Artificial Intelligence (AI) offers organizations unprecedented opportunities. However, one of the risks of using AI is that its outcomes and inner workings are not intelligible. In industries where trust is critical, such as healthcare and finance, explainable AI (XAI) is a necessity. However, the implementation of XAI is not straightforward, as it requires addressing both technical and social aspects. Previous studies on XAI primarily focused on either technical or social aspects and lacked a practical perspective. This study aims to empirically examine the XAI related aspects faced by developers, users, and managers of AI systems during the development process of the AI system. To this end, a multiple case study was conducted in two Dutch financial services companies using four use cases. Our findings reveal a wide range of aspects that must be considered during XAI implementation, which we grouped and integrated into a conceptual model. This model helps practitioners to make informed decisions when developing XAI. We argue that the diversity of aspects to consider necessitates an XAI “by design” approach, especially in high-risk use cases in industries where the stakes are high such as finance, public services, and healthcare. As such, the conceptual model offers a taxonomy for method engineering of XAI related methods, techniques, and tools.
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Abstract The emergence of new technologies such as mp3 and music streaming, and the accompanying digital transformation of the music industry, have led to the shift and change of the entire music industry’s value chain. While music is increasingly being consumed through digital channels, the number of empirical studies, particularly in the field of music copyright in the digital music industry, is limited. Every year, rightsholders of musical works, valued 2.5 billion dollars, remain unknown. The objectives of this study are twofold: First to understand and describe the structure and process of the Dutch music copyright system including the most relevant actors within the system and their relations. Second to apply evolutionary economics approach and Values Sensitive Design method within the context of music copyright through positive-empirical perspective. For studies of technological change in existing markets, the evolutionary economics literature provides a coherent and evidence-based foundation. The actors are generally perceived as being different, for example with regard to their access to information, their ability to handle information, their capital and knowledge base (asymmetric information). Also their norms, values and roles can differ. Based on an analysis of documents and held expert interviews, we find that the collection and distribution of the music copyright money is still based on obsolete laws, neoclassical paradigm and legacy IT-system. Finally, we conclude that the rightsholders are heterogenous and have asymmetrical information and negotiating power. The outcomes of this study contribute to create a better understanding of impact of digitization of music copyright industry and empower the stakeholders to proceed from a more informed perspective on redesigning and applying the future music copyright system and pre-digital norms and values amongst actors.
Recently, the job market for Artificial Intelligence (AI) engineers has exploded. Since the role of AI engineer is relatively new, limited research has been done on the requirements as set by the industry. Moreover, the definition of an AI engineer is less established than for a data scientist or a software engineer. In this study we explore, based on job ads, the requirements from the job market for the position of AI engineer in The Netherlands. We retrieved job ad data between April 2018 and April 2021 from a large job ad database, Jobfeed from TextKernel. The job ads were selected with a process similar to the selection of primary studies in a literature review. We characterize the 367 resulting job ads based on meta-data such as publication date, industry/sector, educational background and job titles. To answer our research questions we have further coded 125 job ads manually. The job tasks of AI engineers are concentrated in five categories: business understanding, data engineering, modeling, software development and operations engineering. Companies ask for AI engineers with different profiles: 1) data science engineer with focus on modeling, 2) AI software engineer with focus on software development , 3) generalist AI engineer with focus on both models and software. Furthermore, we present the tools and technologies mentioned in the selected job ads, and the soft skills. Our research helps to understand the expectations companies have for professionals building AI-enabled systems. Understanding these expectations is crucial both for prospective AI engineers and educational institutions in charge of training those prospective engineers. Our research also helps to better define the profession of AI engineering. We do this by proposing an extended AI engineering life-cycle that includes a business understanding phase.
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