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 KW - artificial intelligence 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 - TY - JOUR A1 - Rosendorff, André A1 - Hodes, Alexander A1 - Fabian, Benjamin T1 - Artificial intelligence for last-mile logistics - Procedures and architecture JF - The Online Journal of Applied Knowledge Management (OJAKM) N2 - 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. KW - supply chain management KW - logistics KW - artificial intelligence KW - machine learning KW - business intelligence Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-15586 SN - 2325-4688 VL - 9 IS - 1 SP - 46 EP - 61 PB - The International Institute for Applied Knowledge Management ER - TY - JOUR A1 - Fernholz, Yannick A1 - Ermakova, Tatiana A1 - Fabian, Benjamin A1 - Buxmann, Peter T1 - User-driven prioritization of ethical principles for artificial intelligence systems JF - Computers in Human Behavior: Artificial Humans N2 - 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. KW - artificial intelligence KW - ethics KW - ethical guidelines KW - trustworthy AI KW - requirements prioritization Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18509 SN - 2949-8821 VL - 2 IS - 1 PB - Elsevier ER - 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 -