@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{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} } @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} }