TAII Framework for Trustworthy AI Systems

Baker-Brunnbauer, J. (2021). TAII Framework for Trustworthy AI Systems. ROBONOMICS: The Journal of the Automated Economy, 2, 17

12 Pages Posted: 20 Sep 2021

Date Written: August 30, 2021

Abstract

Organizations and companies need practical tools and guidelines to kick-off the implementation of Trustworthy Artificial Intelligence (TAI) systems. AI development companies are still in the beginning of this process or have not even started yet. The findings of this article address to decrease the entry level barrier for AI ethics implementation by introducing the Trustworthy Artificial Intelligence Implementation (TAII) Framework. The outcome is comparatively unique given that it considers a meta perspective of implementing TAI within organizations. As such, this research aims to fill a literature gap for management guidance to tackle trustworthy AI implementation while considering ethical dependencies within the company. The TAII Framework takes a holistic approach to identify the systemic relationships of ethics for the company ecosystem and considers corporate values, business models, and common good aspects like the Sustainable Development Goals and the Universal Declaration of Human Rights. The TAII Framework creates guidance to initiate the implementation of AI ethics in organizations without requiring a deep background in philosophy and considers the social impacts outside of a software and data engineering setting. Depending on the legal regulation or area of application, the TAII Framework can be adapted and used with different regulations and ethical principles.

Keywords: Artificial Intelligence, Ethics, Trustworthiness, Business Model, Social Impact, Common Good

JEL Classification: M14, M15, O31, O32, O35

Suggested Citation

Baker-Brunnbauer, Josef, TAII Framework for Trustworthy AI Systems (August 30, 2021). Baker-Brunnbauer, J. (2021). TAII Framework for Trustworthy AI Systems. ROBONOMICS: The Journal of the Automated Economy, 2, 17, Available at SSRN: https://ssrn.com/abstract=3914105

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