Jürgen Fleiß

University of Graz

Universitätsstraße 15/G3

8044 Graz, Austria

I am a Senior Lecturer at the Business Analytics and Data Science Center (Bandas-Center) at the University of Graz. I work on the use of explainable artificial intelligence in HR and other business fields, with a focus on bias (reduction) and acceptance of algorithms. My work on biased decisions extends to basic research on how group identity influences behavior in competitive settings. Most recently, I employ data-driven methods in behavioral screens to detect anti-trust violations.

I also, together with Armin Haberl, develop and maintain the software package aTrain, an easy-to-use machine learning based offline tool for easy transcription of audio data. aTrain is used at and recommended by research institutions all over the world.

selected publications

Take the aTrain. Introducing an interface for the Accessible Transcription of Interviews

Armin Haberl, Jürgen Fleiß, Dominik Kowald, and Stefan Thalmann

Journal of Behavioral and Experimental Finance, 2024

Abs

Research in behavioral and experimental finance becomes more multifaceted and the analysis of data from speech interactions more important. This raises the need for technical support for researchers using qualitative data generated from speech interactions. aTrain serves this need and is an open-source, offline transcription tool with a graphical interface for audio data in multiple languages. It requires no programming skills, runs on most computers, operates without internet, and ensures data is not uploaded to external servers. aTrain combines OpenAI’s Whisper transcription models with speaker recognition and provides output that integrates with MAXQDA and ATLAS.ti. Available on the Microsoft Store for easy installation, its source code is also accessible on GitHub. aTrain, designed for speed on local computers, transcribes audio files at 2-3 times the audio duration on mobile CPUs using the highest-accuracy Whisper transcription models. With an entry-level graphics card, this speed improves to 30% of the audio duration.
Detecting resale price maintenance for competition law purposes: Proof-of-concept study using web scraped data

Jan Amthauer, Jürgen Fleiß, Franziska Guggi, and Viktoria H.S.E. Robertson

Computer Law & Security Review, 2023

Abs

Computational antitrust tools can support competition authorities in the detection of antitrust infringements. However, these tools require the availability of suitable data sets in order to produce reliable results. The present proof-of-concept study focuses on the understudied area of resale price maintenance, that is, the fixing of retail prices between manufacturers and retailers. By applying web scraping to price data for washing machines in Austria from a publicly accessibly price comparison website, we compiled a comprehensive data set for a period of nearly three months. Visualised with the help of interactive dashboards, this data was then analysed using various benchmarks in order to determine whether individual washing machine manufacturers and their retailers may be engaging in resale price maintenance. We conclude that the availability of data is a strong driver for research into and the application of computational antitrust tools. If market data were publicly accessible and provided in a more structured format, researchers and competition enforcers could develop ever more refined computational antitrust applications and screens that would, ultimately, help safeguard competition in markets.
The application of AI in digital HRM–an experiment on human decision-making in personnel selection

Christine Dagmar Malin, Jürgen Fleiß, Isabella Seeber, Bettina Kubicek, and 2 more authors

Business Process Management Journal, 2024

Abs

Purpose How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality. Design/methodology/approach This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality. Findings Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality. Originality/value Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.