Re: Law Enforcement Algorithms

Agreed. Enabling a direct comparison of 'Target Results' and 'Actual
Results' is simpler than parsing/interpreting loosely defined 'Assumptions'
and contrasting them with 'Results'..

Carl

On Fri, May 15, 2020, 6:21 PM Owen Ambur <Owen.Ambur@verizon.net> wrote:

> Carl, while the StratML standard doesn't explicitly address hypotheses or
> assumptions, in StratML Part 2 <PerformanceIndicator>s of the
> <TargetResult>s type are analogous to those concepts -- in the sense that
> the envisioned (hypothetical) results are achieved (proven) or not
> (disproved) or perhaps only partially achieved.  They can be documented as
> <ActualResult>s and compared to the <TargetResult>s.
>
> The degree to which objectives are achieved is analogous to the concept of
> degrees of confidence with respect to the probability that a hypothetical
> result could not have occurred by chance.  However, to the degree that
> inputs and processes are documented in an open, standard, machine-readable
> format like StratML, AI can be applied to learn and inform users what is
> required to achieve the results they desire and, eventually, to deliver it
> exactly when and where it is needed (with close to 100% confidence for
> routine purposes).  When that occur, the advertising, marketing, and
> acquisition/procurement paradigms will be transformed.
>
> With respect to assumptions, I like the saying about what they make of u
> and me.  https://www.urbandictionary.com/define.php?term=Assume  In
> short, I'd much prefer to document and share target and actual performance
> indicators than assumptions.
>
> At least, that's my hypothesis for now.  The proof will be in the
> puddin'.  https://grammarist.com/usage/proof-is-in-the-pudding/
>
> BTW, the contents of GAO's report are now available in StratML format at
> https://stratml.us/drybridge/index.htm#AUFLE
>
> I see that GAO plans to issue a second report with policy
> recommendations.  When they do, I plan to render them in StratML format as
> well ... but it certainly would be nice if GAO were do that themselves.
> They do have a database to track their recommendations, at
> https://www.gao.gov/reports-testimonies/recommendations-database/
> However, if it is interoperable with their reports, that would be news to
> me.
>
> This report has not been categorized under the Artificial Intelligence
> <https://www.gao.gov/reports-testimonies/recommendations-database/?q=%22Artificial+intelligence%22&field=thesaurus_ss&list=1&rec_type=priority#results>
> facet of their hypertext browse index at
> https://www.gao.gov/reports-testimonies/recommendations-database/?browse=thesaurus_ss&rec_type=priority#results
> but that's understandable because it does not contain recommendations.
>
> Owen
> On 5/14/2020 3:35 PM, carl mattocks wrote:
>
> Thanks for sharing the report..
>
> I noted it mentioned use of Hypothesis to explain how outcome could be
> interpreted . Would we want to note those statements as Assumptions used
> for a Goal?.
>
> Carl
>
>
>
> It was a pleasure to clarify
>
>
> On Wed, May 13, 2020 at 11:50 PM Owen Ambur <Owen.Ambur@verizon.net>
> wrote:
>
>> The U.S. GAO's new report on Algorithms Used in Federal Law Enforcement
>> is available at https://www.gao.gov/assets/710/706849.pdf
>>
>> This report is different than typical GAO reports, which usually contain
>> recommendations.  So I have not converted its relevant content to
>> StratML format as I have done with the recommendations set forth in 19
>> of their other reports at https://stratml.us/drybridge/index.htm#GAO
>>
>> However, if anyone thinks it might be useful to do so, I'd be happy to
>> give it a shot.  Goals, objectives, and stakeholders are implicit in the
>> text of the narrative report.  Rendering them in StratML format would
>> make them more explicit, in machine-readable format.
>>
>> Needless to say, as a self-avowed leading practice organization, it
>> would be good if GAO could start showing Executive Branch agencies how
>> to comply with section 10 of GPRAMA and the OPEN Government Data Act
>> (OGDA) by publishing its reports in machine-readable format conforming
>> to an internationally standardized schema.
>>
>> Owen
>>
>>
>>
>>
>>