@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{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} }