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Resending: Feedback on explainability NIST (Email feedback)

From: Paola Di Maio <paola.dimaio@gmail.com>
Date: Thu, 15 Oct 2020 10:04:02 +0800
Message-ID: <CAMXe=SqOBqTpWusoEUF8mN--9GCD-C=7peqGX1gfiAbHBU-KcQ@mail.gmail.com>
To: explainable-AI@nist.gov, W3C AIKR CG <public-aikr@w3.org>, www-archive@w3.org
resending the feedback notes with linked MP3 rather than bulky audiofile
attachment which may not come through the email

> cc W3C AI KR CG
> Thank you for the opportunity to provide feedback on the draft
> principles for Xplainability
> I paste below and attach (as text, xls and mp3 narration
> <https://drive.google.com/file/d/1INcc8c7LKIlcI3j9WfyQGBSOup-WrOIJ/view?usp=sharing>in
> the link linked) some

comments, which I would be grateful if they could be
> taken into account and possibly addressed.
> Looking forward to progress towards a standard for explainability
> Keep us informed, thank you
> Best regards
> Paola Di Maio, PhD
> ---------------------------------------------------------------------------
> Draft NIST  IR 8312
> from PAOLA DI MAIO, Expert and Co-chair W3C  AI KR CG
> 13 October 2020
> a) before explainability can be addressed in the context of AI, AI
> should be better understood/defined. The reality is that we may not
> yet have AI after all
> b) In addition to the distinction between narrow and general AI, the
> distinction between closed vs open system AI is also necessary. This
> particularly applies to the point of Knowledge limits in the draft.
> 1. EXPLANATION type mismatch among the principles
> for example explanation, is a noun, while meaningful is an adjective,
> would be advisable to have some consistency in the naming conventions?
> 2. MEANINGFUL explanation is described as a principle that mandates an
> explanation for AI, and meaningful is described as a principle that
> the explanation is meaningful, but it does not describe
> criteria/parameters for meaningfulness. This does not seem up to
> standard. Looks to me that meaningful is a qualifier for  explanation
> (1)
> 3. EXPLANATION ACCURACY - same as above, this does not seem a
> principle more like a qualifier for principle 1. Looks to me that 2
> and 3 are qualifiers for 1. however they should be better defined
> 4. KNOWLEDGE LIMITS - this is new (ie. unheard of) Is there a
> reference for such a notion? Where does it come from? who may have
> come up with such an idea?
> Intelligence can be said to overcome knowledge limits, ie, given
> limited knowledge, an intelligent process relies on logical inferences
> deduction, abduction to achieve a conclusion. Reasoning with limited
> knowledge is a defining characteristic of intelligent systems.
> Furthermore in open systems, knowledge is not limited, by contrast, it
> is continually updated with new knowledge.  To consider limited
> knowledge for intelligent systems/AI is a contradiction in terms.  A
> knowledge limit applies to closed database systems not to AI.
> OTHER points
> =======
> - In addition to meaningful and accurate, explanations should also be
> timely, accessible, updatable etc
> - (symbolic) Knowledge Representation (KR) is a mechanism for
> explainability should be emphasized
> - this work possibly leads to a standard for explainability? would be
> needed, please keep me up to date
> Best regards
> -------------------------------------


Received on Thursday, 15 October 2020 02:04:52 UTC

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