Suchergebnisse

"Artificial Intelligence (AI) is being rapidly introduced into the workplace, creating debate around what AI means for our work and organizations. This book gives grounded counterweight to provocative newspaper headlines by using in-depth case studies of eight organizations' experiences of implementing and using AI, providing readers with a solid understanding of what is actually happening in practice. Critical yet constructive, the authors address the challenges of implementing AI: organizing for data, testing and validating, algorithmic brokering, and changing work. Using a combination of existing literature and thorough practical examples, they provide answers to questions such as: What data do I need? When is a system good enough to actually take over tasks? And how can my employees be prepared for working with AI? The book presents four recommendations for WISE management of AI, requiring work-related insights, interdisciplinary knowledge, sociotechnical change processes, and ethical awareness. Offering insight into the unique characteristics of AI in organizations, this book will be essential reading for scholars of business and management, data analytics and information systems, technology and innovation, and computer science. With practical recommendations for managing the challenges of AI, it will also provide business managers with reflections to improve their own AI development and implementation processes"--

This paper presents research on how knowledge brokers attempt to translate opaque algorithmic predictions. The research is based on a 31-month ethnographic study of the implementation of a learning algorithm by the Dutch police to predict the occurrence of crime incidents and offers one of the first empirical accounts of algorithmic brokers. We studied a group of intelligence officers, who were tasked with brokering between a machine learning community and a user community by translating the outcomes of the learning algorithm to police management. We found that, as knowledge brokers, they performed different translation practices over time and enacted increasingly influential brokerage roles, namely, those of messenger, interpreter, and curator. Triggered by an impassable knowledge boundary yielded by the black-boxed machine learning, the brokers eventually acted like "kings in the land of the blind" and substituted the algorithmic predictions with their own judgments. By emphasizing the dynamic and influential nature of algorithmic brokerage work, we contribute to the literature on knowledge brokerage and translation in the age of learning algorithms.