Abstract
Mainstream public perception of AI varies greatly depending on an individual's perspective and experiences. On the one hand, from a user perspective, it can be perceived as a set of services that rely on data to enable new levels of innovations, insights, and organizational performance. On the other hand, from a more technical perspective, it can be perceived as a technology using mathematical frameworks, computing infrastructures along with associated software, and processing tools for analyzing and/or extracting patterns in large volumes of data. Yet, each of these perspectives differs from our definition in “The Need for Artificial Intelligence” section of Chapter 1.
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Notes
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Systems (such as software and/or hardware) that perform a range of operations to the data to provide the best course of action(s) to take to achieve a set objective while simultaneously maintaining certain human/business values and principles.
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Most organizations matching this profile generally rely on AI services offered by big technology companies such as Amazon, Google, and Microsoft.
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The mere identification of factors contributing to employee success or failure within an organization is still a big research question in the management literature (Rusu, Avasilcai, and Huţu 2016; Rafique et al. 2017).
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Assuming fairness is one of the organization’s corporate values and ethical requirements on the system.
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In our view, organizations should seek to comply with such regulations regardless of whether they are legally required to or not.
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Data provenance can be thought of as a record trail that accounts for the origin of a piece of data together with an explanation of how and why it got to the present place.
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Tsafack Chetsa, G.L. (2021). Intra-organizational Understanding of AI: Toward Transparency. In: Towards Sustainable Artificial Intelligence. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-7214-5_4
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DOI: https://doi.org/10.1007/978-1-4842-7214-5_4
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