AI KR Strategists This plan defines the roles AI KR Strategists. Artificial Intelligence Knowledge Representation Community Group AIKR CG Organization_cd4a9bd6-0ec8-425c-ae47-9599f9b4b209 Carl Mattocks CoChair Work performed and works created for each AI value proposition is clearly and transparently documented and measured. Vision_861566c8-e9be-4642-b52f-f673fa499f4e To be responsible and accountable for the selection, development, application and management of Knowledge Representation (KR) for Artificial Intelligence (AI). Mission_861566c8-e9be-4642-b52f-f673fa499f4e Strategic Plan Document the vision, values, goals, objectives for one or more AIKR objects Goal_995c0b60-d64c-445e-86c8-a6f755f5ed9a 1 An AI KR Object may be : - an algorithm (example - enable an entity to determine consequences; a set of instructions that provide the ability to monitor and/or move the environment; the rules that are used to change/manipulate/interpret data) - an ontology (which has a set of ontological commitments) See Goal - Ontological Statements (provides sufficient definition to allow measurement to be performed) - an Intelligent Reasoning (fragmentary) Theory, such as, - deduction, - induction, - abduction, - by analogy, - probabilistic, - case-based  - a Reasoning Mechanism (computational environment), such as, - natural language processer, - rules engine, - machine learning - a Vocabulary (medium of human expression) Human-in-the-Loop Controllers Human-in-the-Loop Controllers are humans that Train/Test AI systems. They control the inputs of the humans when humans are in the loop. As a simple example, they tag the initial images that are fed into the algorithms; later they come back and refine the patterns identified; ultimately they may also come back and test the outcome. See: - https://en.wikipedia.org/wiki/Human-in-the-loop  - https://humansintheloop.org/model-training/ Model, test, evaluate and implement ethical approaches for Supervised Machine Learning of curated (labeled) data sets and the Active Learning training of algorithms via adjustment of parameters Ontology Employ ontology content that removes ambiguity, supports performance measurement and enables buy in Objective_a3d0d024-38a2-44e2-8c05-feaf13fdfb1d 1 The AI Strategist will work, with the AI KR Strategist and / or other experts, to ensure that ontology content mitigates bias by employing a complete glossary of all the data used and utilizing an accurate representation of the (data) relationship rules identified in processing instructions. That is, the ontology,  Representational Adequacy is able represent all the required knowledge;Inferential Adequacy is able to manipulate the knowledge represented to produce new knowledge (inferred from the original);Acquisitional Efficiency is able to acquire new knowledge using  Human In-The-Loop and  /or Human Out-of-The-Loop methods AI KR Strategists AI Strategists Algorithms Understand the various different types of algorithms and where they can support business strategy Objective_72826a99-eaf8-482d-b07f-a09afa7d1379 2 Algorithms' capabilities and limitations should be explained in a manner that is adapted to stakeholder concerns and identifies how accuracy, robustness, computational cost and stability will be measured Applications Understand the potential applications of AI to business strategies. Goal_2a903634-050d-43e0-9d2e-f0b1f3328635 2 Requirements Identify which areas of the requirements warrant AI solutions versus which can be achieved with other types of solutions Goal_a4a5f259-af0b-47f7-8e34-6c8eaefdae19 3 Glossaries Employ definitions from one or more glossaries when explaining AIKR object audit data, veracity facts and (human, social and technology) risk mitigation factors Goal_0083c58a-3d13-4e0e-95d1-8391c3f6414a 4 So that (business) people more readily understand the value that the glossaries bring. Risks Identify and mitigate risks and known threats Goal_bbeed24a-c843-427c-944e-08376a49ab9e 5 A guiding principle is that AIKR systems must mitigate risks. DARPA This goal arose in reference to the DARPA initiative. Consequences Identify and minimize adverse and/or unintended consequences Objective_fab00957-6d05-461b-a684-197efdecef6e 1 "Environment" includes the natural environment, as well as socio-economic and societal environments. - Minimise the risk of unintended consequences. - AI shall do no harm - When you're testing something, you should not alter the environmental conditions. - The social and societal impact should be carefully considered Data Ensure data quality and integrity Objective_545676f5-3cde-4aa5-9e22-1a4a0f108e85 2 Data quality: the data is fit for its intended purpose/use. Is supported by a systematic method for driving agreement on the definitions of categories. Data integrity: is the maintenance and assurance of the accuracy and consistency of data over its entire life-cycle. Is supported by a monitoring system that compares actual outcome with predicted accuracy Bias Identify and reduce bias in AI KR objects Objective_1ea3840a-0a5c-452e-afcf-1a486d38fbc5 3 Bias is disproportionate weight in favour of or against an idea or thing, usually in a way that is closed-minded, prejudicial or unfair. A bias is a systematic error. Security Guard against illegitimate access whilst ensuring legitimate access Objective_036d44e3-15a7-416e-b93e-a6a9b7941229 4 Security means protection as well as the measures taken to be safe or protected. Control Design the criteria to control the use (and misuse) of algorithms and data Objective_0548440c-d869-4347-89de-6b8157947b6f 5 Control: - control of the algorithms: To stop them from learning beyond our ability to control them. - control of the people who develop (strategists and developers) and use algorithms: they can be used for good or for evil. AI KR Strategists AI Developers Users of AI systems Intellectual Property Manage Intellectual Property rights over AI KR works Objective_703a1123-98b1-401c-a691-1cc441b4953b 6 What works can be protected, and what form of protection can be used for them? Privacy Protect the rights of the individuals/corporations whose data is processed Objective_46348127-5596-4b21-8d17-5bf451701316 7 Ensuring data is processed with the permission of the people to whom it pertains. E.g. GDPR, intellectual property, etc. Governance Design governance in line with the risk tolerance Objective_5336be3a-69dd-40b8-b18f-fef6cb1e87a0 8 Data and algorithm governance. Compliance Ensure AI Systems comply with all applicable laws and regulations, such as, provision audit data defined by a governance operating model Goal_b71896a0-3d86-4713-a720-15738315e36b 6 Compliance policies and procedures ensure that a planned change to a KR Object usage will comply with applicable laws/regulations during the identification, development, documentation, testing, validation, implementation, modification, use and retirement lifecycle Ethics Ensure AI Systems adhere to principles of ethics Goal_bbcb3dc4-5946-4d7d-b43f-0a55af305cc2 7 Autonomy Find the balance between human control/oversight and machine autonomy Objective_28520dbc-b02c-4e4f-a93e-e91ffaff0659 1 Oversight controls will enable the assessment of algorithms, data and design processes Veracity Veracity Objective_7a9d8c77-e826-4a55-8641-e7812145de41 2 Accountability Accountability Objective_fc02c4bf-a0cc-42b3-9452-05d06965e47f 3 Confidentiality Confidentiality Objective_4fc0efcb-adaa-4ead-926b-4ab5512b62a5 4 Robustness 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 Goal_5a34fa22-8d74-402f-b111-d0e585de11a2 8 Outcomes Track AIKR object performance outcome via KPI (Key Performance Indicator) based on supervised learning models measurements Goal_e2b04ebe-49d3-43f3-a723-a44135690f64 9 Algorithm Evaluation Evaluate Algorithms Goal_56cd3982-542c-4719-965e-0bcce6606a01 10 Assess how well Algorithm results match actual outcomes to determine - how sensitive inferences made are to the parameters and - the proportion of observations made were accurately predicted. When needed the algorithmic impact assessments will also identify cause and effect of any biases. Artificial Intelligence Knowledge Representation Community Group (AIKR CG) Community of Interest Trustworthiness Advance use of AI safeguards Objective_fa222026-9d57-4423-9433-9933bfe755e0 1 Advance use of AI change management, knowledge representation performance evaluation, algorithmic impact assessment and context aware safeguards for a reliable, safe and transparent outcome Classification Track Classification Performance Indicators Objective_964efa5e-58a7-4d9a-a839-daa8aef2a857 2 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). KR Objects Evaluate KR Object Performance Goal_2fdd92f6-fce6-41f7-b914-993aac92123e 11 KR Object oversight mechanisms will define how performance measurements are used via human-in-the-loop, human-on-the-loop, and human-in-command approaches StrategyPlan_861566c8-e9be-4642-b52f-f673fa499f4e 2020-04-01 2025-12-24 https://www.stratnavapp.com/StratML/Part1/861566c8-e9be-4642-b52f-f673fa499f4e/Styled Submitter_861566c8-e9be-4642-b52f-f673fa499f4e Carl Mattocks CarlMattocks@WellnessIntelligence.Institute