Titlebook: Responsible Artificial Intelligence; Challenges for Susta René Schmidpeter,Reinhard Altenburger Book 2023 The Editor(s) (if applicable) and

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Developing Responsible AI Business Model,latively during 2011–2020. Data is one of the precious resources in today’s time and cannot be undermined (Antonio Neri, March 2020).?Building and developing data and artificial intelligence & machine learning businesses that are based on Responsible Artificial Intelligence (AI) business models are

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ESG Fingerprint: How Big Data and Artificial Intelligence Can Support Investors, Companies, and Stasies have an impact on a company’s valuation. Early investors, stakeholders, and companies are already using the ESG data and ratings generated to inform their investments or strategic decisions in companies. On the other hand, it is apparent that measurements are often based on static indicators co

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,It’s Only a Bot! How Adversarial Chatbots can be a Vehicle to Teach Responsible AI,hics, trust, and responsibility. Understanding, implementing, and designing human interactions with these technologies is central to many advanced uses of intelligent and distributed systems and is related to contested concepts, such as various forms of agency, shared decision-making, and situationa

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AI and Leadership: Automation and the Change of Management Tasks and Processes,ed to shift towards automating processes and routines using AI. A step-by-step approach includes the identification of challenges, effectiveness and efficiency potentials in the company and the search for solutions, the introduction of AI solutions with a sustainable impact on the daily work of the

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,Artificial Intelligence: Companion to a New Human “Measure”?,ss the relationship between man and machine in the broader triangle of “man-machine-economy.” It is often the economic fields of application that influence the further development of technologies and their acceptance in society.

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ESG Fingerprint: How Big Data and Artificial Intelligence Can Support Investors, Companies, and Sta comes in and develops an ESG taxonomy based on historical ESG events from which risk patterns, the so-called ESG fingerprint, are automatically extracted. These help to reduce complexity and enable the design of artificial intelligence-based ESG information systems that map the risk management process across phases.

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Concerted Actions to Integrate Corporate Social Responsibility with AI in Business: Two Recommendatinesses and consequently the maximisation of positive externalities of AI in business. The two recommendations are contextualised with insights from a dialogue with four projects in Latin America using AI for the Sustainable Development Goals.