We at the Frankfurt Big Data Lab at the Goeth University of Frankfurt are working on the definition of an assessment process for Ethical AI, that we call Z-inspection.
We decided to go for an open development and incremental improvement to establish our process and brand (“Z Inspected”).
We are assessing right now a real use case in healthcare (cardiology) https://cardis.io
We will look for additional real AI use cases soon.
The recording of our latest presentation is available here (30 min.):
Copy of the slides are available here:
More info on our research work on AI and Ethics is available here:
and this is our team: http://www.bigdata.uni-frankfurt.de/people/
The benefits of having such an AI Ethical assessment process in place are clearly explained in : "If governments deploy AI systems on human populations without framework for accountability, they risk losing touch with how decisions have been made, thus making it difficult for them to identify or respond to bias, errors, or other problems. The public will have less insight into how agencies function, and have less power to question or appeal decisions."
An Ethical assessment "would also benefit vendors (AI developers) that prioritize fairness, accountability, and transparency in their offering. Companies that are best equipped to help agencies and researchers study their system would have a competitive advantage over others. Cooperation would also help improve public trust, especially at a time when skepticism of the societal benefits of AI is on the rise.” 
The aim of our research work is to help contribute to closing the gap between “principles” (the “what” of AI ethics) and “practices” (the ”how”).
The project is non commercial.
Z-inspection is open access and distributed under the terms and conditions of the Creative Commons (Attribution-NonCommercial-ShareAlike CC BY-NC-SA) license (https://creativecommons.org/licenses/by-nc-sa/4.0/)
In our opinion, one cornerstone of being able to conduct a neutral, effective AI Ethical assessment is the absence of conflict of interests (direct and indirect).
1. Ensure no conflict of interests exist between the inspectors and the entity/organization to be examined;
2. Ensure no conflict of interests exist between the inspectors and vendors of tools and/toolkits/frameworks to be used in the inspection;
3. Assess potential bias of the team of inspectors.
This result in a:
→ GO if all three above are satisfied.
→ Still GO with restricted use of specific tools, if 2 is not satisfied.
→ NoGO if 1 or 3 are not satisfied.
 Algorithmic Impact Assessment: A Practical Framework for Public Agency Accountability, AI Now, April 2018
Roberto V. Zicari (*), Irmhild van Halem (*), Matthew Eric Bassett (*),
Karsten Tolle (*), Timo Eichhorn (*), Todor Ivanov (*), Jesmin Jahan Tithi (**),
Thomas Ploug (***), Georgios Kararigas (+),Romeo Kienzler (§). Marijana Tadic (++).
(*) Frankfurt Big Data Lab, Goethe University Frankfurt, Germany.
(**) Intel Labs, Santa Clara, CA, USA.
(***) Centre for Applied Ethics and Philosophy of Science, Aalborg University Copenhagen, Denmark.
(+) German Centre for Cardiovascular Research, Charité University Hospital, Berlin, Germany
(§) IBM Center for Open Source Data and AI Technologies, San Francisco, CA, USA
(++) Cardiology Department, Charite University Hospital, Berlin, Germany
Prof. Roberto V. Zicari
Frankfurt Big Data Lab
Goethe University Frankfurt