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Data-Driven Innovation for Trustworthy AI
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Global concerns over the trustworthiness of rapidly proliferating AI-centric artefacts have led to generic institutional recommendations for trustworthy AI, which are yet to be operationalised and integrated with design and innovation processes. We leverage the double hump model of data-driven innovation to propose and illustrate diverse data-driven approaches for identifying and evaluating opportunities, and generating and evaluating concepts for trustworthy AI, addressing the recommendations of trustworthy AI as part of these processes. Building on the existing frameworks for classifying and managing risks associated with AI, we advocate for an ontological basis for trustworthy AI to enable fine-grained, computational assessments of AI-centric artefacts, their domains, and organisations.
Title: Data-Driven Innovation for Trustworthy AI
Description:
Global concerns over the trustworthiness of rapidly proliferating AI-centric artefacts have led to generic institutional recommendations for trustworthy AI, which are yet to be operationalised and integrated with design and innovation processes.
We leverage the double hump model of data-driven innovation to propose and illustrate diverse data-driven approaches for identifying and evaluating opportunities, and generating and evaluating concepts for trustworthy AI, addressing the recommendations of trustworthy AI as part of these processes.
Building on the existing frameworks for classifying and managing risks associated with AI, we advocate for an ontological basis for trustworthy AI to enable fine-grained, computational assessments of AI-centric artefacts, their domains, and organisations.
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