Data-Driven Innovation for Trustworthy AI

The rapid integration of AI in products, services, and innovation processes has enabled transformative applications, raising global concerns about the trustworthiness of AI features and the corresponding development processes. In this paper, we provide a perspective on how design and innovation processes can be adapted to ensure the trustworthiness of AI-centric artefacts. We review generic recommendations for trustworthy AI provided by various organisations and scholars. By leveraging the “double-hump” model of data-driven innovation, we explain and illustrate how trustworthy AI could be integrated into the design and innovation processes. We then propose research directions, data, and methods that could help gather an empirical understanding of trustworthiness and thus lead to an assessment of existing AI artefacts for trustworthiness. Since there is a disparity among domains and organisations in terms of AI-related risk and maturity, we expect that the proposed process model and the assessment methods could contribute towards a reliable road map for the development and assessment of trustworthy AI.