Wednesday, September 2, 2020 at 22:22:08 Eastern Daylight Time 

Subject: Fw: Call for Comments on F our Principles of Explainable Ar9ficial Intelligence 

Date: Friday, August 21, 2020 a t 9:22:28 AM Eastern Daylight Time 

From: Ashley P. Moore 

To: NIST Explainable AI 

CC: James Reeves 

AFachments: IT Governance of AI AS DraP 3 APM 8-2020.pdf, GPI-Ar9ficial-Intelligence-Part-III.pdf, GPIAr9ficial-Intelligence.pdf, GPI-Ar9ficial-Intelligence-Part-II.pdf, A governance framework for 

algorithmic accountability and transparency.pdf, Algorithm Accountability Report 2020 

Working DraP 9 APM.pdf, aiareport2018.pdf 

Gree9ngs, 

Thanks for the opportunity to provide comment on the 4 proposed Principles. 

In our research to develop an Execu9ve Branch Agency CIO led AI Governance program that leads to 

the adop9on of various AI tools we found that there were 7 overarching principles used within the 

private sector and within the accredited audi9ng sector. Our findings are based for current proposed 

U.S. Legisla9on on Algorithm Accountability, AI Impact Assessments process, Audit domain (See 

a\ached), IEEE Guidance and Standards work--to include current standards development by ISO, New 

York AI Taskforce, and the Canadian Government Algorithm Impact Assessment work. 

Governance of ADS/AI Principles: 

Principle 1 — Organiza9onal Governance: Provide sufficient oversight into the internal/external 

impact assessments, governance, ethics, and accountability of the use of these technologies 

around the agency, within public/private sector agreements and established service level 

agreements 

Principle 2 — Human Rights: Ensure the use of these technologies do not infringe on local, 

na9onal, and interna9onally recognized human rights 

Principle 3 — Priori9zing “Human-in-the-Loop”: Ensure during the use of these technologies the 

command chain that authorizes the use is documented and approved to the extent where the 

commands flow down to a human to support specific ac9ons and/or task 

Principle 4 — Accountability: Ensure that within the procurement documenta9on and server 

level agreements (SLAs) that the designers and operators are responsible and accountable 

throughout the life cycle of the technology 

Principle 5 — Transparence: Privacy, and Data Management: Ensure these technologies operate 

in a transparent manner, meet federal privacy and data management requirements as these 

technologies and use evolves 

Principle 6 — ADS/AI Technology Misuse and Awareness: Minimize the Strategic, Opera9onal, 

and technical risks of their misuse 

Principle 7 — ADS/AI Cybersecurity Eco-system(s) Requirements: Ensure the organiza9on is 

protected and the architecture of such technologies are transparent to all leadership, include 

top-down bo\om-up empowerment, ownership, and responsibility, and the need to consider 

system deployment contexts 

Thanks again for the opportunity. 

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If you have any ques9ons, please feel free to contact me any9me. 

Mr. Ashley P. Moore, MBCI 

USAGM--Office of the Chief InformaVon Officer (CIO) 

IT Risk Management Division 

IT Business Analyst (IT/IS/ICT/Cloud/Shared/Partnerships) 

ADS/AI CapabiliVes in USAGM 

United States Agency for Global Media (USAGM) 

330 Independence Avenue, SW 

Washington, DC 20237 

Cell: 

"I am certain that aPer the dust of centuries has passed over our ci9es, we, too, will be remembered 

not for victories or defeats in ba\le or in poli9cs, but for our contribu9on to the human spirit." ~ John 

F. Kennedy (1962) 

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From: NIST AI Program <ai.nist@service.govdelivery.com> 

Sent: Monday, August 17, 2020 4:47 PM 

To: Ashley P. Moore

Subject: Call for Comments on Four Principles of Explainable Ar9ficial Intelligence 

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AI at NIST | News 

Call for Comments on Four Principles of

Explainable Artificial Intelligence 

NIST invites you to comment on the first draft of Four Principles of Explainable Artificial 

Intelligence (NISTIR 8312). The paper presents four principles that capture the fundamental 

properties of explainable Artificial Intelligence (AI) systems. These principles are heavily 

influenced by an AI system’s interaction with the human receiving the information. The 

requirements of the given application, the task, and the consumer of the explanation will 

influence the type of explanation deemed appropriate. Our four principles are intended to 

capture a broad set of motivations, applications, and perspectives. 

To characterize the diverse methods, applications, and perspectives of explainable AI, we 

are seeking comments on our white paper. Please submit comments by email 

to explainable-AI@nist.gov. We encourage you to submit comments using the form 

provided on this page. Anonymous comments are accepted. However, including your name 

and contact information enables the authors to contact you for clarification if necessary. All 

comments will be made public as-is, with no edits or redactions. Please be careful to 

not include confidential business or personal information, otherwise sensitive or protected 

information, or any information you do not wish to be posted. 

A call for patent claims is included in the front matter of this draft. For additional information, 

Any questions should be directed to explainable-AI@nist.gov. 

The comment period for this document is August 17, 2020 through October 15, 2020. 

see the Information Technology Laboratory (ITL) Patent Policy. 

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