Markus Langer
Biographic Data
| ID | 4626224 |
|---|---|
| NAME | Markus Langer |
| GIVEN NAMES | Markus |
| FAMILY NAME | Langer |
| SIGNATURE | LANGER M |
| AFFILIATIONS | Saarland University |
| ORCID | 0000-0002-8165-1803 |
| VERIFIED | Yes |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 11 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Trust the Explanation or my Expectation? Effects of Output Accuracy and Explanations on Expectation Violations and Trust in AI-Supported Decisions
Inaccurate AI outputs led to expectation violations. • Expectation violations mediated the effects of AI output accuracy on trust. • Explanations did not moderate the link between accuracy and expectation violations. • For inaccurate AI outputs, explanations led to more trusting behavior. Systems based on Artificial Intelligence (AI) increasingly support decision-making, but their outputs may be inaccurate. Prior research has suggested that expla…
Personalizing explanations in AI-based decisions
The increasing reliance on AI-based decision-making in high-stakes contexts underscores the need for transparency and justice. Here, negative outcomes drive individuals affected by AI-based decisions to seek actionable explanations that enable them to realize what they can do to achieve a better future outcome. However, actionability is subjective, varying across individuals and contexts. Personalization of explanations has been proposed to addre…
How do we assess the trustworthiness of AI? Introducing the trustworthiness assessment model (TrAM)
Designing trustworthy AI-based 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 trustors assess a system's actual trustworthiness to arrive at their perceived trustworthiness r…
Conceptualizing understanding in explainable artificial intelligence (XAI)
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 conceptualization of understanding in XAI and show that these are fulfilled by an abilities-based …
A quasi‐experimental investigation of differences between face‐to‐face and videoconference interviews in an actual selection process
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 videoconferences and FTF interviews. However, evidence from actual selection processes stems from the beginn…
What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
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 multiple largely disconnected disciplines, and it often remains unclear how explainability approache…
Resume = Resume? The effects of blockchain, social media, and classical resumes on resume fraud and applicant reactions to resumes
What to expect from opening up ‘black boxes’? Comparing perceptions of justice between human and automated agents
The future of artificial intelligence at work
Economic Predictors of Differences in Interview Faking Between Countries
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 faking behavior in hiring processes. In a cross‐national study across 20 countries, participants ( N = 3…
Gamification in the classroom
No prominent works on this page.
Gamification in the classroom
What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
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 multiple largely disconnected disciplines, and it often remains unclear how explainability approache…
Resume = Resume? The effects of blockchain, social media, and classical resumes on resume fraud and applicant reactions to resumes
What to expect from opening up ‘black boxes’? Comparing perceptions of justice between human and automated agents
The future of artificial intelligence at work
Economic Predictors of Differences in Interview Faking Between Countries
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 faking behavior in hiring processes. In a cross‐national study across 20 countries, participants ( N = 3…
Conceptualizing understanding in explainable artificial intelligence (XAI)
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 conceptualization of understanding in XAI and show that these are fulfilled by an abilities-based …
A quasi‐experimental investigation of differences between face‐to‐face and videoconference interviews in an actual selection process
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 videoconferences and FTF interviews. However, evidence from actual selection processes stems from the beginn…
How do we assess the trustworthiness of AI? Introducing the trustworthiness assessment model (TrAM)
Designing trustworthy AI-based 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 trustors assess a system's actual trustworthiness to arrive at their perceived trustworthiness r…
Trust the Explanation or my Expectation? Effects of Output Accuracy and Explanations on Expectation Violations and Trust in AI-Supported Decisions
Inaccurate AI outputs led to expectation violations. • Expectation violations mediated the effects of AI output accuracy on trust. • Explanations did not moderate the link between accuracy and expectation violations. • For inaccurate AI outputs, explanations led to more trusting behavior. Systems based on Artificial Intelligence (AI) increasingly support decision-making, but their outputs may be inaccurate. Prior research has suggested that expla…
Personalizing explanations in AI-based decisions
The increasing reliance on AI-based decision-making in high-stakes contexts underscores the need for transparency and justice. Here, negative outcomes drive individuals affected by AI-based decisions to seek actionable explanations that enable them to realize what they can do to achieve a better future outcome. However, actionability is subjective, varying across individuals and contexts. Personalization of explanations has been proposed to addre…
Computer Science (7 works) · Psychology (7 works) · Ethics and Social Impacts of AI (6 works) · Explainable Artificial Intelligence (XAI (5 works) · Social Psychology (5 works) · Artificial Intelligence (4 works) · Artificial Intelligence in Healthcare and Education (4 works) · Economics (3 works) · Human-Automation Interaction and Safety (3 works) · Knowledge management (3 works)