Re: [StratNavApp] Recent changes in your projects

Regarding the Role of an AI Strategist  I suggest we adopt as-is the GOALS
from
https://www.stratnavapp.com/StratML/Part1/d95875f4-04b2-46d3-a953-c441e16f428a/Styled
Goal: Ethics
Navigate through the potential ethical and legal issues of AI technology,
while driving forward the execution of smarter, more intelligent products
and services and processes
------------------------------
Goal: Strategy
develop and drive corporate strategy to lead assessments of new product
ideas, develop business cases and lead prototype creation to reach formal
business recommendation on new opportunities geared at enhancing service
and member experience
------------------------------
Goal: Problems
Articulate in solving a business problem how much will be done by an AI
system and how much by the human system


AND add them to Strategy (previously discussed)
https://www.stratnavapp.com/StratML/Part2/861566c8-e9be-4642-b52f-f673fa499f4e/Styled
<https://chfbdgg.r.af.d.sendibt2.com/tr/cl/rEsV5jT4sswXrPTTNfeC6nDjtm-nY1ZIrbl-LZSSKUkUYZgjnbTv8Udz3_YAbqwqUVZN_Mh-3G1zCay-QQbmAsIiYP6DYRPQ-gHFsm5Onb8Xu6c7fTMAyYa1jfbH_uQG3qOf0p7m2kbD7BomBZ8yw2ziX7qxU7mHkQZpdgBzER8fLV30znD2wfdD9K_Y1ANHX7NkMFXFwAQKoHCD7YHDD6uoVfsVF69ud0Ng7AUmt8FAnsV3Q5sx6TZ74Ta_vL9klYIGl8iJD0V01kG3Pf-6AQ>

VisionFor all AI systems to have clearly and transparently documented goals
and performance data showing that they are being achieved.MissionThe
mission of an AI Strategist is to define the purpose and goals of AI
systems, as well as the KPIs by which we can determine if the system is
meeting its goals.
------------------------------
Goal: Ethical
Ensure AI Systems adhere to pivotal principles, such as, confidentiality,
autonomy, accountability and veracity
------------------------------
Goal: Machine Learning Evaluation
Evaluate machine learning modelsStakeholders:

Artificial Intelligence Knowledge Representation Community Group (AIKR CG)

Role: Community of Interest

Objectives:
Objective: Trustworthy
Provide the foundation for a trustworthy AIKR

Other Information: Evaluation metrics are tied to machine learning tasks.
Perhaps the easiest metric to interpret is the percent of estimates that
differ from the true value by no more than X%.
Objective: Track
Track Classification Performance Indicators

Other Information: Ontological Statement: Classification Accuracy is the
ratio of number of correct class label predictions to the total number of
input samples data. Ontological Statement: F1 Score measure the Harmonic
Mean between precision and recall. The range for F1 Score is [0, 1]. It
tells you how precise your classifier is (how many instances it classifies
correctly), as well as how robust it is (it does not miss a significant
number of instances).
Performance Indicator: Precision Recall
Quantitative in Outcome

Other Information: Ontological Statement: Precision is the number of
correct positive results divided by the number of positive results
predicted by the classifier. Ontological Statement: Recall is the number of
correct positive results divided by the number of all relevant samples (all
samples that should have been identified as positive).
Performance Indicator: Accuracy
Quantitative in Outcome

Other Information: Ontological Statement: Classification Rate or Accuracy
is given by the relation: True Positives + True Negatives / All Instances
(True & False Positives + True & False Negatives)
Performance Indicator: Confusion Matrix
Quantitative in Outcome

Other Information: Ontological Statement: A confusion matrix is a summary
of prediction results on a classification problem. The number of correct
and incorrect predictions are summarized with count values and broken down
by each class (the types of errors being made) Types : * True Positives :
The cases in which we predicted YES and the actual output was also YES. *
True Negatives : The cases in which we predicted NO and the actual output
was NO. * False Positives : The cases in which we predicted YES and the
actual output was NO. * False Negatives : The cases in which we predicted
NO and the actual output was YES. Accuracy for the matrix can be calculated
by taking average of the values lying across the “main diagonal” Type
StartDate EndDate Description Target Number of True Positives Target Number
of False Positives Target Number of True Negatives Target Number of False
Negatives Actual [To be determined]
Performance Indicator: Per-class accuracy
Quantitative in Outcome
Performance Indicator: Log-Loss
Quantitative in Outcome

Other Information: Ontological Statement: Logarithmic loss (related to
cross-entropy) measures the performance of a classification model where the
prediction input is a probability value between 0 and 1 - Log loss
increases as the predicted probability diverges from the actual label
Logarithmic Loss or Log Loss, works by penalising the false
classifications. It works well for multi-class classification. When working
with Log Loss, the classifier must assign probability to each class for all
the samples. where, y_ij, indicates whether sample i belongs to class j or
not p_ij, indicates the probability of sample i belonging to class j Log
Loss has no upper bound and it exists on the range [0, ∞). Log Loss nearer
to 0 indicates higher accuracy, whereas if the Log Loss is away from 0 then
it indicates lower accuracy. In general, minimising Log Loss gives greater
accuracy for the classifier.
Performance Indicator: AUC-ROC Curve
Quantitative in Outcome

Other Information: Ontological Statement: check performance of multi -
class classification AUROC (Area Under the Receiver Operating
Characteristics) curve.Ontological Statement: Area Under Curve(AUC) is one
of the most widely used metrics for evaluation. It is used for binary
classification problem. AUC of a classifier is equal to the probability
that the classifier will rank a randomly chosen positive example higher
than a randomly chosen negative example. True Positive Rate (Sensitivity) :
True Positive Rate is defined as TP/ (FN+TP). True Positive Rate
corresponds to the proportion of positive data points that are correctly
considered as positive, with respect to all positive data points. False
Positive Rate (Specificity) : False Positive Rate is defined as FP /
(FP+TN). False Positive Rate corresponds to the proportion of negative data
points that are mistakenly considered as positive, with respect to all
negative data points.
Performance Indicator: F-measure
Quantitative in Outcome

Other Information: F1 Score is the Harmonic Mean between precision and
recall. Ontological Statement: F-measure represents both Precision and
Recall it helps to have a measurement that represents both of them.
F-measure is calculated using Harmonic Mean (in place of Arithmetic Mean).
Ontological Statement:  Mean Absolute Error is the average of the
difference between the Original Values and the Predicted Values. It gives
us the measure of how far the predictions were from the actual output.
Ontological Statement:  Mean Squared Error(MSE) takes the average of the
square of the difference between the original values and the predicted
values.
Performance Indicator: NDCG
Quantitative in Outcome

Other Information: Ontological Statement: Normalized discounted cumulative
gain (DCG) is a measure of ranking quality. In information retrieval, DCG
measures the usefulness, or gain, of a document based on its position in
the result list.
Performance Indicator: Regression Analysis
Quantitative in Outcome

Other Information: Root Mean Square Error (RMSE) Ontological Statement:
Root Mean Square Error (RMSE) is the standard deviation of the residuals
(prediction errors). Residuals are a measure of how far from the regression
line data points are; RMSE is a measure of how spread out these residuals
are.
Performance Indicator: Quantiles of Errors
Quantitative in Outcome

Other Information: Quantiles (or percentiles), which is the element of a
set that is larger than half of the set, and smaller than the other half.
Performance Indicator: "Almost correct" predictions
Quantitative in Outcome
------------------------------
Goal: Lawful
Ensure AI Systems comply with all applicable laws and regulations, such as,
provision audit data defined by a governance operating model
------------------------------
Goal: Ontological Statements
Employ ontological statements when explaining AIKR object audit data,
veracity facts and (human, social and technology) risk mitigation factors
------------------------------
Goal: Track
Track AIKR object performance outcome via KPI (Key Performance Indicator)
based on supervised learning models measurements
------------------------------
Goal: Document
Document the vision, values, goals, objectives for one or more AIKR objects
------------------------------
Goal: Robust
Ensure AI Systems are designed to handle uncertainty and tolerate
perturbation from a likely threat perspective, such as, design
considerations incorporate human, social and technology risk factors

Carl

It was a pleasure to clarify


On Thu, May 28, 2020 at 1:22 AM StratNavApp.com <mail@stratnavapp.com>
wrote:

> Here is an update on your projects on StratNavApp
> <https://chfbdgg.r.af.d.sendibt2.com/tr/cl/mTDzGfBLX2fVW9_RJaXU23JLd64bcuSrKcq7SlYzh-ZWy_yjKDgI6uv3y4ZNml3aSdmdP0PGZOiV3y2cFub_oVHoiUWiYl7oNeWX3biAklgL9hDL8i3InMw-aXlNtwMwHCIaeyP42T7DMJ_Q4JgtltyObIgTLFCkiTN3ygga0px55idzmntCffrSIxyCrQ>
> :
>
> To view, update, comment on or respond to any of these, please click on
> the *View* link next to it, and edit the item or add a note of your own -
> not by replying to this email.
>
> For stakeholder: Paul Alagna  view/contribute
> <https://chfbdgg.r.af.d.sendibt2.com/tr/cl/UTmoLahXS6sWrh9M10hPfRHA78eJu3YTXsXL3MWMMGHrJNgdYBnhTBQVw6beShnXpP80ExcH7HreHrxMSZY2xcIO5pHybo6E6KZNDcAJjUr5I-9aGL6LB8bOiZ8B0kW232rRgzzaJaaHm_732wZPWX2CdazGYHzcSFl-weEHfGG0Lkau_ChMp_1ZGYbpViFFDE7hViOZQjMbzLBOeCRfGSXkxOCw1nO4baTeWIcUPCgqQ9hMRN9Lb8V8OznxZHHEE6XMY5WcDf-4TEUERfJ5M4UyhJpdBbet0cVJQck23_QLOaJWXLTZIw4bQnI13IpJf1ay42mK>
>
> *In project: StratML for AIKR*
> On 27/05/2020 at 20:25, *Paul Alagna*
>
>    - added the phone number:
>    7323225641
>    - changed the email address:
>    pjalagna@gmailPJAlagna@Gmail.com
>    - added the mobile number:
>    7323225641
>
> For goal: AI Strategists  view/contribute
> <https://chfbdgg.r.af.d.sendibt2.com/tr/cl/tNE2YzBlu_5_eS3O-PMBZz-8oj7VfvELilZ3NutUSp_3V8K5hZ--ptAOa6-cFv4sgBcSfs_xqeK73OOz1NmnlcGHd8DKJc52NXWrfbAyCzSfJM3pQiIEcaX9d0GKs8rYXltWkS-H6nEPUZ5QiAqXU_E3aylNQUl5VoqN3ZFwWhznwU46wFsYDJ7LV1QX5qvCObC14AzCrC5odE0x0IuBO3IsNDuYEC_iY3aPjGauSIwBGUrH8kICyru2ZgCu13ElYxYfe1VEuus0UEh7f30vL5mNd_M3XDIIYOYH7M1ueQroCqLoSkXYAGHZW6X8RxQ>
>
> *In project: StratML for AIKR*
> On 27/05/2020 at 06:47, *Chris Fox* wrote:
>
> We now have two attempts to define the role of the AI Strategist in
> StratML format.
>
>    -
>    https://www.stratnavapp.com/StratML/Part1/d95875f4-04b2-46d3-a953-c441e16f428a/Styled
>    <https://chfbdgg.r.af.d.sendibt2.com/tr/cl/2TmjD4g955eAY6vZvU3g9IpqXPnqj96RjJx6NDp_sembIPLKi8RCpcFvGGFQlvYAV-jIw4Ew6VGm5bY5_iBtH_6T078jxgxW4SDGtRm6k4We4Ie631jbm-0xmbJmYCtjRIn92sKK0X_aRB8lHWb2-QuvGNge2RifceIEBlINT_w1_hMGHdWS7U4fuSTJJpN9-f8VLhF080WLG9KunTfK6ginqD-rrJreBGtUAISDXD8uj53jdhYBYB_obIz6sqccs8y8eCSrk5xa0pHGcGgB8w>
>    and
>    - this one
>    <https://chfbdgg.r.af.d.sendibt2.com/tr/cl/taFcSAQX8zFh1qLhKNr0swPkg0skuWqbAIB3zURLv5ze7eu6730cqT_jvC51DIs6jz4UTRYi8D6q4PSSvyBEpYF9YrjF93dIGVtE1hPUXZCf8depw-sL9wnjb9yCO_yL42yPyGFxRbs2NX_TGl6Zp6EDCFG87lCUei8UgFiS2e9EK4c4i7MyW7HRiMSGKrdatf2m2znyUQNBOZCsPTkHZ1gJLQqcGY3NloAcfduNGF_yExpki70vkRSQCNT5CcP3>
>    .
>
> I think if we are to say we have achieved this goal, we need to work
> towards a single one.
>
> For project: Roles of AI Strategists  view/contribute
> <https://chfbdgg.r.af.d.sendibt2.com/tr/cl/keuPPmZdSKiP4rrR2CRClMimi7kpXjg8byT1ax0w6K3wHfYiVovZR9T2ShMX5s6WBJaDd2vSSyQlxWxMqRZVgQwLxtzXXrqRPOulZBlDm0EbIRzK5IzebC8r1aCbyWA3kDmKaX4sOcSEClO9kTaRx4PV2XlRNuq-nnfBSKf-pp0OticPrpz53w4WQ0kUzLIYpxqOZNsC8RO6TZeGEIjASwX1-n5M99vISrX9W1sx91iQE0Qm2GgGlgRCAyIKZJRowhRQsCNG>
>
> *In project: Roles of AI Strategists*
> On 27/05/2020 at 06:38, *chris@chriscfox.com <chris@chriscfox.com>*
> wrote:
>
> We now have two StratML plans for the role of the AI Strategist. This one,
> and the one we previously discussed at
> https://www.stratnavapp.com/StratML/Part2/861566c8-e9be-4642-b52f-f673fa499f4e/Styled
> <https://chfbdgg.r.af.d.sendibt2.com/tr/cl/rEsV5jT4sswXrPTTNfeC6nDjtm-nY1ZIrbl-LZSSKUkUYZgjnbTv8Udz3_YAbqwqUVZN_Mh-3G1zCay-QQbmAsIiYP6DYRPQ-gHFsm5Onb8Xu6c7fTMAyYa1jfbH_uQG3qOf0p7m2kbD7BomBZ8yw2ziX7qxU7mHkQZpdgBzER8fLV30znD2wfdD9K_Y1ANHX7NkMFXFwAQKoHCD7YHDD6uoVfsVF69ud0Ng7AUmt8FAnsV3Q5sx6TZ74Ta_vL9klYIGl8iJD0V01kG3Pf-6AQ>
>
> I think that the object should be to merge the two so that we only have
> one.
>
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