General, cross-sectional research

Current Research Projects
An human-like, artificially intelligent robot holding a tablet.

Intelligent Security Agent (ISA)

Almost every organization worldwide making use of the Internet has faced one or even multiple types of cybercrime in the past. Moreover, those responsible report that security breaches continue to be costlier and more extensive (Ponemon Institute 2019). In addition to financial losses, further negative long-term effects (e.g., loss of reputation or customer trust) complete the fatal consequences such incidents have on organizations. Solely relying on security software and technologies has turned out to be a failed strategy for companies of all kinds over the last decade. The only effective solution, according to security researchers and practitioners alike, is educating and training staff (Matthews 2016).

Against this background, the stated goal of this research project is to develop a support system that helps users to increase their own information security awareness in real-time while at the same time offering appropriate solutions and support by AI-based learning processes. For this purpose, a digital agent, the Intelligent Security Agent (ISA), accompanies the user in everyday digital communication, draws attention to potential threats, and in particular prevents them from causing security breaches by acting carelessly. In general, this research initiative seeks to sensitize employees regarding their communication, hence, the type of information disseminated and the way this information is communicated. The ISA shall raise employees’ awareness by displaying warnings, whenever they are about to communicate passwords or confidential data, thoughtlessly share insights on internal matters with outsiders, and alike.

Principal Investigator: Prof. Dr. Wolfgang König

Research Assistants: Clara Ament, Muriel Frank

 

 

EURHISFIRM

EURHISFIRM will design a world-class research infrastructure (RI) to connect, collect, collate, align, and share detailed, reliable, and standardized long-term company-level data for Europe to enable researchers, policymakers and other stakeholders to analyze, develop, and evaluate effective strategies to promote investment and economic growth. To achieve this goal, EURHISFIRM develops innovative tools to spark a “big data revolution” in the historical social sciences and to open access to cultural heritage in close cooperation with existing RIs.

Our research team will develop European common standards and a process to normalize and map data collected from local sources using those standards. This convergence will encourage the technological development to spark a “big data revolution” in the historical sciences and to push the technological boundaries. 

EURHISFIRM is a part of Horizon 2020, the EU’s largest Research and Innovation initiative to date.

Principal Investigator: Prof. Dr. Wolfgang König

Research Assistants: Lukas Manuel Ranft, Pantelis Karapanagotis

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Algorithmic Discrimination

Algorithms are a central part of today’s modern life. These man-made programmed tools are subject to prejudice, as described in the research work of Sweeney on search engines (2013). A special challenge for politics consists in the regulation of algorithmic discrimination (AD), which could be observed exemplarily in the verdict of the european court of justice (EuGH) of the year 2011. As a result of the binding gender neutrality in the collection of data for health insurances, a price increase was induced for male customers. This again resulted in a general decrease of societal welfare. Furthermore, a lack of transparency of the market structures and the price-setting processes hinder the comprehensibility for consumers on online platforms.

This leads to the following problem for political decision-makers if and to what extent a market intervention may be useful to enable efficient price-setting processes in the sense of society. The goal of this research undertaking is a data-driven analysis of algorithmic discrimination to derive meaningful and suitable recommendations for action for politics and business.

The research methodology includes data acquisition, methods of simulation, as well as the analysis of the collected data through empirical research methods from econometrics, game-theoretical experiments, statistics and machine learning to answer the posed research questions and to derive recommendations for action for politics and business.

Principal Investigator: Prof. Dr. Oliver Hinz

Research Assistants: Nicolas Pfeuffer, Benjamin M. Abdel-Karim

 

 

Sponsors

The following sponsors support efl - the Data Science Institute Frankfurt