TY - JOUR A1 - Dhiman, Rachit A1 - Miteff, Sofia A1 - Wang, Yuancheng A1 - Ma, Shih-Chi A1 - Amirikas, Ramila A1 - Fabian, Benjamin T1 - Artificial Intelligence and Sustainability—A Review JF - Analytics N2 - 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. KW - artificial intelligence KW - AI KW - sustainability KW - systematic mapping study Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18651 SN - 2813-2203 VL - 3 IS - 1 SP - 140 EP - 164 PB - MDPI ER - TY - JOUR A1 - Langenberg, Anna A1 - Ma, Shih-Chi A1 - Ermakova, Tatiana A1 - Fabian, Benjamin T1 - Formal Group Fairness and Accuracy in Automated Decision Making JF - Mathematics N2 - 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. KW - AI KW - machine learning KW - automated decision making KW - algorithmic bias KW - metric KW - group fairness Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17323 SN - 2227-7390 VL - 11 IS - 8 PB - MDPI ER -