Abstract
There is growing consensus and appreciation for the importance of trust in the development of Artificial Intelligence (AI) technologies; however, there is a reliance on principles-based frameworks. Recent research has highlighted the principles/practice gap, where principles alone are not actionable, and may not be wholly effective in developing more trustworthy AI. We argue for complementary, evidence-based tools to close the principles/practice gap, and present ELATE (Evidence-Based List of Exploratory Questions for AI Trust Engineering) as one such resource. We discuss several tools or approaches for making ELATE actionable within the context of systems development.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
