate that explanations do not necessarily facilita...

National and international guidelines for trustworthy artificial intelligence (AI) consider explainability to be a central facet of trustworthy systems. This paper outlines a multi-disciplinary rationale for explainability auditing. Specifically, we propose that explainability auditing can ensure the quality of explainability of systems in applied...

The public discussion about trustworthy AI is fueling research on new methods to make AI explainable and fair. However, users may incorrectly assess system trustworthiness and consequently could overtrust untrustworthy systems or undertrust trustworthy systems. In order to understand what determines accurate assessments of system trustworthiness we...

Many companies recruit employees from different parts of the globe, and faking behavior by potential employees is a ubiquitous phenomenon. It seems that applicants from some countries are more prone to faking compared to others, but the reasons for these differences are largely unexplored. This study relates country-level economic variables to faki...

Advances in artificial intelligence are increasingly leading to the automation and augmentation of decision processes in work contexts. Although research originally generally focused upon decision-makers, the perspective of those targeted by automated or augmented decisions (whom we call “second parties”) and parties who observe the effects of such...

Applicants seem to react negatively to artificial intelligence-based automated systems in personnel selection. This study investigates the impact of different pieces of information to alleviate applicant reactions in an automated interview setting. In a 2 (no process information vs. process information) × 2 (no process justification vs. process jus...

Advances in artificial intelligence contribute to increasing automation of decisions. In a healthcare-scheduling context, this study compares effects of decision agents and explanations for decisions on decision-recipients’ perceptions of justice. In a 2 (decision agent: automated vs. human) × 3 (explanation: no explanation vs. equality-explanation...

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these stakeholders' desiderata) in a variety of contexts. However, the literature on XAI is vast, spreads out across mult...

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these stakeholders' desiderata) in a variety of contexts. However, the literature on XAI is vast, spreads out across mult...

Introducing automated systems based on artificial intelligence and machine learning for ethically sensitive decision tasks requires investigating of trust processes in relation to such tasks. In an example of such a task (personnel selection), this study investigates trustworthiness, trust, and reliance in light of a trust violation relating to eth...

Resumes are a ubiquitous first hurdle in hiring processes. Applicants' resume fraud behavior and applicants' reactions to selection methods can therefore influence all subsequent selection stages. In addition to classical resumes, professional social media resumes and blockchain resumes emerge as alternative resume formats. In two online studies, t...

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue tha...

Purpose: Due to recent advancements in Artificial Intelligence, automatic evaluation of job interviews has become an alternative for assessing interviewees. Therefore, questions arise regarding applicant reactions and behavior when algorithms automatically evaluate applicants’ interview responses. This study tests arguments from previous research s...

Purpose The technological evolution of job interviews continues as highly automated interviews emerge as alternative approaches. Initial evidence shows that applicants react negatively to such interviews. Additionally, there is emerging evidence that contextual influences matter when investigating applicant reactions to highly automated interviews....

In this paper, we focus on experience-based role play with virtual agents to provide young adults at the risk of exclusion with social skill training. We present a scenario-based serious game simulation platform. It comes with a social signal interpretation component, a scripted and autonomous agent dialog and social interaction behavior model, and...

Background. Recent research on game-based assessment and training demonstrates growing interest in how individual differences affect game-based outcomes. However, there is still a lack of clarity about the variables that affect important game-based outcomes and issues with measurement approaches regarding these variables (e.g., no validation of sca...

Recent research efforts strive to aid in designing explainable systems. Nevertheless, a systematic and overarching approach to ensure explainability by design is still missing. Often it is not even clear what precisely is meant when demanding explainability. To address this challenge, we investigate the elicitation, specification, and verification...

Gamification is emerging as a method aimed at enhancing instructional contents in educational settings. However, theoretical underpinnings of the proposed effects of gamification are lacking. This paper applies the theory of gamified learning and extends research exploring the benefits of gamification on student learning through the testing effect....

Researchers' careers depend on publishing papers. There are explicit expectations (e.g., paper structure) that affect editors' and reviewers' perceptions of manuscripts and therefore chances of publishing papers that can be easily conveyed in written feedback. However, previous research uncovered that some expectations could be rather implicit, thu...

Purpose: Many employees suffer from stress causing health problems and absenteeism. Early and efficient measurement of stress-related aspects could offer possibilities for stress monitoring and interventions. Previous attempts to capture stress focused on self-report questionnaires or complex measures of biological stress indicators. We introduce a...

Technological advancements in Artificial Intelligence allow the automation of every part of job interviews (information acquisition, information analysis, action selection, action implementation) resulting in highly automated interviews. Efficiency advantages exist, but it is unclear how people react to such interviews (and whether reactions depend...

The Cambridge Handbook of Technology and Employee Behavior - edited by Richard N. Landers February 2019

In case of an applicant shortage, signaling theory and research on interviewer impression management (IM) imply that hiring managers use more IM. To test which kind of IM behavior they apply and whether it indeed influences applicants, participants fulfilled the role of hiring managers and recorded company presentation videos, either assuming an ap...

Applicants from different cultures vary in their self-presentation behavior during job interviews. This study investigates self-presentation behavior in the United Arab Emirates (UAE), the second largest economy in the Arab world. Specifically, it examines self-presentation behavior of applicants from the UAE and compares it to the behavior of Amer...

Algorithmusbasierte Entscheidungen beeinflussen den Alltag zunehmend. Sei es bei Kaufempfehlungen im Online Shopping (Senecal & Nantel, 2004), bei der automatischen Bewertung von Bewerbungsunterlagen (Campion, Campion, Campion & Reider, 2016) oder bei der Diagnose von Krankheiten (Cummins et al., 2013): Algorithmen sind bereits heute allgegenwärtig...