@article{LangenbergMaErmakovaetal.2023, author = {Langenberg, Anna and Ma, Shih-Chi and Ermakova, Tatiana and Fabian, Benjamin}, title = {Formal Group Fairness and Accuracy in Automated Decision Making}, series = {Mathematics}, volume = {11}, journal = {Mathematics}, number = {8}, publisher = {MDPI}, issn = {2227-7390}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17323}, year = {2023}, abstract = {Most research on fairness in Machine Learning assumes the relationship between fairness and accuracy to be a trade-off, with an increase in fairness leading to an unavoidable loss of accuracy. In this study, several approaches for fair Machine Learning are studied to experimentally analyze the relationship between accuracy and group fairness. The results indicated that group fairness and accuracy may even benefit each other, which emphasizes the importance of selecting appropriate measures for performance evaluation. This work provides a foundation for further studies on the adequate objectives of Machine Learning in the context of fair automated decision making.}, language = {en} } @article{RosendorffHodesFabian2021, author = {Rosendorff, Andr{\´e} and Hodes, Alexander and Fabian, Benjamin}, title = {Artificial intelligence for last-mile logistics - Procedures and architecture}, series = {The Online Journal of Applied Knowledge Management (OJAKM)}, volume = {9}, journal = {The Online Journal of Applied Knowledge Management (OJAKM)}, number = {1}, publisher = {The International Institute for Applied Knowledge Management}, issn = {2325-4688}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-15586}, pages = {46 -- 61}, year = {2021}, abstract = {Artificial Intelligence (AI) is becoming increasingly important in many industries due to its diverse areas of application and potential. In logistics in particular, increasing customer demands and the growth in shipment volumes are leading to difficulties in forecasting delivery times, especially for the last mile. This paper explores the potential of using AI to improve delivery forecasting. For this purpose, a structured theoretical solution approach and a method for improving delivery forecasting using AI are presented. In doing so, the important phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, a standard process for data mining, are adopted and discussed in detail to illustrate the complexity and importance of each task such as data preparation or evaluation. Subsequently, by embedding the described solution into an overall system architecture for information systems, ideas for the integration of the solution into the complexity of real information systems for logistics are given.}, language = {en} } @article{FernholzErmakovaFabianetal.2024, author = {Fernholz, Yannick and Ermakova, Tatiana and Fabian, Benjamin and Buxmann, Peter}, title = {User-driven prioritization of ethical principles for artificial intelligence systems}, series = {Computers in Human Behavior: Artificial Humans}, volume = {2}, journal = {Computers in Human Behavior: Artificial Humans}, number = {1}, publisher = {Elsevier}, issn = {2949-8821}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18509}, year = {2024}, abstract = {Despite the progress of Artificial Intelligence (AI) and its contribution to the advancement of human society, the prioritization of ethical principles from the viewpoint of its users has not yet received much attention and empirical investigations. This is important to develop appropriate safeguards and increase the acceptance of AI-mediated technologies among all members of society. In this research, we collected, integrated, and prioritized ethical principles for AI systems with respect to their relevance in different real-life application scenarios. First, an overview of ethical principles for AI was systematically derived from various academic and non-academic sources. Our results clearly show that transparency, justice and fairness, non-maleficence, responsibility, and privacy are most frequently mentioned in this corpus of documents. Next, an empirical survey to systematically identify users' priorities was designed and conducted in the context of selected scenarios: AI-mediated recruitment (human resources), predictive policing, autonomous vehicles, and hospital robots. We anticipate that the resulting ranking can serve as a valuable basis for formulating requirements for AI-mediated solutions and creating AI algorithms that prioritize user's needs. Our target audience includes everyone who will be affected by AI systems, e.g., policy makers, algorithm developers, and system managers as our ranking clearly depicts user's awareness regarding AI ethics.}, language = {en} } @article{DhimanMiteffWangetal.2024, author = {Dhiman, Rachit and Miteff, Sofia and Wang, Yuancheng and Ma, Shih-Chi and Amirikas, Ramila and Fabian, Benjamin}, title = {Artificial Intelligence and Sustainability—A Review}, series = {Analytics}, volume = {3}, journal = {Analytics}, number = {1}, publisher = {MDPI}, issn = {2813-2203}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18651}, pages = {140 -- 164}, year = {2024}, abstract = {In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI's potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper critically reviews the evolving landscape of AI sustainability, addressing economic, social, and environmental dimensions. The literature is systematically categorized into "Sustainability of AI" and "AI for Sustainability", revealing a balanced perspective between the two. The study also identifies a notable trend towards holistic approaches, with a surge in publications and empirical studies since 2019, signaling the field's maturity. Future research directions emphasize delving into the relatively under-explored economic dimension, aligning with the United Nations' Sustainable Development Goals (SDGs), and addressing stakeholders' influence.}, language = {en} } @inproceedings{OjoWalther2025, author = {Ojo, Emil and Walther, Mathias}, title = {Emotion Perception in AI-Generated Facial Expressions}, series = {Wildauer Konferenz f{\"u}r K{\"u}nstliche Intelligenz 2025 (WiKKI25)}, booktitle = {Wildauer Konferenz f{\"u}r K{\"u}nstliche Intelligenz 2025 (WiKKI25)}, editor = {Reiff-Stephan, J{\"o}rg and Beuster, Anja}, publisher = {TIB Open Publishing}, address = {Hannover}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-20799}, year = {2025}, abstract = {This study analyses whether facial expressions in generated images are perceived in the same way as in real images. It examines the perception of emotional intensity and sympathy, the ability to recognise emotions, and the distinction between real and generated images. To answer these questions, an online survey was conducted with more than 200 participants. The results show slight to moderate differences in perception between generated and real images, but it also becomes clear that the distinction between the two is becoming increasingly difficult.}, language = {de} } @article{HeroldRoedenbeck2025, author = {Herold, Marcel and Roedenbeck, Marc}, title = {AI-Driven Research in the Recruitment and Selection Process: Application of an AI Taxonomy With a Systematic Literature Review}, series = {SAGE Open}, volume = {15}, journal = {SAGE Open}, number = {3}, publisher = {Sage}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-20736}, year = {2025}, abstract = {A review of the literature on the application of artificial intelligence (AI) in the recruitment and selection process (RSP) was conducted, but no relevant studies were identified. While several reviews have focussed on AI in human resource management in general, none of these have examined the RSP in detail or employed an AI taxonomy for clustering. Consequently, we applied an AI taxonomy identified in the literature with the aim to identify the stages of the RSP in the focus of research and the algorithms mostly used. We conducted a systematic literature review underpinned by a concept matrix, complemented by a computational literature review (CLR), that employed natural language processing (NLP). The initial 4,579 studies were sourced from three databases and narrowed down to a total of 502. Our major findings indicate that the majority of studies were categorised under the stages "assessment \& selection" and "processing incoming applications" in the RSP. The predominant algorithms in use pertain to the field of NLP and machine learning. The CLR emphasised the significance of ethics in AI research. While our study has expanded the general AI taxonomy by incorporating an ethical perspective and is one of the studies with the most articles used to reflect this topic, it is solely focussing on describing the past. Nevertheless, this article helps to align research on exploring and testing alternative approaches with those most frequently used.}, language = {en} }